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Purpose: The current project sought to examine whether delivery of lung cancer risk projections (calculated using the Liverpool Lung Project [LLP] risk model) predicted follow-up smoking status. Design: Two single-blinded randomized controlled trials. Setting: Stop Smoking Services in Liverpool (United Kingdom). Participants: Baseline current smokers (N = 297) and baseline recent former smokers (N = 216) were recruited. Intervention: Participants allocated to intervention groups were provided with personalized lung cancer risk projections, calculated using the LLP risk model. Measures: Baseline and follow-up questionnaires explored sociodemographics, smoking behavior, and lung cancer risk perceptions. Analysis: Bivariate analyses identified significant differences between randomization groups, and logistic regression models were developed to investigate the intervention effect on the outcome variables. Results: Lung cancer risk projections were not found to predict follow-up smoking status in the trial of baseline current smokers; however, they did predict follow-up smoking status in the trial of baseline recent former smokers (odds ratio: 1.91; 95% confidence interval: 1.03-3.55). Conclusion: The current study suggests that lung cancer risk projections may help maintain abstinence among individuals who have quit smoking, but the results did not provide evidence to suggest that lung cancer risk projections motivate current smokers to quit.

This paper describes the application of two multimedia models, PRESTO and MMSOILS, to predict contaminant migration from a landfill that contains an organic chemical (methylene chloride) and a radionuclide (uranium‐238). Exposure point concentrations and human health risks are predicted, and distributions of those predictions are generated using Monte Carlo techniques. Analysis of exposure point concentrations shows that predictions of uranium‐238 in groundwater differ by more than one order of magnitude between models. These differences occur mainly because PRESTO simulates uranium‐238 transport through the groundwater using a one‐dimensional algorithm and vertically mixes the plume over an effective mixing depth, whereas MMSOILS uses a three‐dimensional algorithm and simulates a plume that resides near the surface of the aquifer.A sensitivity analysis, using stepwise multiple linear regression, is performed to evaluate which of the random variables are most important in producing the predicted distributions of exposure point concentrations and health risks. The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables. Simple regression techniques are applied, for comparison, to the same scenarios, and results are similar. The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.
ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.
MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.
Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.
ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.

In the past few years, the prediction of CVD risk has received special attention; however, some investigators assert that risk models have so far not been very successful. Thus, we examined whether the inclusion of dietary evaluation in a risk prediction model that already contained the classical CVD risk factors increases the accuracy and reduces the bias in estimating future CVD events. The database of the ATTICA study (which included information from 1,514 men and 1,528 women) was used. At baseline, the HellenicSCORE values (based on age, gender, smoking, systolic blood pressure, and total cholesterol) were calculated, while overall assessment of dietary habits was based on the Mediterranean diet score (MDS) that evaluates adherence to this traditional diet. In 2006, a five‐year follow‐up was performed in 2,101 participants and development of CVD (coronary heart disease, acute coronary syndromes, stroke, or other CVD) was defined according to WHO‐ICD‐10 criteria. The MDS and the HellenicSCORE were significant predictors of CVD events, even after adjusting for various potential confounders (p < 0.05). However, estimating bias (i.e., misclassification of cases) of the model that included HellenicSCORE and other potential confounders was 8.7%. The MDS was associated with the estimating bias of the outcome (p < 0.001) and explained 5.5% of this bias. Other baseline factors associated with bias were increased body mass index, low education status, and increased energy intake/BMR ratio. The inclusion of dietary evaluation, as well as other Sociodemographic and anthropometric characteristics, increases the accuracy and reduces estimating bias of CVD risk prediction models.

Objectives: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g. collected by loop detectors). The main objective of this paper is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways. Methods: In this study, the potential prediction variables are confined to traffic related characteristics. Given that the dependent variable (i.e. traffic safety condition) is dichotomous (i.e. "no-crash" or "crash"), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction towards Antwerp. Results: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed and standard deviation of speed at the upstream loop detector station, and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately while it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate. Conclusions: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems. ; This research was carried out within the framework of the Policy Research Centre on Traffic Safety with the support of the Flemish government and was partly supported by a grant from the Research Foundation Flanders (FWO). The content of this article is the sole responsibility of the authors.

Environmentally associated infectious diseases, including those driven by extreme weather events, represent a critical challenge for public health as their source and transmission are frequently sporadic and associated mechanisms often not well understood. Over the past decade, the Republic of Ireland (ROI) has persistently reported the highest incidence of confirmed verotoxigenic E. coli (VTEC) and cryptosporidiosis infection in the European Union. Moreover, recent climate projections indicate that the incidence, severity and timing of extreme rainfall events and flooding will increase dramatically over the next century, with Ireland forecast to be the second most affected European country with respect to the mean proportion of the population residing in flood-prone areas by 2100. This study aimed to assess the association(s) between potential flood risk exposure and the spatial occurrence of confirmed VTEC and cryptosporidiosis infection in Ireland over a 10-year period (2008-2017). In 2012, the Irish Office of Public Works (OPW) initiated the National Catchment Flood Risk Assessment and Management (CFRAM) Programme within the framework of the Flood Directive (2007/60/CE), with high-resolution flood maps produced for coastal and fluvial risks and three risk scenarios based on calculated return periods (low, medium and high probability). Small area identifiers (national census area centroids) were used to attach anonymised spatially referenced case data to CFRAM polygons using Geographical Information Systems (GIS) to produce an anonymised dataframe of confirmed infection events linked to geographically explicit flood risk attributes. Generalised linear modelling with binary link functions (infection presence/absence) were used to calculate probabilistic odds ratios (OR) between flood risk (presence/absence and scenarios) and confirmed human infection. Preliminary results indicate a clear relationship between both infections and hydrological risk. Over one third of all infection cases were reported within areas exposed to flood risk (VTEC 948/2755 cases; cryptosporidiosis 1548/4509 cases). Census areas categorised by a high (10-year Return Period) fluvial flood risk probability exhibited significantly higher incidence rates for both VTEC (OR: 1.83, P = 0.0003) and cryptosporidiosis (OR: 1.80, P = 0.0015). Similarly, areas characterised by low (1000-year Return Period) coastal flood risk probability were over twice as likely to report ≥1 confirmed case of cryptosporidiosis during the study period (OR: 2.2, P= 0.003). Space-time scan statistics (temporally-specific spatial autocorrelation) indicate an unseasonal peak of cryptosporidiosis cases occurring during April 2016, a majority of which took place within or adjacent to high flood risk areas (56% of total cases), revealing a potential relationship with the exceptional flooding events experienced during winter 2015-2016 (November-January). Further work will seek to identify the individual/combined flood risk (CFRAM) elements most significantly associated with the incidence of infections. Flood risk assessment mapping may represent an innovative approach to assessing the human health impacts of flood risk exposure and climate change. The outcomes of this study will contribute to predictive modelling of VTEC and cryptosporidiosis in Ireland, thus aiding surveillance and control of these diseases in the future, and the causative nature of regional hydrology and climate.

BELE Study Group leaded by Xavier Castells are listed here in alphabetical order and grouped by institution: (a) IMIM (Hospital Del Mar Medical Research Institute), Barcelona, Spain: Andrea Burón, Xavier Castells, Merce Comas, Jose Maria Corominas, Javier Louro, Ana Rodríguez-Arana, Marta Román, Maria Sala, Sonia Servitja, Mar Vernet-Tomas; (b) Corporació Sanitària Parc Taulí, Sabadell, Spain: Marisa Baré, Nuria Tora; (c) Catalan Institute of Oncology, Barcelona, Spain: Llucia Benito, Carmen Vidal (d) Hospital Santa Caterina, Girona, Spain: Joana Ferrer; (e) Catalan Institute of Oncology, Girona, Spain: Rafael Marcos-Gragera; (f) Hospital de la Santa Creu i Sant Pau, Barcelona, Spain: Judit Solà-Roca, María Jesús Quintana; (g) General Directorate of Public Health, Government of Cantabria, Spain: Mar Sánchez; (h) Principality of Astúrias Health Service, Spain: Miguel Prieto; (i) Fundació Lliga per a La Investigació i Prevenció Del Cáncer, Tarragona, Spain: Francina Saladié, Jaume Galceran; (j) Hospital Clinic, Barcelona, Spain; Xavier Bargalló, Isabel Torá-Rocamora; (k) Vallés Oriental Breast Cancer Early Detection Program, Spain; Lupe Peñalva; (l) Catalonian Cancer Strategy, Barcelona, Spain: Josep Alfons Espinàs. ; IRIS Study Group leaded by Marta Román are listed here in alphabetical order and grouped by institution: (a) IMIM (Hospital Del Mar Medical Research Institute), Barcelona, Spain: Rodrigo Alcantara, Xavier Castells, Laia Domingo, Javier Louro, Margarita Posso, Maria Sala, Ignasi Tusquets, Ivonne Vazquez, Mar Vernet-Tomas; (b) Corporació Sanitària Parc Taulí, Sabadell, Spain: Marisa Baré, Javier del Riego; (c) Catalan Institute of Oncology, Barcelona, Spain: Llucia Benito, Carmen Vidal (d) Hospital Santa Caterina, Girona, Spain: Joana Ferrer; (e) Catalan Institute of Oncology, Girona, Spain: Rafael Marcos-Gragera; (f) Hospital de la Santa Creu i Sant Pau, Barcelona, Spain: Judit Solà-Roca, María Jesús Quintana; (g) General Directorate of Public Health, Government of Cantabria, Spain: Mar Sánchez; (h) ...