Inequal distribution of nursing personnel: a subnational analysis of the distribution of nurses across 58 countries.

BACKGROUND: Nursing personnel are critical for enabling access to health service in primary health care. However, the State of the World's Nursing 2020 report showed important inequalities in nurse availability between countries. METHODS: The purpose of this study/analysis was to describe the differences in nurse-to-population density in 58 countries from six regional areas and the relationship between differences in access to nurses and other indicators of health equity. RESULTS: All countries and income groups showed subnational inequalities in the distribution of nursing personnel with Gini coefficients ranging from 1 to 39. The latter indicated situation such as 13% of the population having access to 45% of nurses in a country. The average max-to-min ratio was on average of 11-fold. In our sample, the African region had the highest level of subnational inequalities with the average Gini coefficient of 19.6. The European Region had the lowest level of within-country inequalities with the average Gini coefficient being 5.6. A multivariate analysis showed a clustering of countries in three groups: (1) high Gini coefficients comprised mainly African countries; (2) moderate Gini coefficients comprised mainly South-East Asian, Central and South American countries; (3) low Gini coefficients comprised mainly Western countries, Japan, and Korea. The analysis also showed that inequality in distribution of nurses was correlated with other indices of health and inequality such as the Human Development Index, maternal mortality, and life expectancy. CONCLUSIONS: This study showed that there is a high level of geographic inequality in the distribution of nurses at subnational level. Inequalities in nursing distribution are multifactorial, to improve access to nurses, policies should be bundled, tailored to the local context and tackle the various root causes for inequalities.

Efforts needed for preventing breast and colorectal cancer through changes in dietary patterns.

BACKGROUND: Prospective cohort studies on diet and cancer report risk associations as hazard ratios. But hazard ratios do not inform on the number of people who need to alter their dietary behaviours for preventing cancer. The objective of this study is to estimate the number of people that need to alter their diet for preventing one additional case of female breast or colorectal cancer. METHODS: Based on the largest prospective studies done in the USA and in Europe, we computed the number of subjects who need to alter their diet. RESULTS: For preventing one case of breast cancer, European women should increase their fruit consumption by 100 g/day during 33 000 person-years, and US women by 60 g/day during 10 600 person-years. For vegetables, European women should increase their consumption by 160 g/day during 26 900 person-years and US women by 100 g/day during 19 000 person-years. For preventing one case of colorectal cancer, European subjects should decrease their red meat consumption by 20 g/day during 26 100 person-years, and US subjects by 30 g/day during 8170 person-years. For processed meat, European subjects should decrease their consumption by 20 g/day during 17 400 person-years, and US subjects by 10 g/day during 7940 person-years. CONCLUSIONS: Large number of subjects would need to alter their intake of fruits, vegetables, red and processed meat during many years in order to prevent one additional breast or colorectal cancer.

Latitudinal variation in climate-associated genes imperils range edge populations.

The ecological impacts of increasing global temperatures are evident in most ecosystems on Earth, but our understanding of how climatic variation influences natural selection and adaptive resilience across latitudes remains largely unknown. Latitudinal gradients allow testing general ecosystem-level theories relevant to climatic adaptation. We assessed differences in adaptive diversity of populations along a latitudinal region spanning highly variable temperate to subtropical climates. We generated and integrated information from environmental mapping, phenotypic variation and genome-wide data from across the geographical range of the rainbowfish Melanotaenia duboulayi, an emerging aquatic system for studies of climate change. We detected, after controlling for spatial population structure, strong interactions between genotypes and environment associated with variation in stream flow and temperature. Some of these hydroclimate-associated genes were found to interact within functional protein networks that contain genes of adaptive significance for projected future climates in rainbowfish. Hydroclimatic selection was also associated with variation in phenotypic traits, including traits known to affect fitness of rainbowfish exposed to different flow environments. Consistent with predictions from the "climatic variability hypothesis," populations exposed to extremes of important environmental variables showed stronger adaptive divergence and less variation in climate-associated genes compared to populations at the centre of the environmental gradient. Our findings suggest that populations that evolved at environmental range margins and at geographical range edges may be more vulnerable to changing climates, a finding with implications for predicting adaptive resilience and managing biodiversity under climate change.

Accurate continuous geographic assignment from low- to high-density SNP data.

MOTIVATION: Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies. RESULTS: We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41-197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology. AVAILABILITY AND IMPLEMENTATION: http://www2.imm.dtu.dk/∼gigu/Spasiba/ CONTACT: gilles.b.guillot@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A unifying model for the analysis of phenotypic, genetic, and geographic data.

Recognition of evolutionary units (species, populations) requires integrating several kinds of data, such as genetic or phenotypic markers or spatial information in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double original advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography and (ii) it allows one to analyze genetic and phenotypic data within a unified model and inference framework, thus opening the way to robust comparisons between markers and possibly combined analyses. We show from simulated data as well as real data that our method estimates parameters accurately and is an improvement over alternative approaches in many situations. The power of this method is exemplified using an intricate case of inter- and intraspecies differentiation based on an original data set of georeferenced genetic and morphometric markers obtained on Myodes voles from Sweden. A computer program is made available as an extension of the R package Geneland.

On the inference of spatial structure from population genetics data.

MOTIVATION: In a series of recent papers, Tess, a computer program based on the concept of hidden Markov random field, has been proposed to infer the number and locations of panmictic population units from the genotypes and spatial locations of these individuals. The method seems to be of broad appeal as it is conceptually much simpler than other competing methods and it has been reported by its authors to be fast and accurate. However, this methodology is not grounded in a formal statistical inference method and seems to rely to a large extent on arbitrary choices regarding the parameters used. The present article is an investigation of the accuracy of this method and an attempt to assess whether recent results reported on the basis of this method are genuine features of the genetic process or artefacts of the method. METHOD: I analyse simulated data consisting of populations at Hardy-Weinberg and linkage equilibrium and also data simulated under a scenario of isolation-by-distance at mutation-migration-drift equilibrium. Arabidopsis thaliana data previously analysed with this method are also reconsidered. RESULTS: Using the Tess program under the no-admixture model to analyse data consisting of several genuine HWLE populations with individuals of pure ancestries leads to highly inaccurate results; Using the Tess program under the admixture model to analyse data consisting of a continuous isolation-by-distance population leads to the inference of spurious HWLE populations whose number and features depend on the parameters used. Results previously reported about the A.thaliana using Tess seem to a large extent to be artefacts of the statistical methodology used. The findings go beyond population clustering models and can be an help to design more efficient algorithms based on graphs. AVAILABILITY: The data analysed in the present article are available from http://folk.uio.no/gillesg/Bioinformatics-HMRF.

Correcting for ascertainment bias in the inference of population structure.

BACKGROUND: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e.identifying clusters of individuals) brings up challenging numerical difficulties. METHOD: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). RESULT: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially. AVAILABILITY: Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/.

Dose-response analysis of toxicological and pharmacological mixtures with the model deviation ratio method: Problems and solutions.

Risk assessment for mixtures of chemicals requires to investigate the magnitude of their potential adverse effects on living organisms. This is usually done by assessing how experimental toxicological mixture data depart from the model of Loewe additivity. Several recent scientific studies propose to perform this task using an ad hoc method known as model deviation ratio (MDR) method. Moreover, the first official European regulatory document for the study of combined exposures explicitly recommends the use of the MDR method (EFSA Scientific Committee et al. Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals. EFSA Journal, 2019). We show here that the MDR method is not rooted in statistical principles and can lead to erroneous claims. We show however that the distribution of the MDR can be evaluated by simulations and show how this allows us to devise and carry out a bona fide statistical test. The proposed method accounts for uncertainty in the estimation of ED/EC50 and does not require a minimum sample size. The computer code developped in this study is made available as an R package called MDR.

Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited.

MOTIVATION: This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown. RESULTS: A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed. It is shown that they significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership. This is illustrated numerically with data simulated from the prior-likelihood model used in inference and also with data simulated from a Wright-Fisher model. Improvements are also illustrated on a real dataset of eighty-eight wolverines (Gulo gulo) genotyped at 10 microsatellites loci. The interest of the solutions presented here are not specific to any clustering model and are hence relevant to many settings in populations genetics where weakly differentiated populations are assumed or sought. AVAILABILITY: The improvements implemented will be made available in version 3.0.0 of the R package Geneland. Informations on how to get and use the software are available from http://folk.uio.no/gillesg/Geneland.html. SUPPLEMENTARY INFORMATION: http://folk.uio.no/gillesg/CFM/SuppMat.pdf.

Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface.

UNLABELLED: We introduce a new algorithm to account for the presence of null alleles in inferences of populations clusters from individual multilocus genetic data. We show by simulations that the presence of null alleles can affect the accuracy of inferences if not properly accounted for and that our algorithm improve signficantly their accuracy. AVAILABILITY: This new algorithm is implemented in the program Geneland. It is freely available under GNU public license as an R package on the Comprehensive R Archive Network. It now includes a fully clickable graphical interface. Informations on how to get the software are available on folk.uio.no/gillesg/Geneland.html

Statistical methods in spatial genetics.

The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.

Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment.

Bees are exposed to a wide range of multiple chemicals "chemical mixtures" from anthropogenic (e.g. plant protection products or veterinary products) or natural origin (e.g. mycotoxins, plant toxins). Quantifying the relative impact of multiple chemicals on bee health compared with other environmental stressors (e.g. varroa, viruses, and nutrition) has been identified as a priority to support the development of holistic risk assessment methods. Here, extensive literature searches and data collection of available laboratory studies on combined toxicity data for binary mixtures of pesticides and non-chemical stressors has been performed for honey bees (Apis mellifera), wild bees (Bombus spp.) and solitary bee species (Osmia spp.). From 957 screened publications, 14 publications provided 218 binary mixture toxicity data mostly for acute mortality (lethal dose: LD50) after contact exposure (61%), with fewer studies reporting chronic oral toxicity (20%) and acute oral LC50 values (19%). From the data collection, available dose response data for 92 binary mixtures were modelled using a Toxic Unit (TU) approach and the MIXTOX modelling tool to test assumptions of combined toxicity i.e. concentration addition (CA), and interactions (i.e. synergism, antagonism). The magnitude of interactions was quantified as the Model Deviation Ratio (MDR). The CA model applied to 17% of cases while synergism and antagonism were observed for 72% (MDR > 1.25) and 11% (MDR < 0.83) respectively. Most synergistic effects (55%) were observed as interactions between sterol-biosynthesis-inhibiting (SBI) fungicides and insecticide/acaricide. The mechanisms behind such synergistic effects of binary mixtures in bees are known to involve direct cytochrome P450 (CYP) inhibition, resulting in an increase in internal dose and toxicity of the binary mixture. Moreover, bees are known to have the lowest number of CYP copies and other detoxification enzymes in the insect kingdom. In the light of these findings, occurrence of these binary mixtures in relevant crops (frequency and concentrations) would need to be investigated. Addressing this exposure dimension remains critical to characterise the likelihood and plausibility of such interactions to occur under field realistic conditions. Finally, data gaps and further work for the development of risk assessment methods to assess multiple stressors in bees including chemicals and non-chemical stressors in bees are discussed.

Population substructure in Finland and Sweden revealed by the use of spatial coordinates and a small number of unlinked autosomal SNPs.

BACKGROUND: Despite several thousands of years of close contacts, there are genetic differences between the neighbouring countries of Finland and Sweden. Within Finland, signs of an east-west duality have been observed, whereas the population structure within Sweden has been suggested to be more subtle. With a fine-scale substructure like this, inferring the cluster membership of individuals requires a large number of markers. However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis. RESULTS: We genotyped 34 unlinked autosomal single nucleotide polymorphisms (SNPs), originally designed for zygosity testing, from 2044 samples from Sweden and 657 samples from Finland, and 30 short tandem repeats (STRs) from 465 Finnish samples. We saw significant population structure within Finland but not between the countries or within Sweden, and isolation by distance within Finland and between the countries. In Sweden, we found a deficit of heterozygotes that we could explain by simulation studies to be due to both a small non-random genotyping error and hidden substructure caused by immigration. Geneland, a model-based Bayesian clustering algorithm, clustered the individuals into groups that corresponded to Sweden and Eastern and Western Finland when spatial coordinates were used, whereas in the absence of spatial information, only one cluster was inferred. CONCLUSION: We show that the power to cluster individuals based on their genetic similarity is increased when including information about the spatial coordinates. We also demonstrate the importance of estimating the size and effect of genotyping error in population genetics in order to strengthen the validity of the results.

Inverse effects of the gas feed positioning on sonochemistry and sonoluminescence.

Purging of solutions to enhance sonochemical reactions is a common practice. A fundamental study combining sonoluminescence spectroscopy and sonochemical activity is adopted to study the effects of continuous Ar gas flow in the solution and of the position of the gas inlet tube on high-frequency sonolysis of aqueous solutions. It has been observed that neither sonochemical activity nor sonoluminescence intensity is controlled by the gas solubility only. Besides, the change in position of the gas inlet tube leads to opposite effects in sonoluminescence intensity and sonochemical activity: while the former increases, the latter decreases. Such an observation has never been reported despite sonochemical reactions have been carried out under different gas environments. Sonoluminescence spectroscopy indicates that more extreme conditions are reached at collapse with the gas inlet on the side, which could be explained by a more symmetrical collapse. Finally, it is shown in certain conditions that it is possible to favor the formation of some sonochemical products simply by positioning the gas inlet at different positions, which has practical significance in designing large scale sonochemical reactors for industrial applications.

Serological monitoring on milk and serum samples in a BVD eradication program: A field study in Belgium showing antibody ELISA performances and epidemiological aspects.

In a cross-sectional field study involving 51 cattle herds in Belgium, 3159 serum samples and 557 individual milk samples were collected and tested by four different commercial antibody (Ab) ELISAs on serum and two Ab ELISAs on milk. A virus neutralization test (VNT) was performed on serum samples with discording ELISA results and on all samples from non-vaccinating herds. An epidemiological survey was carried out in the same herds to collect information about herd characteristics, management practices, BVD vaccination and BVD infection status. The objective of the study was to evaluate the performances of the Ab ELISAs relatively to the VNT, to assess the possibility of using pooled samples and to give recommendations regarding serological monitoring of BVD-free herds in the context of the Belgian national BVD eradication program which started early 2015. Depending on the assays, for ELISAs on serum, the diagnostic sensitivity (DSe) was estimated to be between 93.0 and 98.7% and the diagnostic specificity (DSp) between 94.3% and 99.1%. For the two ELISAs on milk, the DSe were 91.3% and 96.7% and the DSp 94.0% and 100% respectively and the Cohen's agreement coefficients between serum and milk samples were 0.75 and 0.85. Positive serum and milk samples diluted in negative samples to mimic different pool sizes were not detected by all ELISAs at dilutions above 1:5 or 1:10, leading to the conclusion that the testing of pooled samples should be used cautiously for serological monitoring and only with ELISAs with high sensitivity. The epidemiological analysis and the seroprevalence study, based on a general estimating equation model, showed that several factors had a significant influence on overall animal seroprevalence and within-herd seroprevalence such as age class, herd size, BVD herd infection status, BVD vaccination of young and/or adult cattle and the number of stables in the farm. This study showed that the best performances obtained with commercial Ab ELISAs are observed on individual serum samples, which should therefore be the preferred matrix to monitor BVD-free herds in the context of the Belgian eradication program. By regularly testing a limited number of samples from young (6-18 months) unvaccinated cattle it is possible to confirm the BVD-free herd status or to detect a recent infection.

Bayesian clustering using hidden Markov random fields in spatial population genetics.

We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.

Discrimination and scoring using small sets of genes for two-sample microarray data.

Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control group. We also compare our methodology to other recently proposed methods on publicly available datasets. It is found that (i) use of several genes gives a much improved discrimination of the groups as compared to one gene only, (ii) the genes that play the most important role in the multivariate analysis are not necessarily those that rank first in univariate comparisons of the groups, (iii) Linear Discriminant Analysis carried out with sets of 2-5 genes selected according to their Hotelling T2 give results comparable to state-of-the-art methods using many more genes, a feature of our method which might be crucial in clinical applications. Finding groups of genes that together give optimal multivariate discrimination (given the size of the group) can identify crucial pathways and networks of genes responsible for a disease. The computer code that we developed to make computations is available as an R package.

Guidance on dermal absorption.

This guidance on the assessment of dermal absorption has been developed to assist notifiers, users of test facilities and Member State authorities on critical aspects related to the setting of dermal absorption values to be used in risk assessments of active substances in Plant Protection Products (PPPs). It is based on the 'scientific opinion on the science behind the revision of the guidance document on dermal absorption' issued in 2011 by the EFSA Panel on Plant Protection Products and their Residues (PPR). The guidance refers to the EFSA PPR opinion in many instances. In addition, the first version of this guidance, issued in 2012 by the EFSA PPR Panel, has been revised in 2017 on the basis of new available data on human in vitro dermal absorption for PPPs and wherever clarifications were needed. Basic details of experimental design, available in the respective test guidelines and accompanying guidance for the conduct of studies, have not been addressed but recommendations specific to performing and interpreting dermal absorption studies with PPPs are given. Issues discussed include a brief description of the skin and its properties affecting dermal absorption. To facilitate use of the guidance, flow charts are included. Guidance is also provided, for example, when there are no data on dermal absorption for the product under evaluation. Elements for a tiered approach are presented including use of default values, data on closely related products, in vitro studies with human skin (regarded to provide the best estimate), data from experimental animals (rats) in vitro and in vivo, and the so called 'triple pack' approach. Various elements of study design and reporting that reduce experimental variation and aid consistent interpretation are presented. A proposal for reporting data for assessment reports is also provided. The issue of nanoparticles in PPPs is not addressed. Data from volunteer studies have not been discussed since their use is not allowed in EU for risk assessment of PPPs.

A spatial statistical model for landscape genetics.

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST<0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.

Estimating the location and shape of hybrid zones.

We propose a new model to make use of georeferenced genetic data for inferring the location and shape of a hybrid zone. The model output includes the posterior distribution of a parameter that quantifies the width of the hybrid zone. The model proposed is implemented in the GUI and command-line versions of the Geneland program versions ≥ 3.3.0. Information about the program can be found on http://www2.imm.dtu.dk/gigu/Geneland/.