A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries.

The COVID-19 pandemic has expanded fast over the world, affecting millions of people and generating serious health, social, and economic consequences. All South East Asian countries have experienced the pandemic, with various degrees of intensity and response. As the pandemic progresses, it is important to track and analyse disease trends and patterns to guide public health policy and treatments. In this paper, we carry out a sequential cross-sectional study to produce reliable weekly COVID-19 death (out of cases) rates for South East Asian countries for the calendar years 2020, 2021, and 2022. The main objectives of this study are to characterise the trends and patterns of COVID-19 death rates in South East Asian countries through time, as well as compare COVID-19 rates among countries and regions in South East Asia. Our raw data are (daily) case and death counts acquired from "Our World in Data", which, however, for some countries and time periods, suffer from sparsity (zero or small counts), and therefore require a modelling approach where information is adaptively borrowed from the overall dataset where required. Therefore, a sequential cross-sectional design will be utilised, that will involve examining the data week by week, across all countries. Methodologically, this is achieved through a two-stage random effect shrinkage approach, with estimation facilitated by nonparametric maximum likelihood.

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.

Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother.

Research indicates a higher prevalence of attention deficits in children exposed to HG in utero compared to controls with some claiming that the deficit is due to prenatal effects of malnutrition in HG mothers and others that it is due to maternal mental health after birth. The current study examines the effect of hyperemesis gravidarum (HG) diagnosis during pregnancy on infant attention controlling for maternal stress, depression anxiety and attachment. Thirty-eight infants mean age 4 months were videotaped with their mothers (19 mothers with a hyperemesis diagnosis and 19 controls) during play with a soft toy and looking at a picture book. Infant attention was operationalized as gaze direction towards the play activity, mother, and 'distracted' (indicated by looking away from play or mother). Mothers completed stress, depression, anxiety, and attachment questionnaires. HG exposed infants attended for significantly less time during play with a book or soft toy compared to controls. Maternal stress, depression, anxiety, and attachment did not differ in HG mothers and controls. Infant ability to attend to the toy, book, mother or being distracted did not relate to maternal postnatal attachment, or mental health. These results suggest that the prenatal environment, especially exposure to HG might be associated with reduced infant attention abilities independent of maternal postnatal health.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study.

OBJECTIVE: The medication administration process is complex and consequently prone to errors. Closed Loop Medication Administration solutions aim to improve patient safety. We assessed the impact of a novel medication scanning device (MedEye) on the rate of medication administration errors in a large UK Hospital. METHODS: We performed a feasibility before and after study on one ward at a tertiary-care teaching hospital that used a commercial electronic prescribing and medication administration system. We conducted direct observations of nursing drug administration rounds before and after the MedEye implementation. We calculated the rate and type ('timing', 'omission' or 'other' error) of medication administration errors (MAEs) before and after the MedEye implementation. RESULTS: We observed a total of 1069 administrations before and 432 after the MedEye intervention was implemented. Data suggested that MedEye could support a reduction in MAEs. After adjusting for heterogeneity, we detected a decreasing effect of MedEye on overall errors (p = 0.0753). Non-timing errors ('omission' and 'other' errors) reduced from 51 (4.77%) to 11 (2.55%), a reduction of 46.5%, which had borderline significance at the 5% level, although this was lost after adjusting for confounders. CONCLUSIONS: This pilot study detected a decreasing effect of MedEye on overall errors and a reduction in non-timing error rates that was clinically important as such errors are more likely to be associated with harm. Further research is needed to investigate the impact on a larger sample of medications.

Effects of maternal mental health on prenatal movement profiles in twins and singletons.

AIM: Prenatal experiences, including maternal stress, depression and anxiety, form crucial building blocks affecting the maturation of the foetal central nervous system. Previous research has examined foetal movements without considering effects of maternal mental health factors critical for healthy foetal development. The aim of this research is to assess the effects of maternal mental health factors on foetal twin compared with singleton movement profiles. METHOD: We coded foetal touch and head movements in 56 ultrasound scans, from a prospective opportunity sample of 30 mothers with a healthy pregnancy (mean gestational age 27.8 weeks for singleton and 27.2 for twins). At the ultrasound scan appointment, participants completed questionnaires assessing their stress, depression and anxiety. RESULTS: Maternal depression increased foetal self-touch significantly. In foetal twins, maternal stress significantly decreased and maternal depression significantly increased other twin touch. Maternal mental health factors affected the head movements of twins significantly more than singletons, with maternal depression decreasing head movement frequency for twins significantly. CONCLUSION: These results indicate that maternal mental health might have an impact on types of body schemata formed in utero, in twin compared with singleton pregnancies. Future research needs to examine whether these prenatal effects affect postnatal differences in body awareness.

The effect of data aggregation on dispersion estimates in count data models.

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can be aggregated without loss of information on the Poisson parameter, which remains true if the Poisson assumption is relaxed towards quasi-Poisson. However, in biodosimetry in particular, but also beyond, the question of how the dispersion estimates for quasi-Poisson models behave under data aggregation have received little attention. Indeed, for real data sets featuring unexplained heterogeneities, dispersion estimates can increase strongly after aggregation, an effect which we will demonstrate and quantify explicitly for some scenarios. The increase in dispersion estimates implies an inflation of the parameter standard errors, which, however, by comparison with random effect models, can be shown to serve a corrective purpose. The phenomena are illustrated by γ-H2AX foci data as used for instance in radiation biodosimetry for the calibration of dose-response curves.

Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number.

PURPOSE: The traditional workflow for biological dosimetry based on manual scoring of dicentric chromosomes is very time consuming. Especially for large-scale scenarios or for low-dose exposures, high cell numbers have to be analyzed, requiring alternative scoring strategies. Semi-automatic scoring of dicentric chromosomes provides an opportunity to speed up the standard workflow of biological dosimetry. Due to automatic metaphase and chromosome detection, the number of counted chromosomes per metaphase is variable. This can potentially introduce overdispersion and statistical methods for conventional, manual scoring might not be applicable to data obtained by automatic scoring of dicentric chromosomes, potentially resulting in biased dose estimates and underestimated uncertainties. The identification of sources for overdispersion enables the development of methods appropriately accounting for increased dispersion levels. MATERIALS AND METHODS: Calibration curves based on in vitro irradiated (137-Cs; 0.44 Gy/min) blood from three healthy donors were analyzed for systematic overdispersion, especially at higher doses (>2 Gy) of low LET radiation. For each donor, 12 doses in the range of 0-6 Gy were scored semi-automatically. The effect of chromosome number as a potential cause for the observed overdispersion was assessed. Statistical methods based on interaction models accounting for the number of detected chromosomes were developed for the estimation of calibration curves, dose and corresponding uncertainties. The dose estimation was performed based on a Bayesian Markov-Chain-Monte-Carlo method, providing high flexibility regarding the implementation of priors, likelihood and the functional form of the association between predictors and dicentric counts. The proposed methods were validated by simulations based on cross-validation. RESULTS: Increasing dose dependent overdispersion was observed for all three donors as well as considerable differences in dicentric counts between donors. Variations in the number of detected chromosomes between metaphases were identified as a major source for the observed overdispersion and the differences between donors. Persisting overdispersion beyond the contribution of chromosome number was modeled by a Negative Binomial distribution. Results from cross-validation suggested that the proposed statistical methods for dose estimation reduced bias in dose estimates, variability between dose estimates and improved the coverage of the estimated confidence intervals. However, the 95% confidence intervals were still slightly too permissive, suggesting additional unknown sources of apparent overdispersion. CONCLUSIONS: A major source for the observed overdispersion could be identified, and statistical methods accounting for overdispersion introduced by variations in the number of detected chromosomes were developed, enabling more robust dose estimation and quantification of uncertainties for semi-automatic counting of dicentric chromosomes.

A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay.

Over the last decade, the γ-H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double-strand-breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure to ionizing radiation. While the existing literature has convincingly demonstrated a dose-response effect, and also presented approaches to dose estimation based on appropriately defined calibration curves, a more widespread practical use is still hampered by a certain lack of discussion and agreement on the specific dose-response modelling and uncertainty quantification strategies, as well as by the unavailability of implementations. This manuscript intends to fill these gaps, by stating explicitly the statistical models and techniques required for calibration curve estimation and subsequent dose estimation. Accompanying this article, a web applet has been produced which implements the discussed methods.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

PURPOSE: Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. MATERIALS AND METHODS: Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises. RESULTS: The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed. CONCLUSIONS: Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.

Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study.

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed.

Data compression and regression through local principal curves and surfaces.

We consider principal curves and surfaces in the context of multivariate regression modelling. For predictor spaces featuring complex dependency patterns between the involved variables, the intrinsic dimensionality of the data tends to be very small due to the high redundancy induced by the dependencies. In situations of this type, it is useful to approximate the high-dimensional predictor space through a low-dimensional manifold (i.e., a curve or a surface), and use the projections onto the manifold as compressed predictors in the regression problem. In the case that the intrinsic dimensionality of the predictor space equals one, we use the local principal curve algorithm for the the compression step. We provide a novel algorithm which extends this idea to local principal surfaces, thus covering cases of an intrinsic dimensionality equal to two, which is in principle extendible to manifolds of arbitrary dimension. We motivate and apply the novel techniques using astrophysical and oceanographic data examples.

Software for calculating blood lactate endurance markers.

Blood lactate markers are used as summary measures of the underlying model of an athlete's blood lactate response to increasing work rate. Exercise physiologists use these endurance markers, typically corresponding to a work rate in the region of high curvature in the lactate curve, to predict and compare endurance ability. A short theoretical background of the commonly used markers is given and algorithms provided for their calculation. To date, no free software exists that allows the sports scientist to calculate these markers. In this paper, software is introduced for precisely this purpose that will calculate a variety of lactate markers for an individual athlete, an athlete at different instants (e.g. across a season), and simultaneously for a squad.