Inference for variograms

The empirical variogram is a standard tool in the investigation and modelling of spatial covariance. However, its properties can be difficult to identify and exploit in the context of exploring the characteristics of individual datasets. This is particularly true when seeking to move beyond description towards inferential statements about the structure of the spatial covariance which may be present. A robust form of empirical variogram based on a fourth-root transformation is used. This takes advantage of the normal approximation which gives an excellent description of the variation exhibited on this scale. Calculations of mean, variance and covariance of the binned empirical variogram then allow useful computations such as confidence intervals to be added to the underlying estimator. The comparison of variograms for different datasets provides an illustration of this. The suitability of simplifying assumptions such as isotropy and stationarity can then also be investigated through the construction of appropriate test statistics and the distributional calculations required in the associated p-values can be performed through quadratic form methods. Examples of the use of these methods in assessing the form of spatial covariance present in datasets are shown, both through hypothesis tests and in graphical form. A simulation study explores the properties of the tests while pollution data on mosses in Galicia (North-West Spain) are used to provide a real data illustration

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