Comparative analysis of the yields of dicentrics and chromosomal translocations.

PURPOSE: Chromosomal dicentrics and translocations are commonly employed as biomarkers to estimate radiation doses. The main goal of this article is to perform a comparative analysis of yields of both types of aberrations. The objective is to determine if there are relevant distinctions between both yields, allowing for a comprehensive assessment of their respective suitability and accuracy in the estimation of radiation doses. MATERIALS AND METHODS: The analysis involved data from a partial-radiation simulation study with the calibration data obtained through two scoring methods: conventional and PAINT modified. Subsequently, a Bayesian bivariate zero-inflated Poisson model was employed to compare the posterior marginal density of the mean of dicentrics and translocations and assess the differences between them. RESULTS: When employing the conventional method of scoring, the findings indicate that there is no notable disparity between the yield of observed translocations and dicentrics. However, when utilizing the PAINT modified method, a notable discrepancy is observed for higher doses, indicating a relevant difference in the mean number of the two types of aberrations. CONCLUSIONS: The choice of scoring method significantly influences the analysis of radiation-induced aberrations, especially when distinguishing between complex and simple chromosomal formations. Further research and analysis are necessary to gain a deeper understanding of the factors and mechanisms impacting the formation of dicentrics and translocations.

A framework for estimating and visualising excess mortality during the COVID-19 pandemic.

COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.

Determination of Ram (Ovis aries) Sperm DNA Damage Due to Oxidative Stress: 8-OHdG Immunodetection Assay vs. SCSA®.

Conventional DNA analysis techniques can hardly detect DNA damage in ruminant spermatozoa due to high DNA compaction in these cells. Furthermore, these techniques cannot discriminate whether the damage is due to oxidative stress. The main purpose of this study was to evaluate the efficacy of two techniques for determining DNA damage in ovine sperm when the source of that damage is oxidative stress. Semen samples from twenty Manchega rams (Ovis aries) were collected and cryopreserved. After thawing, the samples were subjected to different levels of oxidative stress, and DNA oxidation was quantified using an 8-hydroxy-2'-deoxyguanosine (8-OHdG) immunodetection assay and Sperm Chromatin Structure Assay (SCSA®). For this purpose, we evaluated five different concentrations of an oxidation solution (H2O2/FeSO4•7H2O) on ram sperm DNA. Our study with the 8-OHdG immunodetection assay shows that there are higher values for DNA oxidation in samples that were subjected to the highest oxidative stress (8 M H2O2/800 µM FeSO4•7H2O) and those that were not exposed to high oxidative stress, but these differences were not significant (p ≥ 0.05). The two SCSA® parameters considered, DNA fragmentation index (DFI %) and high DNA stainability (HDS %), showed significant differences between samples that were subjected to high concentrations of the oxidation agent and those that were not (p < 0.05). We can conclude that the 8-OHdG immunodetection assay and SCSA® detect DNA damage caused by oxidative stress in ovine sperm under high oxidative conditions; SCSA® is a more straightforward method with more accurate results. For these reasons, an oxidative-stress-specific assay such as 8-OHdG immunodetection is not needed to measure DNA damage caused by oxidative stress in ram sperm samples.

Radiation dose estimation with time-since-exposure uncertainty using the [Formula: see text]-H2AX biomarker.

To predict the health effects of accidental or therapeutic radiation exposure, one must estimate the radiation dose that person received. A well-known ionising radiation biomarker, phosphorylated [Formula: see text]-H2AX protein, is used to evaluate cell damage and is thus suitable for the dose estimation process. In this paper, we present new Bayesian methods that, in contrast to approaches where estimation is carried out at predetermined post-irradiation times, allow for uncertainty regarding the time since radiation exposure and, as a result, produce more precise results. We also use the Laplace approximation method, which drastically cuts down on the time needed to get results. Real data are used to illustrate the methods, and analyses indicate that the models might be a practical choice for the [Formula: see text]-H2AX biomarker dose estimation process.

Regional excess mortality during the 2020 COVID-19 pandemic in five European countries.

The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in the first stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.

Bayesian survival analysis with BUGS.

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.

Approximate Bayesian inference for multivariate point pattern analysis in disease mapping.

We present a novel approach for analysing multivariate case-control georeferenced data in a Bayesian disease mapping context using stochastic partial differential equations (SPDEs) and the integrated nested Laplace approximation (INLA) for model fitting. In particular, we propose smooth terms based on SPDE models to estimate the underlying spatial variation as well as risk associated to pollution sources. Log-Gaussian Cox processes are used to estimate the intensity of the cases and controls, to account for risk factors and include a term to measure spatial residual variation. Each intensity is modelled on a baseline spatial effect (estimated from both controls and cases), a disease-specific spatial term and the effects of some covariates. By fitting these models, the residual spatial terms can be easily compared to detect high-risk areas not explained by the covariates. Three different types of effects to model exposure to pollution sources are considered on the distance to the source: a fixed effect, a smooth term to model non-linear effects by means of a discrete random walk of order one and a Gaussian process in one dimension with a Matérn covariance function. Spatial terms are modelled using a Gaussian process in two dimensions with a Matérn covariance function and are approximated using an approach based on solving an SPDE through INLA. Finally, this new framework is applied to a dataset of three different types of cancer and a set of controls from Alcalá de Henares (Madrid, Spain). Covariates available include the distance to several polluting industries and socioeconomic indicators. Our findings point to a possible risk increase due to the proximity to some of these industries.

Geographic study of mortality due to mesothelioma in Peru and its evolution.

BACKGROUND: Peru has a public health problem because of asbestos imports. We analyzed the mortality trends for mesothelioma in Peru and its provinces from 2005 to 2014 and estimated their relationship with the amount of asbestos imported previously. METHODS: We computed age-standardized mortality rates (ASMRs) per 100,000 population (direct method and SEGI world standard population reference), and the standardized mortality ratio (SMR). The relationship between the amount of asbestos imported annually along the period 1965-2010 and the number of mesothelioma deaths per year from 2005 to 2014 was estimated by log-linear Poisson regression models and Pearson correlation calculations. RESULTS: After correcting the number of deaths, Peru registered 428 cases (or 430 when corrected cases are rounded by sex) between 2005 and 2014. The highest ASMRs were in Arequipa and Callao (range: 0.40-0.41/100,000 population), followed by Huancavelica (0.36/100,000 population). This translates into approximately one death per each 68-111 of asbestos tons imported. The latency period for the higher level of positive correlation found was 8 years (r = 0.8). Male female sex ratio was lower in provinces such as Junin and Hunacavelica with geological asbestos risk. CONCLUSIONS: Two patterns of mesothelioma risk have been detected, occupational and environmental. During the 2002-2006 years, Peru increased the asbestos use. If crocidolite imports were also increased, this could be behind the 8 years latency period detected. Peru should boost strategies towards the total ban of all forms of asbestos.

Individual and institutional factors associated with functional disability in nursing home residents: An observational study with multilevel analysis.

BACKGROUND: High prevalence of functional limitations has been previously observed in nursing homes. Disability may depend not only on the characteristics of the residents but also on the facility characteristics. The aims of this study were: 1, to describe the prevalence of functional disability in older people living in Spanish nursing homes; and 2, to analyze the relationships between individual and nursing home characteristics and residents' functional disability. METHODS: A cross-sectional study with data collected from 895 residents in 34 nursing homes in the province of Albacete (Spain) was conducted. Functional status was assessed by the Barthel Index. Taking into account both levels of data (individual and institutional characteristics) we resorted to a multilevel analysis in order to take different sources of variability in the data. RESULTS: The prevalence of functional disability of the total sample was 79.8%. The best fitting multilevel model showed that female gender, older age, negative self-perception of health, and living in private nursing homes were factors significantly associated with functional disability. After separating individual and institutional effects, the institutions showed significant differences. CONCLUSIONS: In line with previous findings, our study found high levels of functional dependence among institutionalized elders. Gender, age, self-perception of health, and institution ownership were associated with functional status. Disentangling individual and institutional effects by means of multilevel models can help evaluate the quality of the residences.

The mossy north: an inverse latitudinal diversity gradient in European bryophytes.

It remains hotly debated whether latitudinal diversity gradients are common across taxonomic groups and whether a single mechanism can explain such gradients. Investigating species richness (SR) patterns of European land plants, we determine whether SR increases with decreasing latitude, as predicted by theory, and whether the assembly mechanisms differ among taxonomic groups. SR increases towards the south in spermatophytes, but towards the north in ferns and bryophytes. SR patterns in spermatophytes are consistent with their patterns of beta diversity, with high levels of nestedness and turnover in the north and in the south, respectively, indicating species exclusion towards the north and increased opportunities for speciation in the south. Liverworts exhibit the highest levels of nestedness, suggesting that they represent the most sensitive group to the impact of past climate change. Nevertheless, although the extent of liverwort species turnover in the south is substantially and significantly lower than in spermatophytes, liverworts share with the latter a higher nestedness in the north and a higher turn-over in the south, in contrast to mosses and ferns. The extent to which the similarity in the patterns displayed by spermatophytes and liverworts reflects a similar assembly mechanism remains, however, to be demonstrated.

Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County.

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Statistical methods for the geographical analysis of rare diseases.

In this chapter we provide a summary of different methods for the detection of disease clusters. First of all, we give a summary of methods for computing estimates of the relative risk. These estimates provide smoothed values of the relative risks that can account for its spatial variation. Some methods for assessing spatial autocorrelation and general clustering are also discussed to test for significant spatial variation of the risk. In order to find the actual location of the clusters, scan methods are introduced. The spatial scan statistic is discussed as well as its extension by means of Generalised Linear Models that allows for the inclusion of covariates and cluster effects. In this context, zero-inflated models are introduced to account for the high number of zeros that appear when studying rare diseases. Finally, two applications of these methods are shown using data of Systemic Lupus Erythematosus in Spain and brain cancer in Navarre (Spain).

Spatial analysis of the relationship between mortality from cardiovascular and cerebrovascular disease and drinking water hardness.

Previously published scientific papers have reported a negative correlation between drinking water hardness and cardiovascular mortality. Some ecologic and case-control studies suggest the protective effect of calcium and magnesium concentration in drinking water. In this article we present an analysis of this protective relationship in 538 municipalities of Comunidad Valenciana (Spain) from 1991-1998. We used the Spanish version of the Rapid Inquiry Facility (RIF) developed under the European Environment and Health Information System (EUROHEIS) research project. The strategy of analysis used in our study conforms to the exploratory nature of the RIF that is used as a tool to obtain quick and flexible insight into epidemiologic surveillance problems. This article describes the use of the RIF to explore possible associations between disease indicators and environmental factors. We used exposure analysis to assess the effect of both protective factors--calcium and magnesium--on mortality from cerebrovascular (ICD-9 430-438) and ischemic heart (ICD-9 410-414) diseases. This study provides statistical evidence of the relationship between mortality from cardiovascular diseases and hardness of drinking water. This relationship is stronger in cerebrovascular disease than in ischemic heart disease, is more pronounced for women than for men, and is more apparent with magnesium than with calcium concentration levels. Nevertheless, the protective nature of these two factors is not clearly established. Our results suggest the possibility of protectiveness but cannot be claimed as conclusive. The weak effects of these covariates make it difficult to separate them from the influence of socioeconomic and environmental factors. We have also performed disease mapping of standardized mortality ratios to detect clusters of municipalities with high risk. Further standardization by levels of calcium and magnesium in drinking water shows changes in the maps when we remove the effect of these covariates.