Effects of climate and environmental factors on childhood and adolescent asthma: A systematic review based on spatial and temporal analysis evidence.

BACKGROUND: Asthma is a prevalent chronic respiratory disease among children, influenced by various climate and environmental factors. Despite its prevalence, the specific effects of these factors on asthma remain unclear. This study aims to systematically assess the epidemiological evidence using spatial and temporal methods on the impact of climate and environmental factors on childhood asthma. METHODS: A systematic review was conducted to analyse the impact of climate and environmental factors on childhood asthma and wheezing, focusing on spatial and temporal trends. Searches were carried out in PubMed, Embase, and CINAHL databases for studies published from January 2000 to April 2024, using key search terms 'asthma/wheezing', 'extreme weather, 'green space', 'air pollution' and 'spatial or temporal analyses". RESULTS: The systematic review analysed 28 studies, with six employing spatial and 22 using temporal analysis methods; however, none incorporated spatio-temporal analysis in their models. The findings reveal that extreme weather events, including heatwaves and heavy rainfall, elevate childhood asthma risks across various climates, with significant effects observed during summer and winter months. Dust storms in arid and subtropical regions are linked to immediate spikes in hospital admissions due to asthma exacerbations. The effects of green spaces on childhood asthma are mixed, with some studies indicating protective effects while others suggest increased risks, influenced by local environmental factors. Air pollutants such as PM2.5, NO2, and ozone can exacerbate asthma symptoms and along with other environmental factors, contribute to seasonal effects. High temperatures generally correlate with increased asthma risks, though the effects vary by age, sex, and climate. CONCLUSION: Future research should integrate spatial and temporal methods to better understand the effects of environmental and climate changes on childhood asthma.

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia.

Queensland is the main coal mining state in Australia where populations in coal mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997-2014. An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk. The analysis considered 2,831,121 hospitalisation records. Coal mining was associated with a 4 % (2.4-5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coal mining areas for each, chronic circulatory and respiratory diseases. Coal mining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coal mining and these diseases.

Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia.

Introduction: The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022. Methods: Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk. Results: There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta. Conclusions: The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.

The association of wildfire air pollution with COVID-19 incidence in New South Wales, Australia.

The 2020 COVID-19 outbreak in New South Wales (NSW), Australia, followed an unprecedented wildfire season that exposed large populations to wildfire smoke. Wildfires release particulate matter (PM), toxic gases and organic and non-organic chemicals that may be associated with increased incidence of COVID-19. This study estimated the association of wildfire smoke exposure with the incidence of COVID-19 in NSW. A Bayesian mixed-effect regression was used to estimate the association of either the average PM10 level or the proportion of wildfire burned area as proxies of wildfire smoke exposure with COVID-19 incidence in NSW, adjusting for sociodemographic risk factors. The analysis followed an ecological design using the 129 NSW Local Government Areas (LGA) as the ecological units. A random effects model and a model including the LGA spatial distribution (spatial model) were compared. A higher proportional wildfire burned area was associated with higher COVID-19 incidence in both the random effects and spatial models after adjustment for sociodemographic factors (posterior mean = 1.32 (99% credible interval: 1.05-1.67) and 1.31 (99% credible interval: 1.03-1.65), respectively). No evidence of an association between the average PM10 level and the COVID-19 incidence was found. LGAs in the greater Sydney and Hunter regions had the highest increase in the risk of COVID-19. This study identified wildfire smoke exposures were associated with increased risk of COVID-19 in NSW. Research on individual responses to specific wildfire airborne particles and pollutants needs to be conducted to further identify the causal links between SARS-Cov-2 infection and wildfire smoke. The identification of LGAs with the highest risk of COVID-19 associated with wildfire smoke exposure can be useful for public health prevention and or mitigation strategies.

Environmental and sociodemographic risk factors associated with environmentally transmitted zoonoses hospitalisations in Queensland, Australia.

Zoonoses impart a significant public health burden in Australia particularly in Queensland, a state with increasing environmental stress due to extreme weather events and rapid expansion of agriculture and urban developments. Depending on the organism and the environment, a proportion of zoonotic pathogens may survive from hours to years outside the animal host and contaminate the air, water, food, or inanimate objects facilitating their transmission through the environment (i.e. environmentally transmitted). Although most of these zoonotic infections are asymptomatic, severe cases that require hospitalisation are an important indicator of zoonotic infection risk. To date, no studies have investigated the risk of hospitalisation due to environmentally transmitted zoonotic diseases and its association with proxies of sociodemographic and environmental stress. In this study we analysed hospitalisation data for a group of environmentally transmitted zoonoses during a 15-year period using a Bayesian spatial hierarchical model. The analysis incorporated the longest intercensal-year period of consistent Local Government Area (LGA) boundaries in Queensland (1996-2010). Our results showed an increased risk of environmentally transmitted zoonoses hospitalisation in people in occupations such as animal farming, and hunting and trapping animals in natural habitats. This risk was higher in females, compared to the general population. Spatially, the higher risk was in a discrete set of north-eastern, central and southern LGAs of the state, and a probability of 1.5-fold or more risk was identified in two separate LGA clusters in the northeast and south of the state. The increased risk of environmentally transmitted zoonoses hospitalisations in some LGAs indicates that the morbidity due these diseases can be partly attributed to spatial variations in sociodemographic and occupational risk factors in Queensland. The identified high-risk areas can be prioritised for health support and zoonosis control strategies in Queensland.

Amplitude Modulation Approach for Real-Time Algorithms of ECG-Derived Respiration / Desarrollo de Algoritmos en Tiempo Real para Derivar la Respiración del ECG Bajo el Criterio de Amplitud Modulada

This work presents the development of an ECG-Derived Respiration (EDR) methodology based on the amplitude modulation approach. It allows to redefine actual methodologies in order to obtain a continuous EDR signals with high correlations and small delay between EDR and respiration activity. Two algorithms are implemented: one of them using the amplitude modulation of the R-peak (EDRAM) and another one applying a band-pass filter in the bandwidth of respiration. Unlike other techniques in literature, conventional low order filters are applied without sacrifice of correlation factor (0.76 and 0.67) and a minimum delay of 0.27s (with EDRAM) in a ∼6s cycle. A robustness test was performed, and it shows a noise tolerance of up to 20% of the maximum value before its correlation factor drops considerably. The application into a wearable sensor was successfully implemented. The two methodologies proposed show advantages like real-time processing and robustness under certain noises. The proposed AM perspective supports the use of both algorithms for typical applications with high efficiency, low computational cost and ease of implementation. These characteristics result on a technique that facilitates the development of wearable systems, and to increase the information of actual databases.

Este trabajo presenta una metodología para la extracción de la actividad respiratoria derivada de un ECG (EDR, por sus siglas en ingles), basado en el enfoque de amplitud modulada (AM). Esto permite redefinir las metodologías actuales para obtener una señal EDR más continua, con altos factores de correlación y un retraso menor entre la EDR y la actividad respiratoria. Se implementaron dos algoritmos: uno utilizando la modulación de la amplitud del pico R (EDRAM) y el otro aplicando un filtro paso-banda en el espectro de frecuencia de la respiración. A diferencia de otros trabajos en la literatura, se utilizan filtros convencionales de bajo orden pero sin sacrificar el factor de correlación (0.76 y 0.67) y manteniendo un retardo de ∼0.27s (con EDRAM) en un ciclo de ∼6s. Se realizó una prueba de robustez, donde se muestra una tolerancia a ruido blanco de hasta un 20% del valor máximo antes de que el factor de correlación bajara considerablemente. El algoritmo EDRAM se aplicó con éxito en un prototipo de sistema portable. Las dos metodologías propuestas muestran ventajas como el procesamiento en tiempo real y robustez bajo ciertos ruidos. La perspectiva de AM propuesta soporta el uso de ambos algoritmos para aplicaciones típicas con alta eficiencia, bajo costo computacional y facilidad de implementación. Estas características hacen que esta técnica facilite el desarrollo de sistemas portátiles, así como para incrementar la información de las bases de datos actuales.