Effect of calcium channel blockers on influenza incidence: a population-based retrospective cohort study using administrative claims data in Japan

Objectives
Laboratory experiments have indicated that calcium channel blockers (CCBs) inhibit the entry and replication of influenza A virus in cells. However, no clinical studies have assessed the incidence of influenza among patients receiving CCBs. This study aimed to investigate the association between CCB use and the incidence of influenza among patients with hypertension using administrative claims data in Japan.

Design
Retrospective cohort study.

Setting
Administrative health insurance claims database of Kumamoto Prefecture, Japan.

Participants
360 515 patients with hypertension (10th edition of the International Classification of Diseases code I10) who were prescribed CCBs and 171 142 patients who were prescribed angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) between 2012 and 2016.

Primary outcome
We compared the incidence of influenza between the CCB and ACEI/ARB groups using high-dimensional propensity-score (HD-PS) matching.

Results
A total of 166 814 HD-PS matched pairs were obtained. Before HD-PS matching, the CCB group had a significantly lower influenza incidence than the ACEI/ARB group in the overall analysis (2.4% vs 2.5%, p=0.007; risk ratio 0.95, 95% CI 0.92 to 0.99). However, no significant difference was observed between the two groups after HD-PS matching (2.4% vs 2.5%, p=0.067; risk ratio 0.96, 95% CI 0.92 to 1.00); only in 2012 did the CCB group have a significantly lower likelihood of influenza than the ACEI/ARB group.

Conclusions
No significant difference was observed in the influenza incidence between the CCB and ACEI/ARB groups. A direct comparative study between background-matched patients with and without CCBs is warranted to confirm the effect of CCBs on reducing the incidence of influenza.

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Piloting a PREMs and PROMs longitudinal survey on the integration of healthcare services for patients living with hepatitis C in Tuscany region: study protocol

Introduction
Patient-reported measures are an invaluable resource for health systems to improve the quality of healthcare services. Patients with hepatitis C virus (HCV) are an under-represented group within the stream of literature on collecting and using the experiences and outcomes reported by patients to improve healthcare performance. This protocol outlines the methodology to implement a longitudinal survey in Tuscany, Italy, to systematically gather patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) for patients with HCV, with a focus on the integration of primary and hospital care.

Methods and analysis
We designed and developed a longitudinal survey to collect HCV PREMs and PROMs. The survey, which lasts 1 year, consists of three questionnaires, starting with the first visit with a specialist/treatment initiation, with follow-ups at 6 and 12 months. It was implemented in six hospitals in Tuscany, Italy, of which three are University Hospitals. The survey was offered to all patients treated for HCV at these healthcare centres, deliberately not applying a specific criterion for patient selection, through both paper based and electronic modes of completion. The data from the three structured questionnaires will be analysed quantitatively.

Ethics and dissemination
The Ethics Committee for Clinical Experimentation of Area Vasta Nord Ovest approved the protocol (CEAVNO—CODE 18829). Participation in this study is voluntary. Study results will be disseminated through peer-reviewed publications and academic conferences.

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Cohort profile: an observational population-based cohort study on COVID-19 vaccine effectiveness in the Netherlands – the VAccine Study COVID-19 (VASCO)

Purpose
VAccine Study COVID-19 (VASCO) is a cohort study with a 5-year follow-up that was initiated when COVID-19 vaccination was introduced in the Netherlands. The primary objective is to estimate real-world vaccine effectiveness (VE) of COVID-19 vaccines against SARS-CoV-2 infection in the Netherlands, overall and in four subpopulations defined by age and medical risk.

Participants
The cohort consists of 45 547 community-dwelling participants aged 18–85 years who were included irrespective of their COVID-19 vaccination status or intention to get vaccinated. A medical risk condition is present in 4289 (19.8%) of 21 679 individuals aged 18–59 years, and in 9135 (38.3%) of 23 821 individuals aged 60–85 years. After 1 year of follow-up, 5502 participants had dropped out of the study. At inclusion and several times after inclusion, participants are asked to take a self-collected fingerprick blood sample in which nucleoprotein and spike protein receptor binding domain-specific antibody concentrations are assessed. Participants are also asked to complete monthly digital questionnaires in the first year, and 3 monthly in years 2–5, including questions on sociodemographic factors, health status, COVID-19 vaccination, SARS-CoV-2-related symptoms and testing results, and behavioural responses to COVID-19 measures.

Findings to date
VASCO data have been used to describe VE against SARS-CoV-2 infection of primary vaccination, first and second booster and bivalent boosters, the impact of hybrid immunity on SARS-CoV-2 infection and VE against infectiousness. Furthermore, data were used to describe antibody response following vaccination and breakthrough infections and to investigate the relation between antibody response and reactogenicity.

Future plans
VASCO will be able to contribute to policy decision-making regarding future COVID-19 vaccination. Furthermore, VASCO provides an infrastructure to conduct further studies and to respond to changes in vaccination campaigns and testing policy, and new virus variants.

Trial registration number
NL9279.

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WHO Announces Mpox Global Plan, Appeals for Funding

As the mpox virus continues to spread in Africa, the World Health Organization (WHO) has launched a global emergency response plan to limit transmission. As of June 2024, the agency reported, the outbreak has resulted in nearly 100 000 confirmed cases and 200 deaths, mostly in the Democratic Republic of Congo, but also in such neighboring countries as Burundi and Kenya. Both Sweden and Thailand have reported a single travel-related case each.

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Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study

Objectives
With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop practical predictive models for mortality among patients infected with new COVID-19 variants.

Design
A retrospective study.

Setting and participants
We extracted data from 1029 COVID-19 patients in the respiratory disease wards of a general hospital in China between 22 December 2022 and 15 February 2023.

Outcome measures
Mortality within 15 days after hospital discharge.

Results
A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Non-invasive ventilation, intubation, myoglobin, international normalised ratio, age, number of diagnoses, respiratory rate, pulse, neutrophil count and albumin were the most important predictors of mortality among new COVID-19 variants. The area under the curve of logistic regression (LR), decision tree (DT) and Extreme Gradient Boosting (XGBoost) models were 0.959, 0.883 and 0.993, respectively. The diagnostic accuracy was 0.926 for LR, 0.918 for DT and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.908) and specificity (0.989).

Conclusion
Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.

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