Varying coefficient models as Mixed Models : reparametrization methods and bayesian estimation

Non-linear relationships are accommodated in a regression model using smoothing functions. Interaction may occurs between continuous variable, in this case interaction between nonlinear and linear covariate leads to varying coefficent model (VCM), a subclass of generalized additive model. Additive models can be estimated as generalized linear mixed models, after being reparametrized. In this article we show three different type of matrix design for mixed model for VCM, by applying b-spline smoothing functions. An application on real data is provided and model estimates re computed with a Bayesian approach

A note on intrinsic Conditional Autoregressive models for disconnected graphs

In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples on disease mapping.Comment: 14 page

Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England

Bioaerosols have been associated with adverse respiratory-related health effects and are emitted in elevated concentrations from composting facilities. We used modelled Aspergillus fumigatus concentrations, a good indicator for bioaerosol emissions, to assess associations with respiratory-related hospital admissions. Mean daily Aspergillus fumigatus concentrations were estimated for each composting site for first full year of permit issue from 2005 onwards to 2014 for Census Output Areas (COAs) within 4 km of 76 composting facilities in England, as previously described (Williams et al., 2019). We fitted a hierarchical generalized mixed model to examine the risk of hospital admission with a primary diagnosis of (i) any respiratory condition, (ii) respiratory infections, (iii) asthma, (iv) COPD, (v) diseases due to organic dust, and (vi) Cystic Fibrosis, in relation to quartiles of Aspergillus fumigatus concentrations. Models included a random intercept for each COA to account for over-dispersion, nested within composting facility, on which a random intercept was fitted to account for clustering of the data, with adjustments for age, sex, ethnicity, deprivation, tobacco sales (smoking proxy) and traffic load (as a proxy for traffic-related air pollution). We included 249,748 respiratory-related and 3163 Cystic Fibrosis hospital admissions in 9606 COAs with a population-weighted centroid within 4 km of the 76 included composting facilities. After adjustment for confounders, no statistically significant effect was observed for any respiratory-related (Relative Risk (RR) = 0.99; 95% Confidence Interval (CI) 0.96–1.01) or for Cystic Fibrosis (RR = 1.01; 95% CI 0.56–1.83) hospital admissions for COAs in the highest quartile of exposure. Similar results were observed across all respiratory disease sub-groups. This study does not provide evidence for increased risks of respiratory-related hospitalisations for those living near composting facilities. However, given the limitations in the dispersion modelling, risks cannot be completely ruled out. Hospital admissions represent severe respiratory episodes, so further study would be needed to investigate whether bioaerosols emitted from composting facilities have impacts on less severe episodes or respiratory symptoms

Electric field and air ion exposures near high voltage overhead power lines and adult cancers: a case control study across England and Wales

Various mechanisms have been postulated to explain how electric fields emitted by high voltage overhead power lines, and the charged ions they produce, might be associated with possible adult cancer risk, but this has not previously been systematically explored in large scale epidemiological research.; We investigated risks of adult cancers in relation to modelled air ion density (per cm3) within 600 m (focusing analysis on mouth, lung, respiratory), and calculated electric field within 25 m (focusing analysis on non-melanoma skin), of high voltage overhead power lines in England and Wales, 1974-2008.; With adjustment for age, sex, deprivation and rurality, odds ratios (OR) in the highest fifth of net air ion density (0.504-1) compared with the lowest (0-0.1879) ranged from 0.94 [95% confidence interval (CI) 0.82-1.08] for mouth cancers to 1.03 (95% CI 0.97-1.09) for respiratory system cancers, with no trends in risk. The pattern of cancer risk was similar using corona ion estimates from an alternative model proposed by others. For keratinocyte carcinoma, adjusted OR in the highest (1.06-4.11 kV/m) compared with the lowest (<0.70 kV/m) thirds of electric field strength was 1.23 (95% CI 0.65-2.34), with no trend in risk.; Our results do not provide evidence to support hypotheses that air ion density or electric fields in the vicinity of power lines are associated with cancer risk in adults

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

Background. Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predict diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-art DNAm risk scores for cardiovascular diseases, the ‘next-generation’ epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. Results. Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent datasets from Europe and the US. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). Conclusions. We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures

Spatial predictors of landowners’ engagement in the restoration of the Brazilian Atlantic Forest

1. Forest restoration has the potential to contribute to climate, biodiversity, and human well-being, responding to multiple global sustainable development goals. Yet, little is known about the factors associated with local actors’ choice to engage in forest restoration, limiting the development of effective scaling strategies. 2. Our study examines the spatial socio-ecological factors associated with landowners’ engagement in forest restoration, documented by the Atlantic Forest Restoration Pact in Brazil. To meet this aim, we draw on Diffusion of Innovations theory to model associations between forest restoration and explanatory variables related to the relative advantage of engagement, characteristics of landowners, and contextual conditions among 222,000 private properties in the Atlantic Forest. 3. Private properties with greater cattle densities, larger geographical sizes, lower levels of 2010 forest cover, smaller 1990-2010 forest loss, and waterbodies were more likely to be restored. Furthermore, properties in upland areas, with greater topographical ruggedness, at higher travel times from major cities, and in areas with a higher prevalence of local on-farm forest management practices and lower population densities were less likely to be restored. 4. Landowners’ decision-making appears responsive to legislative requirements, underscoring their value as levers for promoting restoration. Commercial landowners might have greater incentives to engage in restoration or be selectively targeted by restoration organisations, risking the marginalisation of smallholders from the Atlantic Forest restoration agenda. Finally, engagement with forest restoration is highest where there are more people and lower travel times to cities, suggesting restoration can potentially deliver ecological benefits in some of Brazil’s most heavily degraded landscapes. Building on these insights, we discuss strategies to accelerate the scaling of forest regeneration to meet national and internationally important nature, climate, and human well-being goals

Fetal growth, stillbirth, infant mortality and other birth outcomes near UK municipal waste incinerators; retrospective population based cohort and case-control study

Some studies have reported associations between municipal waste incinerator (MWI) exposures and adverse birth outcomes but there are few studies of modern MWIs operating to current European Union (EU) Industrial Emissions Directive standards.; Associations between modelled ground-level particulate matter ≤10 μm in diameter (PM; 10; ) from MWI emissions (as a proxy for MWI emissions) within 10 km of each MWI, and selected birth and infant mortality outcomes were examined for all 22 MWIs operating in Great Britain 2003-10. We also investigated associations with proximity of residence to a MWI. Outcomes used were term birth weight, small for gestational age (SGA) at term, stillbirth, neonatal, post-neonatal and infant mortality, multiple births, sex ratio and preterm delivery sourced from national registration data from the Office for National Statistics. Analyses were adjusted for relevant confounders including year of birth, sex, season of birth, maternal age, deprivation, ethnicity and area characteristics and random effect terms were included in the models to allow for differences in baseline rates between areas and in incinerator feedstock.; Analyses included 1,025,064 births and 18,694 infant deaths. There was no excess risk in relation to any of the outcomes investigated during pregnancy or early life of either mean modelled MWI PM; 10; or proximity to an MWI.; We found no evidence that exposure to PM; 10; from, or living near to, an MWI operating to current EU standards was associated with harm for any of the outcomes investigated. Results should be generalisable to other MWIs operating to similar standards

Temporal trends and demographic risk factors for hospital admissions due to carbon monoxide poisoning in England.

Unintentional non-fire related (UNFR) carbon monoxide (CO) poisoning is a preventable cause of morbidity and mortality. Epidemiological data on UNFR CO poisoning can help monitor changes in the magnitude of this burden, particularly through comparisons of multiple countries, and to identify vulnerable sub-groups of the population which may be more at risk. Here, we collected data on age- and sex- specific number of hospital admissions with a primary diagnosis of UNFR CO poisoning in England (2002-2016), aggregated to small areas, alongside area-level characteristics (i.e. deprivation, rurality and ethnicity). We analysed temporal trends using piecewise log-linear models and compared them to analogous data obtained for Canada, France, Spain and the US. We estimated age-standardized rates per 100,000 inhabitants by area-level characteristics using the WHO standard population (2000-2025). We then fitted the Besag York Mollie (BYM) model, a Bayesian hierarchical spatial model, to assess the independent effect of each area-level characteristic on the standardized risk of hospitalization. Temporal trends showed significant decreases after 2010. Decreasing trends were also observed across all countries studied, yet France had a 5-fold higher risk. Based on 3399 UNFR CO poisoning hospitalizations, we found an increased risk in areas classified as rural (0.69, 95% CrI: 0.67; 0.80), highly deprived (1.77, 95% CrI: 1.66; 2.10) or with the largest proportion of Asian (1.15, 95% CrI: 1.03; 1.49) or Black population (1.35, 95% CrI: 1.20; 1.80). Our multivariate approach provides strong evidence for the identification of vulnerable populations which can inform prevention policies and targeted interventions