Behavioural interventions for smoking cessation: an overview and network meta-analysis.

No abstract

BackgroundThere has been an increased interest from governments in implementing mass testing for COVID-19 of asymptomatic individuals using Lateral Flow Tests (LFTs). Successful implementation of such programmes depends on several factors, including feasibility, acceptability and how people act on test results. There is a paucity of studies examining these issues.ObjectiveWe aimed to examine experiences of university students and staff with experience of regular asymptomatic self-testing using LFTs, and their subsequent behaviours.MethodsWe invited people who were participating in a ‘weekly testing’ feasibility study. We conducted semi-structured remote interviews between December 2020 and January 2021. Additional qualitative data from a survey were also analysed. Data were analysed thematically.ResultsWe interviewed 18 and surveyed 214 participants. Participants were motivated to regularly self-test as they wanted to know whether or not they were infected with SARS-CoV-2. Most reported that a negative test result did not change their behaviour but it did provide them with reassurance to engage with permitted activities. In contrast, some participants reported making decisions about visiting other people when they would not have done so otherwise, because they felt reassured by a negative test result. Participants valued the test training but some participants still doubted their ability to carry out the test. Participants were concerned about safety of attending test sites with lots of people and reported home testing was most convenient.ConclusionsIf governments want to increase uptake of LFT use, clear messages highlighting the benefits of regular testing for family, friends and society in identifying asymptomatic cases are needed. This should be coupled with transparent communication about accuracy of LFTs and how to act on either a positive or negative result. Concerns about safety, convenience of testing, and ability to do tests need to be addressed to ensure successful scaling up asymptomatic testing.

SUMMARYStudies investigating associations between air pollution exposure and health outcomes benefit from the estimation of exposures at the individual level, but explicit consideration of the spatio-temporal variation in exposure is relatively new in air pollution epidemiology. We address the problem of estimating spatially and temporally varying particulate matter concentrations (black smoke = BS = PM 4 ) using data routinely collected from 20 monitoring stations in Newcastle-upon-Tyne between 1961 and 1992. We propose a two-stage strategy for modelling BS levels. In the first stage, we use a dynamic linear model to describe the long-term trend and seasonal variation in area-wide average BS levels. In the second stage, we account for the spatio-temporal variation between monitors around the area-wide average in a linear model that incorporates a range of spatio-temporal covariates available throughout the study area, and test for evidence of residual spatio-temporal correlation. We then use the model to assign time-aggregated predictions of BS exposure, with associated prediction variances, to each singleton pregnancy that occurred in the study area during this period, guided by dates of conception and birth and mothers' residential locations. In work to be reported separately, these exposure estimates will be used to investigate relationships between maternal exposure to BS during pregnancy and a range of birth outcomes. Our analysis demonstrates how suitable covariates can be used to explain residual spatio-temporal variation in individual-level exposure, thereby reducing the need to model the residual spatio-temporal correlation explicitly.

No abstract

Background There has been a rapid growth in the publication of new prediction models relevant to child and adolescent mental health. However, before their implementation into clinical services, it is necessary to appraise the quality of their methods and reporting. We conducted a systematic review of new prediction models in child and adolescent mental health, and examined their development and validation. Method We searched five databases for studies developing or validating multivariable prediction models for individuals aged 18 years old or younger from 1 January 2018 to 18 February 2021. Quality of reporting was assessed using the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis checklist, and quality of methodology using items based on expert guidance and the PROBAST tool. Results We identified 100 eligible studies: 41 developing a new prediction model, 48 validating an existing model and 11 that included both development and validation. Most publications (k = 75) reported a model discrimination measure, while 26 investigations reported calibration. Of 52 new prediction models, six (12%) were for suicidal outcomes, 18 (35%) for future diagnosis, five (10%) for child maltreatment. Other outcomes included violence, crime, and functional outcomes. Eleven new models (21%) were developed for use in high‐risk populations. Of development studies, around a third were sufficiently statistically powered (k = 16%, 31%), while this was lower for validation investigations (k = 12, 25%). In terms of performance, the discrimination (as measured by the C‐statistic) for new models ranged from 0.57 for a tool predicting ADHD diagnosis in an external validation sample to 0.99 for a machine learning model predicting foster care permanency. Conclusions Although some tools have recently been developed for child and adolescent mental health for prognosis and child maltreatment, none can be currently recommended for clinical practice due to a combination of methodological limitations and poor model performance. New work needs to use ensure sufficient sample sizes, representative samples, and testing of model calibration.

aStandard analyses of spatial data assume that measurement and prediction locations are measured precisely. In this paper we consider how appropriate methods of estimation and prediction change when this assumption is relaxed and the locations are subject to positional error. We describe basic models for positional error and assess their impact on spatial prediction. Using both simulated data and lead concentration pollution data from Galicia, Spain, we show how the predictive distributions of quantities of interest change after allowing for the positional error, and describe scenarios in which positional errors may affect the qualitative conclusions of an analysis. The subject of positional error is of particular relevance in assessing the exposure of an individual to an environmental pollutant when the position of the individual is tracked using imperfect measurement technology.

We present a comprehensive review of multivariate geostatistical models, focusing on the bivariate case. We compare in detail three approaches, the linear model of coregionalisation, the common component model and the kernel convolution approach, and discuss similarities between them. We demonstrate the merits of the common component class of models as a flexible means for modelling bivariate geostatistical data of the type that frequently arises in environmental applications. In particular, we show how kernel convolution can be used to approximate the common component model, and demonstrate the method using a data-set of calcium and magnesium concentrations in soil samples. We then apply the model to a study of domestic radon concentrations in the city of Winnipeg, Canada, in which exposure was measured at two sites (bedroom and basement) in each residential location. Our analysis demonstrates that in this study the correlation between the two sites within each house dominates the short-range spatial correlation typical of the distribution of radon.