Validation of a Handheld 6-Lead Device for QT Interval Monitoring in Resource-Limited Settings.

Importance: Rifampin-resistant tuberculosis treatment regimens require electrocardiographic (ECG) monitoring due to the use of multiple QTc-prolonging agents. Formal 12-lead ECG devices represent a significant burden in resource-constrained clinics worldwide and a potential barrier to treatment scale-up in some settings.

Objective: To evaluate the diagnostic accuracy of a handheld 6-lead ECG device within resource-constrained clinics.

Seasonality of cholera in Kolkata and the influence of climate.

Background: Cholera in Kolkata remains endemic and the Indian city is burdened with a high number of annual cases. Climate change is widely considered to exacerbate cholera, however the precise relationship between climate and cholera is highly heterogeneous in space and considerable variation can be observed even within the Indian subcontinent. To date, relatively few studies have been conducted regarding the influence of climate on cholera in Kolkata.

Proliferation of atmospheric datasets can hinder policy making: a data blending technique offers a solution.

The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyze several datasets representing rainfall over Nepal.

Current and future trends in heat-related mortality in the MENA region: a health impact assessment with bias-adjusted statistically downscaled CMIP6 (SSP-based) data and Bayesian inference.

Background: The Middle East and North Africa (MENA) is one of the regions that is most vulnerable to the negative effects of climate change, yet the potential public health impacts have been underexplored compared to other regions. We aimed to examine one aspect of these impacts, heat-related mortality, by quantifying the current and future burden in the MENA region and identifying the most vulnerable countries.

Methods: We did a health impact assessment using an ensemble of bias-adjusted statistically downscaled Coupled Model Intercomparison Project phase 6 (CMIP6) data based on four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2·6 [consistent with a 2°C global warming scenario], SSP2-4·5 [medium pathway scenario], SSP3-7·0 [pessimistic scenario], and SSP5-8·5 [high emissions scenario]) and Bayesian inference methods.

Applying an ecosystem services framework on nature and mental health to recreational blue space visits across 18 countries.

The effects of 'nature' on mental health and subjective well-being have yet to be consistently integrated into ecosystem service models and frameworks. To address this gap, we used data on subjective mental well-being from an 18-country survey to test a conceptual model integrating mental health with ecosystem services, initially proposed by Bratman et al. We analysed a range of individual and contextual factors in the context of 14,998 recreational visits to blue spaces, outdoor environments which prominently feature water.

Quantifying Spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach.

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009-2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France.

Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events.

Understanding changes in extreme compound hazard events is important for climate mitigation and policy. By definition, such events are rare so robust quantification of their future changes is challenging. An approach is presented, for probabilistic modelling and simulation of climate model data, which is invariant to the event definition since it models the underlying weather variables.

Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method.

The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability.

A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping.

Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity.

The Acute Effects of Heavy Sled Towing on Acceleration Performance and Sprint Mechanical and Kinematic Characteristics.

The aim of this study was to investigate the effects of heavy sled towing using a load corresponding to a 50% reduction of the individual theoretical maximal velocity (ranged 57-73% body mass) on subsequent 30 m sprint performance, velocity, mechanical variables (theoretical maximal horizontal force, theoretical maximal horizontal velocity, maximal mechanical power output, slope of the linear force-velocity relationship, maximal ratio of horizontal to total force and decrease in the ratio of horizontal to total force) and kinematics (step length and rate, contact and flight time). Twelve ( = 5 males and = 7 females) junior running sprinters performed an exercise under two intervention conditions in random order. The experimental condition (EXP) consisted of two repetitions of 20 m resisted sprints, while in the control condition (CON), an active recovery was performed.

The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil.

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded.

Household cooking fuel estimates at global and country level for 1990 to 2030.

Household air pollution generated from the use of polluting cooking fuels and technologies is a major source of disease and environmental degradation in low- and middle-income countries. Using a novel modelling approach, we provide detailed global, regional and country estimates of the percentages and populations mainly using 6 fuel categories (electricity, gaseous fuels, kerosene, biomass, charcoal, coal) and overall polluting/clean fuel use - from 1990-2020 and with urban/rural disaggregation. Here we show that 53% of the global population mainly used polluting cooking fuels in 1990, dropping to 36% in 2020.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components.

Associations between green/blue spaces and mental health across 18 countries.

Living near, recreating in, and feeling psychologically connected to, the natural world are all associated with better mental health, but many exposure-related questions remain. Using data from an 18-country survey (n = 16,307) we explored associations between multiple measures of mental health (positive well-being, mental distress, depression/anxiety medication use) and: (a) exposures (residential/recreational visits) to different natural settings (green/inland-blue/coastal-blue spaces); and (b) nature connectedness, across season and country. People who lived in greener/coastal neighbourhoods reported higher positive well-being, but this association largely disappeared when recreational visits were controlled for.

Environmental DNA reveals links between abundance and composition of airborne grass pollen and respiratory health.

Grass (Poaceae) pollen is the most important outdoor aeroallergen, exacerbating a range of respiratory conditions, including allergic asthma and rhinitis ("hay fever"). Understanding the relationships between respiratory diseases and airborne grass pollen with a view to improving forecasting has broad public health and socioeconomic relevance. It is estimated that there are over 400 million people with allergic rhinitis and over 300 million with asthma, globally, often comorbidly.

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning.

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes.

Peaks in bat activity at turbines and the implications for mitigating the impact of wind energy developments on bats.

Wind turbines are a relatively new threat to bats, causing mortalities worldwide. Reducing these fatalities is essential to ensure that the global increase in wind-energy facilities can occur with minimal impact on bat populations. Although individual bats have been observed approaching wind turbines, and fatalities frequently reported, it is unclear whether bats are actively attracted to, indifferent to, or repelled by, the turbines at large wind-energy installations.

A laboratory investigation into features of morphology and physiology for their potential to predict reproductive success in male frogs.

Amphibian populations are declining globally, however, the contribution of reduced reproduction to declines is unknown. We investigated associations between morphological (weight/snout-vent length, nuptial pad colour/size, forelimb width/size) and physiological (nuptial pad/testis histomorphology, plasma hormones, gene expression) features with reproductive success in males as measured by amplexus success and fertility rate (% eggs fertilised) in laboratory maintained Silurana/Xenopus tropicalis. We explored the robustness of these features to predict amplexus success/fertility rate by investigating these associations within a sub-set of frogs exposed to anti-androgens (flutamide (50 μg/L)/linuron (9 or 45 μg/L)).

Results from an 18 country cross-sectional study examining experiences of nature for people with common mental health disorders.

Exposure to natural environments is associated with a lower risk of common mental health disorders (CMDs), such as depression and anxiety, but we know little about nature-related motivations, practices and experiences of those already experiencing CMDs. We used data from an 18-country survey to explore these issues (n = 18,838), taking self-reported doctor-prescribed medication for depression and/or anxiety as an indicator of a CMD (n = 2698, 14%). Intrinsic motivation for visiting nature was high for all, though slightly lower for those with CMDs.

Spatial co-localisation of extreme weather events: a clear and present danger.

Extreme weather events have become a dominant feature of the narrative surrounding changes in global climate with large impacts on ecosystem stability, functioning and resilience; however, understanding of their risk of co-occurrence at the regional scale is lacking. Based on the UK Met Office's long-term temperature and rainfall records, we present the first evidence demonstrating significant increases in the magnitude, direction of change and spatial co-localisation of extreme weather events since 1961. Combining this new understanding with land-use data sets allowed us to assess the likely consequences on future agricultural production and conservation priority areas.

Multivariate hierarchical frameworks for modeling delayed reporting in count data.

In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short-term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty.

Desirable BUGS in models of infectious diseases.

Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible.

Differences in the Force Velocity Mechanical Profile and the Effectiveness of Force Application During Sprint-Acceleration Between Sprinters and Hurdlers.

This cross-sectional study aimed to compare the horizontal and vertical force-velocity profile between female sprinters and hurdlers. Twelve high-level athletes (6 sprinters and 6 hurdlers) participated in this investigation. The testing procedures consisted of two maximal 40-m sprints and five to six vertical jumps with additional loads.

A modelling approach for correcting reporting delays in disease surveillance data.

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities.