Distinguishing between a power law and a Pareto distribution.

This paper introduces the location Pareto distribution as a natural extension of the power law distribution and gives a likelihood ratio test for choosing between the two models. Some properties of the distribution and test are thoroughly investigated, and applications to real data are provided. For large values of the observations the two models perform similarly; this explains why some classical approaches are very insensitive to the differentiation between them. The likelihood ratio test between the two models is simple to use and has a high level of discrimination power. It is recommended when the complementary cumulative distribution function exhibits linearity on a log-log scale.

Evaluation of γ-H2AX foci distribution among different peripheral blood mononucleated cell subtypes.

INTRODUCTION: The detection of γ-H2AX foci in peripheral blood mononucleated cells (PBMCs) has been incorporated as an early assay for biological dosimetry. However, overdispersion in the γ-H2AX foci distribution is generally reported. In a previous study from our group, it was suggested that overdispersion could be caused by the fact that when evaluating PBMCs, different cell subtypes are analyzed, and that these could differ in their radiosensitivity. This would cause a mixture of different frequencies that would result in the overdispersion observed. OBJECTIVES: The objective of this study was to evaluate both the possible differences in the radiosensitivities of the different cell subtypes present in the PBMCs and to evaluate the distribution of γ-H2AX foci in each cell subtype. MATERIALS AND METHODS: Peripheral blood samples from three healthy donors were obtained and total PBMCs, and CD3+, CD4+, CD8+, CD19+, and CD56+ cells were separated. Cells were irradiated with 1 and 2 Gy and incubated at 37 °C for 1, 2, 4, and 24 h. Sham-irradiated cells were also analyzed. γ-H2AX foci were detected after immunofluorescence staining and analyzed automatically using a Metafer Scanning System. For each condition, 250 nuclei were considered. RESULTS: When the results from each donor were compared, no observable significant differences between donors were observed. When the different cell subtypes were compared, CD8+ cells showed the highest mean of γ-H2AX foci in all post-irradiation time points. The cell type that showed the lowest γ-H2AX foci frequency was CD56+. The frequencies observed in CD4+ and CD19+ cells fluctuated between CD8+ and CD56+ without any clear pattern. For all cell types evaluated, and at all post-irradiation times, overdispersion in γ-H2AX foci distribution was significant. Independent of the cell type evaluated the value of the variance was four times greater than that of the mean. CONCLUSION: Although different PBMC subsets studied showed different radiation sensitivity, these differences did not explain the overdispersion observed in the γ-H2AX foci distribution after exposure to IR.

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

BACKGROUND: The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process. METHODS: The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community. RESULTS: Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions. CONCLUSIONS: The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.

Biodose Tools: an R shiny application for biological dosimetry.

INTRODUCTION: In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry). MATERIALS AND METHODS: The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented. RESULTS: The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation. CONCLUSION: Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app's source code, it offers a platform to integrate new features.

Optimal Stein-type goodness-of-fit tests for count data.

Common count distributions, such as the Poisson (binomial) distribution for unbounded (bounded) counts considered here, can be characterized by appropriate Stein identities. These identities, in turn, might be utilized to define a corresponding goodness-of-fit (GoF) test, the test statistic of which involves the computation of weighted means for a user-selected weight function f. Here, the choice of f should be done with respect to the relevant alternative scenario, as it will have great impact on the GoF-test's performance. We derive the asymptotics of both the Poisson and binomial Stein-type GoF-statistic for general count distributions (we also briefly consider the negative-binomial case), such that the asymptotic power is easily computed for arbitrary alternatives. This allows for an efficient implementation of optimal Stein tests, that is, which are most powerful within a given class  F $\mathcal {F}$ of weight functions. The performance and application of the optimal Stein-type GoF-tests is investigated by simulations and several medical data examples.

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.

Establishment and validation of surface model for biodosimetry based on γ-H2AX foci detection.

INTRODUCTION: In the event of a radiation accident detecting γ-H2AX foci is being accepted as fast method for triage and dose assessment. However, due to their disappearance kinetics, published calibrations have been constructed at specific post-irradiation times. OBJECTIVES: To develop a surface, or tridimensional, model to estimate doses at times not included in the calibration analysis, and to validate it. MATERIALS AND METHODS: Calibration data was obtained irradiating peripheral mononucleated cells from one donor with radiation doses ranging from 0 to 3 Gy, and γ -H2AX foci were detected microscopically using a semi-automatic method, at different post-irradiation times from 0.5 to 24 h. For validation, in addition to the above-mentioned donor, blood samples from another donor were also used. Validation was done within the range of doses and post-irradiation times used in the calibration. RESULTS: The calibration data clearly shows that at each analyzed time, the γ-H2AX foci frequency increases as dose increases, and for each dose this frequency decreases with post-irradiation time. The γ-H2AX foci nucleus distribution was clearly overdispersed, for this reason to obtain bidimensional and tridimensional dose-effect relationships no probability distribution was assumed, and linear and non-linear least squares weighted regression was used. In the two validation exercises for most evaluated samples, the 95% confidence limits of the estimated dose were between ±0.5 Gy of the real dose. No major differences were observed between donors. CONCLUSION: In case of a suspected overexposure to radiation, the surface model here presented allows a correct dose estimation using γ-H2AX foci as biomarker. The advantage of this surface model is that it can be used at any post-irradiation time, in our model between 0.5 and 24 h.

Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health.

BACKGROUND: Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero). METHODS: This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural). RESULTS: The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. CONCLUSIONS: The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package ( https://cran.r-project.org/package=bayesZIB ).

New statistical model for misreported data with application to current public health challenges.

The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

Cumulated burden of COVID-19 in Spain from a Bayesian perspective.

BACKGROUND: The main goal of this work is to estimate the actual number of cases of COVID-19 in Spain in the period 31 January 2020 to 01 June 2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately re-estimate the lethality of the disease in Spain, taking into account unreported cases. METHODS: A hierarchical Bayesian model recently proposed in the literature has been adapted to model the actual number of COVID-19 cases in Spain. RESULTS: The results of this work show that the real load of COVID-19 in Spain in the period considered is well above the data registered by the public health system. Specifically, the model estimates show that, cumulatively until 1 June 2020, there were 2 425 930 cases of COVID-19 in Spain with characteristics similar to those reported (95% credibility interval: 2 148 261-2 813 864), from which were actually registered only 518 664. CONCLUSIONS: Considering the results obtained from the second wave of the Spanish seroprevalence study, which estimates 2 350 324 cases of COVID-19 produced in Spain, in the period of time considered, it can be seen that the estimates provided by the model are quite good. This work clearly shows the key importance of having good quality data to optimize decision-making in the critical context of dealing with a pandemic.

Quantifying the under-reporting of uncorrelated longitudal data: the genital warts example.

BACKGROUND: Genital warts are a common and highly contagious sexually transmitted disease. They have a large economic burden and affect several aspects of quality of life. Incidence data underestimate the real occurrence of genital warts because this infection is often under-reported, mostly due to their specific characteristics such as the asymptomatic course. METHODS: Genital warts cases for the analysis were obtained from the Catalan public health system database (SIDIAP) for the period 2009-2016. People under 15 and over 94 years old were excluded from the analysis as the incidence of genital warts in this population is negligible. This work introduces a time series model based on a mixture of two distributions, capable of detecting the presence of under-reporting in the data. In order to identify potential differences in the magnitude of the under-reporting issue depending on sex and age, these covariates were included in the model. RESULTS: This work shows that only about 80% in average of genital warts incidence in Catalunya in the period 2009-2016 was registered, although the frequency of under-reporting has been decreasing over the study period. It can also be seen that this issue has a deeper impact on women over 30 years old. CONCLUSIONS: Although this study shows that the quality of the registered data has improved over the considered period of time, the Catalan public health system is underestimating genital warts real burden in almost 10,000 cases, around 23% of the registered cases. The total annual cost is underestimated in about 10 million Euros respect the 54 million Euros annually devoted to genital warts in Catalunya, representing 0.4% of the total budget.

Online Cost-Effectiveness ANalysis (OCEAN): a user-friendly interface to conduct cost-effectiveness analyses for cervical cancer.

BACKGROUND: Most cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate HPV infection, cervical disease and prevention strategies. However, it is common for professionals who would need to perform these analyses to not be familiar with the models. This work introduces the Online Cost-Effectiveness ANalysis tool, featuring an easy-to-use web interface providing health professionals, researchers and decision makers involved in cervical cancer prevention programmes with a useful instrument to conduct complex cost-effectiveness analyses, which are becoming an essential tool as an approach for supporting decision-making that involves important trade-offs. RESULTS: The users can run cost-effectiveness evaluations of cervical cancer prevention strategies without deep knowledge of the underlying mathematical model or any programming language, obtaining the most relevant costs and health outcomes in a user-friendly format. The results provided by the tool are consistent with the existing literature. CONCLUSIONS: Having such a tool will be an asset to the cervical cancer prevention community, providing researchers with an easy-to-use instrument to conduct cost-effectiveness analyses.

Análisis de la Sobredemanda del Hospital General de Barrio Obrero. Implicancias organizacionales y funcionales de una microrred urbana. Asunción, Paraguay 2018-2019 / Analysis of the Excess Demand of the Barrio Obrero General Hospital. Organizational and functional implications of an urban micronetwork. Asunción, Paraguay 2018-2019

La ampliación del acceso a servicios sanitarios constituye el objetivo intrínseco del Sistema de Salud, cuyos resultados dependen de dinámicas y preferencias de los usuarios y del equilibrio de variables no controlables para alcanzar mejores resultados. Esta investigación evaluativa, cuali-cuantitativa, de corte transversal identifica algunas de estas variables, en un hospital general, cabecera de una micro red de servicios de salud urbana, en Asunción, Paraguay. La unidad de análisis incluyo actores clave ejerciendo roles directivos, gerenciales y asistenciales y usuarios fidelizados a la unidad efectora estudiada. La muestra incluyo 10 directivos, 20 profesionales de salud y 150 usuarios de consultorio externo, urgencias e internados, en tres turnos de atención. Se aplicaron tres cuestionarios conteniendo preguntas semiestructuradas sobre oferta, demanda, organización, supervisión, subsistema de información, comunicación interna, motivos de consulta, tiempos de espera, capacidad instalada, disponibilidad de insumos y medicamentos, motivos de preferencia y algunas variables sociales, epidemiológicas y culturales. Usuarios desconocen la organización de los servicios que integran la micro red urbana, prefieren el hospital general para acceder efectivamente a medios diagnósticos 82 %, especialistas, medicamentos y capacidad de respuesta integral 57%, 9,9 % posee seguro médico, el 76 % declara empleo formal, 40 % gana más del sueldo mínimo, 95 % de pacientes declaran buena atención en el Hospital General Barrio Obrero, 69 % está satisfecho. El fortalecimiento de los servicios de primer y segundo nivel puede equilibrar la sobredemanda del hospital cabecera, con implementación de innovaciones organizacionales, incremento de la inversión y adecuados planes de comunicación social

The expansion of the access to health services is the intrinsic objective of the health system, whose performance depends on user dynamics and preferences and the equilibrium of independent variables to achieve better results. The objective of this study was to evaluate dynamics and factors related to the excess demand in a general hospital, head of an urban micro network in Asunción, Paraguay. This was a quali-quantitative, cross-sectional evaluative study in which the unit of analysis were health actors in directive, managerial, and care providing roles as well as loyal users. The sample included 10 directors, 20 health professionals and 150 users in the areas of consultations, emergencies and hospitalization of three different shifts. Three questionnaires with semi-structured questions on offer, demand, organization, supervision, information subsystem, internal communication, reasons for consultation, waiting times, installed capacity, availability of supplies and medicines, hospital organization, supervision, reasons for preference and some social, epidemiological and cultural variables. The users did not know the organization of the services that were part of the urban micro network. They preferred the general hospital to effectively access to diagnostic means (82%), to specialists, medicines and capacity of integral response (57%), 9.9% did not have medical insurance, 76% said they had formal jobs, 40% earned more that the minimal wage, 95% said that the Barrio Obrero General Hospital had good attention and 69% of them were satisfied. The strengthening of first and second level services can balance the excess demand of the main hospital, with the implementation of organizational innovations, increased investment and adequate social communication plans

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

BACKGROUND: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. RESULTS: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. CONCLUSIONS: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

A nonparametric method of estimation of the population size in capture-recapture experiments.

A recent method for estimating a lower bound of the population size in capture-recapture samples is studied. Specifically, some asymptotic properties, such as strong consistency and asymptotic normality, are provided. The introduced estimator is based on the empirical probability generating function (pgf) of the observed data, and it is consistent for count distributions having a log-convex pgf ( LC -class). This is a large family that includes mixed and compound Poisson distributions, and their independent sums and finite mixtures as well. The finite-sample performance of the lower bound estimator is assessed via simulation showing a better behavior than some close competitors. Several examples of application are also analyzed and discussed.

Untangling serially dependent underreported count data for gender-based violence.

Underreporting in gender-based violence data is a worldwide problem leading to the underestimation of the magnitude of this social and public health concern. This problem deteriorates the data quality, providing poor and biased results that lead society to misunderstand the actual scope of this domestic violence issue. The present work proposes time series models for underreported counts based on a latent integer autoregressive of order 1 time series with Poisson distributed innovations and a latent underreporting binary state, that is, a first-order Markov chain. Relevant theoretical properties of the models are derived, and the moment-based and maximum-based methods are presented for parameter estimation. The new time series models are applied to the quarterly complaints of domestic violence against women recorded in some judicial districts of Galicia (Spain) between 2007 and 2017. The models allow quantifying the degree of underreporting. A comprehensive discussion is presented, studying how the frequency and intensity of underreporting in this public health concern are related to some interesting socioeconomic and health indicators of the provinces of Galicia (Spain).

Integración de la medicina natural y tradicional cubana en el sistema de salud / Integration of Cuban natural and traditional medicine in the health system

RESUMEN Introducción: Cuba actúa paradesarrollar un enfoque integrado e integral de la atención de salud, que facilite a los profesionales sanitarios y usuarios de los servicios de salud, el acceso a la medicina natural y tradicional de manera segura, asequible y efectiva. A pesar de ello, en la práctica de los servicios de perciben debilidades en la integración de la medicina natural y tradicional al resto del sistema de prestación de servicios. Objetivo: Identificar el nivel de integración de la medicina natural y tradicional a los servicios según las perspectivas de sus directivos. Metodología: Investigación en sistemas y servicios de salud de corte transversal, aplicando un instrumento para la captura y procesamiento de información valorativa de la integración según criterios evaluados por directivos de nivel provincial del país, que gestionan el programa de MNT. Resultados: La integración de los servicios de la medicina natural y tradicional en el país es parcial (IG = 0,64) según la percepción de los directivos. De 14 metas de integración exploradas 13 fueron percibidas con integración parcial y una meta se encuentra fragmentada. La región occidental mostró el índice más bajo (0,61) mientras que el centro y oriente tuvieron índices ligeramente superiores, pero igualmente reflejan integración parcial. De 43 criterios de integración 25 fueron identificados con fragmentación por más de 50 % de los encuestados. Conclusiones: Existen reservas de mejora para la integración de la medicina natural y tradicional en la red de prestación de servicios que deben ser tomadas en cuenta para el desarrollo de estrategias de intervención.

ABSTRACT Introduction: Cuba recognizes and acts accordingly to develop an integrated and comprehensive approach to health care that provides to the health professionals and, in particular, to the users of health services access to the natural and traditional medicine in a safe, affordable and effective ways. However, in practice the services perceived weaknesses in the integration of natural and traditional medicine to the rest of the system for delivery health services. Objective: To identify the level of integration of Traditional Natural Medicine (TNM) to the services. Methodology: A research was conducted in the way of cross section's health systems and services (ISSS) and applying a method for capturing and processing integration's valuable information according to criteria assessed by provincial -level executives who manage the national program on TNM. Results: Integration of TNM services in the country is partial (IG = 0.64) as perceived by managers. Of 14 integration goals explored, there were 13 with partial integration and one goal is fragmented. The western region showed the lowest rate of integration (0.61), while the central and eastern areas had slightly higher levels of integration but also reflect partial integration. From 43 explored integration criteria, 25 were identified with fragmentation by 50 % of those polled. Conclusions: There are perspectives of improvement for the integration of TNM in the network to provide services that should be taken into account for the development of intervention strategies.

Probability estimation of a Carrington-like geomagnetic storm.

Intense geomagnetic storms can cause severe damage to electrical systems and communications. This work proposes a counting process with Weibull inter-occurrence times in order to estimate the probability of extreme geomagnetic events. It is found that the scale parameter of the inter-occurrence time distribution grows exponentially with the absolute value of the intensity threshold defining the storm, whereas the shape parameter keeps rather constant. The model is able to forecast the probability of occurrence of an event for a given intensity threshold; in particular, the probability of occurrence on the next decade of an extreme event of a magnitude comparable or larger than the well-known Carrington event of 1859 is explored, and estimated to be between 0.46% and 1.88% (with a 95% confidence), a much lower value than those reported in the existing literature.