Public health impact of low-dose aspirin on colorectal cancer, cardiovascular disease and safety in the UK – Results from micro-simulation model

Background: Low-dose aspirin therapy reduces the risk of cardiovascular disease and may have a positive effect on the prevention of colorectal cancer. We evaluated the population-level expected effect of regular low-dose aspirin use on cardiovascular disease (CVD), colorectal cancer (CRC), gastrointestinal bleeding, symptomatic peptic ulcers, and intracranial hemorrhage, using a microsimulation study design. Methods: We used individual-level state transition modeling to assess the impact of aspirin in populations aged 50–59 or 60–69 years old indicated for low-dose aspirin usage for primary or secondary CVD prevention. Model parameters were based on data from governmental agencies from the UK or recent publications. Results: In the 50–59 years cohort, a decrease in incidence rates (IRs per 100 000 person years) of non-fatal CVD (-203 and -794) and fatal CVD (-97 and-381) was reported in the primary and secondary CVD prevention setting, respectively. The IR reduction of CRC (-96 and -93) was similar for primary and secondary CVD prevention. The IR increase of non-fatal (116 and 119) and fatal safety events (6 and 6) was similar for primary and secondary CVD prevention. Similar results were obtained for the 60–69 years cohort. Conclusions: The decrease in fatal CVD and CRC events was larger than the increase in fatal safety events and this difference was more pronounced when low-dose aspirin was used for secondary compared to primary CVD prevention. These results provide a comprehensive image of the expected effect of regular low-dose aspirin therapy in a UK population indicated to use aspirin for CVD prevention. © 202

Vaccine effectiveness against laboratory-confirmed influenza in Europe – Results from the DRIVE network during season 2018/19

The DRIVE project aims to establish a sustainable network to estimate brand-specific influenza vaccine effectiveness (IVE) annually. DRIVE is a public–private partnership launched in response to EMA guidance that requires effectiveness evaluation from manufacturers for all individual influenza vaccine brands every season. IVE studies are conducted by public partners in DRIVE. Private partners (vaccine manufacturers from the European Federation of Pharmaceutical Industries and Association (EFPIA)) provide written feedback moderated by an independent scientific committee. Test-negative design (TND) case-control studies (4 in primary care and five in hospital) were conducted in six countries in Europe during the 2018/19 season. Site-specific confounder-adjusted vaccine effectiveness (VE) estimates for any vaccine exposure were calculated by age group (<18 years (y), 18-64y and 65 + y) and pooled by setting (primary care, hospital) through random effects meta-analysis. In addition, one population-based cohort study was conducted in Finland. TND studies included 3339 cases and 6012 controls; seven vaccine brands were reported. For ages 65 + y, pooled VE against any influenza strain was estimated at 27% (95%CI 6–44) in hospital setting. Sample size was insufficient for meaningful IVE estimates in other age groups, in the primary care setting, or by vaccine brand. The population-based cohort study included 274,077 vaccinated and 494,337 unvaccinated person-years, two vaccine brands were reported. Brand-specific IVE was estimated for Fluenz Tetra (36% [95%CI 24–45]) for ages 2-6y, Vaxigrip Tetra (54% [43–62]) for ages 6 months to 6y, and Vaxigrip Tetra (30% [25–35]) for ages 65 + y. The results presented are from the second influenza season covered by the DRIVE network. While sample size from the pooled TND studies was still too low for precise (brand-specific) IVE estimates, the network has approximately doubled in size compared to the pilot season. Taking measures to increase sample size is an important focus of DRIVE for the coming years

Drug Saf

Introduction Quantitative benefit-risk models (qBRm) applied to vaccines are increasingly used by public health authorities and pharmaceutical companies as an important tool to help decision makers with supporting benefit-risk assessment (BRA). However, many publications on vaccine qBRm provide insufficient details on the methodological approaches used. Incomplete and/or inadequate qBRm reporting may affect result interpretation and confidence in BRA, highlighting a need for the development of standard reporting guidance. Objectives Our objective was to provide an operational checklist for improved reporting of vaccine qBRm. Methods The consolidated standards of reporting quantitative Benefit-RIsk models applied to VACcines (BRIVAC) were designed as a checklist of key information to report in qBRm scientific publications regarding the assessed vaccines, the methodological considerations and the results and their interpretation. Results In total, 22 items and accompanying definitions, recommendations, explanations and examples were provided and divided into six main sections corresponding to the classic subdivisions of a scientific publication: title and abstract (items 1–2), introduction (items 3–4), methods (items 5–15), results (items 16–17), discussion (items 18–20) and other (items 21–22). Conclusions The BRIVAC checklist is the first initiative providing an operational checklist for improved reporting of qBRm applied to vaccines in scientific articles. It is intended to assist authors, peer-reviewers, editors and readers in their critical appraisal. Future initiatives are needed to provide methodological guidance to perform qBRm while taking into account the vaccine specificities

Estimation of the individual residual risk of cervical cancer after vaccination with the nonavalent HPV vaccine

Background: The nonavalent HPV (9vHPV) vaccine is indicated for active immunisation of individuals from the age of 9 years against cervical, vulvar, vaginal and anal premalignant lesions and cancers causally related to vaccine HPV high risk types 16, 18, 31, 33, 45, 52 and 58, and to the HPV low risk types 6 and 11, causing genital warts. Objective: To estimate the lifetime risk (up to the age of 75 years) for developing cervical cancer after vaccinating a HPV naive girl (e.g. 9 to 12 years old) with the 9vHPV vaccine in the hypothetical absence of cervical cancer screening. Methods: We built Monte Carlo simulation models using historical pre-screening age-specific cancer incidence data and current mortality data from Denmark, Finland, Norway, Sweden and the UK. Estimates of genotype contribution fractions and vaccine efficacy were used to estimate the residual lifetime risk after vaccination assuming lifelong protection. Results: We estimated that, in the hypothetical absence of cervical screening and assuming lifelong protection, 9vHPV vaccination reduced the lifetime cervical cancer and mortality risks 7-fold with a residual lifetime cancer risks ranging from 1/572 (UK) to 1/238 (Denmark) and mortality risks ranging from 1/1488 (UK) to 1/851 (Denmark). After decades of repetitive cervical screenings, the lifetime cervical cancer and mortality risks was reduced between 2- and 4-fold depending on the country. Conclusion: Our simulations demonstrate how evidence can be generated to support decision-making by individual healthcare seekers regarding cervical cancer prevention

Contribution of respiratory pathogens to influenza-like illness consultations

Influenza-like illnesses (ILIs) are caused by several respiratory pathogens. These pathogens show weak to strong seasonal activity implying seasonality in ILI consultations. In this paper, the contribution of pathogens to seasonality of ILI consultations was statistically modelled. Virological count data were first smoothed using modulation models for seasonal time series. Second, Poisson regression was used regressing ILI consultation counts on the smoothed time series. Using ratios of the estimated regression parameters, relative measures of the underreporting of pathogens were obtained. Influenza viruses A and B, parainfluenza virus and respiratory syncytial virus (RSV) significantly contributed to explain the seasonal variation in ILI consultations. We also found that RSV was the least and influenza virus A is the most underreported pathogen in Belgian laboratory surveillance. The proposed methods and results are helpful in interpreting the data of clinical and laboratory surveillance, which are the essential parts of influenza surveillance

Bias due to differential and non-differential disease- and exposure misclassification in studies of vaccine effectiveness

Background Studies of vaccine effectiveness (VE) rely on accurate identification of vaccination and cases of vaccine-preventable disease. In practice, diagnostic tests, clinical case definitions and vaccination records often present inaccuracies, leading to biased VE estimates. Previous studies investigated the impact of non-differential disease misclassification on VE estimation. Methods We explored, through simulation, the impact of non-differential and differential disease- and exposure misclassification when estimating VE using cohort, case-control, test-negative case-control and case-cohort designs. The impact of misclassification on the estimated VE is demonstrated for VE studies on childhood seasonal influenza and pertussis vaccination. We additionally developed a web-application graphically presenting bias for user-selected parameters. Results Depending on the scenario, the misclassification parameters had differing impacts. Decreased exposure specificity had greatest impact for influenza VE estimation when vaccination coverage was low. Decreased exposure sensitivity had greatest impact for pertussis VE estimation for which high vaccination coverage is typically achieved. The impact of the exposure misclassification parameters was found to be more noticeable than that of the disease misclassification parameters. When misclassification is limited, all study designs perform equally. In case of substantial (differential) disease misclassification, the test-negative design performs worse. Conclusions Misclassification can lead to significant bias in VE estimates and its impact strongly depends on the scenario. We developed a web-application for assessing the potential (joint) impact of possibly differential disease- and exposure misclassification that can be modified by users to their own study scenario. Our results and the simulation tool may be used to guide better design, conduct and interpretation of future VE studies