Exploring environmental and climate features associated with yellow fever across space and time in the Brazilian Atlantic Forest biome.

The Atlantic Forest Biome (AFB) creates an ideal environment for the proliferation of vector mosquitoes, such as Haemagogus and Sabethes species, which transmit the Yellow Fever virus (YFV) to both human and non-human primates (NHP) (particularly Alouatta sp. and Callithrix sp.). From 2016 to 2020, 748 fatal cases of YF in humans and 1,763 in NHPs were reported in this biome, following several years free from the disease. This underscores the imminent risk posed by the YFV. In this study, we examined the spatiotemporal distribution patterns of YF cases in both NHPs and humans across the entire AFB during the outbreak period, using a generalized linear mixed regression model (GLMM) at the municipal level. Our analysis examined factors associated with the spread of YFV, including environmental characteristics, climate conditions, human vaccination coverage, and the presence of two additional YFV-affected NHP species. The occurrence of epizootics has been directly associated with natural forest formations and the presence of species within the Callithrix genus. Additionally, epizootics have been shown to be directly associated with human prevalence. Furthermore, human prevalence showed an inverse correlation with urban areas, temporary croplands, and savannah and grassland areas. Further analyses using Moran's Index to incorporate the neighborhoods of municipalities with cases in each studied host revealed additional variables, such as altitude, which showed a positive correlation. Additionally, the occurrence of the disease in both hosts exhibited a spatio-temporal distribution pattern. To effectively mitigate the spread of the virus, it is necessary to proactively expand vaccination coverage, refine NHP surveillance strategies, and enhance entomological surveillance in both natural and modified environments.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57 µg/m3 and PM10 at 39.58 µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3 months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8 months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Impact of climate on varicella distribution in Bulgaria (2009-2018).

OBJECTIVES: Temperature is the most important environmental variable associated with the varicella frequency across the world. The present study compares the incidence of varicella in the districts of Bulgaria against some climatic factors and tries to find environmental variables which account for the differences in the varicella distribution observed among the Bulgarian districts. METHODS: The 28 Bulgarian districts were used as units of observation and their average 10-year varicella incidence (2009-2018) was tested for correlation with the standard bioclimatic variables of WorldClim, version 2. RESULTS: The WorldClim estimates for the annual mean temperature, the maximal temperature of the warmest month, the minimal temperature of the coldest month, the mean temperature of the coldest quarter, and the solar radiation inversely and not significantly correlated with the average 10-year varicella frequency. The precipitation of the warmest quarter and the wind speed correlated positively and also not significantly. Only the mean temperature of the driest quarter correlates significantly with the incidence at district level (Spearman's rank correlation coefficient of -0.45, p = 0.02). The mean of average 10-year varicella incidence rates among districts with driest quarter during the winter (January, February, March) was 387.6 ± 114.1, while among districts with driest quarter during the summer/autumn (July, August, September or August, September, October) 283.3 ± 102.1 (p = 0.02, ANOVA test). CONCLUSIONS: Dry winter and/or wet summer appear as significant determinants for the fluctuant spread of varicella infection in Bulgaria.

Past and current climate as a driver in shaping the distribution of the Longitarsus candidulus species group (Coleoptera: Chrysomelidae).

Longitarsus candidulus (Foudras) is a thermophilic flea beetle species widely distributed in the Mediterranean Basin and associated with Daphne gnidium L. and Thymelaea hirsuta (L.). Longitarsus laureolae Biondi and Longitarsus leonardii Doguet, phylogenetically closely related to L. candidulus, show together a peculiar and rare disjunct distribution along the central-southern Apennines and the Cantabrian-Pyrenean mountain system, respectively. Both are associated with Daphne laureola L. in mesophilic habitats. We used "ecological niche modeling" to infer the Pleistocene dynamics in the distribution of the three flea beetle species and their host plants. We interpreted their current distributions, paying particular attention to the presumed time of species divergence as inferred from recent studies. The differentiation of L. laureolae and L. leonardii from L. candidulus likely represents a response to the marked climatic changes during the Late Pliocene. Such a split was likely associated with a trophic niche shift of the laureolae/leonardii ancestor towards the typically mesophilic host plant D. laureola. The subsequent split between L. laureolae and L. leonardii, possibly due at first to the niche competition, was then boosted by an allopatric divergence during the Middle Pleistocene, likely caused by a large area of low environmental suitability for both species, mainly located between the northern Apennines and the south-western Alps.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Quantification and mapping of regulating ecosystem services in canola agroecosystems (case study: Gorgan County, Iran).

Regulating services are the advantages that humans receive from regulating ecosystem processes. These services include, but are not limited to pollination, climate regulation, water purification, carbon sequestration, and erosion control. Quantifying and mapping ecosystem services in agroecosystems is one of the main effective actions to increase pay attention to these services and adopt suitable approaches to direct sustainability. The purpose of the study was quantification, and mapping of regulating ecosystem services in canola agroecosystems of Gorgan County, north of Iran. For this purpose, some regulating services such as carbon sequestration, climate regulation, soil microbial respiration, soil aggregate stability, and pollination by insects were evaluated based on the Common International Classification of Ecosystem Services framework. The information and data required for each of these services were collected through field measurements, laboratory experiments, and field surveys. After quantifying, the surveyed services in canola agroecosystems were presented on geospatial maps generated by ArcGIS software, version 10.3. Results showed that agroecosystems in the west and north of the studied region provided the more regulating services. Also, the results of the pollination showed that pollinating insects belonged to four orders and 13 families. The majority of the pollinators were Hymenoptera (44.74%), especially honey bees (Apis mellifera L.), Diptera (5.26%), Butterflies (Lepidoptera; 25%), and the beetles (Coleoptera; 25%), and Anthophora sp. and Andrena sp. were the second and the third most abundant pollinating species after honey bees. Generally, the canola agroecosystems close to the rivers and the natural ecosystems provided more services than other regions.

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis.

BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. CONCLUSION: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China.

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing-decreasing-increasing-decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

[Response of branch attributes of Larix kaempferi to climate variables]. / 日本落叶松枝条属性特征对气候变量的响应.

We established a mixed-effects model incorporating climatic factors for the base diameter and length of the primary branches of Larix kaempferi using stepwise regression, based on climatic data from a total of 40 standard plots located in Xiaolongshan, Gansu Province, Changlinggang Forest Farm in Jianshi County, Hubei Province, and Dagujia Forest Farm in Qingyuan County, Liaoning Province, as well as the data from 120 L. kaempferi sample trees. Additionally, we created prediction charts for the fixed effects portion of the optimal mixed model to determine the relationship between climatic factors and base diameter and branch length, to explore the differential response of L. kaempferi branches to climatic variables. The results showed that the base diameter mixing model with annual mean temperature and water vapor deficit and the branch length mixing model with annual mean temperature had the best fitting effect, with R2 of 0.6152 and 0.6823, respectively. Based on the fixed effects prediction chart of the mixed model, the overall basal diameter showed an increasing trend with the increases of relative branch depth. The average basal diameter size was in an order of young-aged plantation

[Growth characteristic of statolith of Todarodes pacificus in the East China Sea in the La Niña year]. / 拉尼娜发生期东海太平洋褶柔鱼耳石生长特征.

To investigate the differences on morphological growth patterns of statolith of Todarodes pacificus in the East China Sea during La Niña and normal years, we analyzed the samples of T. pacificus collected in the East China Sea by Chinese light purse seine fishery fleets from February to April in 2020 (a normal year) and 2021 (a La Niña year). The results showed that total statolith length (TSL), lateral dome length (LDL), wing length (WL), and maximum width (MW) could be used as characterization parameters to representing the morphological growth of statolith. The characterization parameters of statolith in T. pacificus differed significantly between different climate years and between different genders. The values of those characterization parameters of statolith were greater in normal year than those in La Niña year, which in both years were larger in females, except for TSL in males in La Niña year. The statolith growth of males were faster than that of females in different climate years. TSL, LDL, and WL increased faster in normal year, while MW increased faster in La Niña year. The relative size of statolith gradually slowed down with the growth of individuals.

Expert judgement reveals current and emerging UK climate-mortality burden.

Weather and climate patterns play an intrinsic role in societal health, yet a comprehensive synthesis of specific hazard-mortality causes does not currently exist. Country-level health burdens are thus highly uncertain, but harnessing collective expert knowledge can reduce this uncertainty, and help assess diverse mortality causes beyond what is explicitly quantified. Here, surveying 30 experts, we provide the first structured expert judgement of how weather and climate directly impact mortality, using the UK as an example. Current weather-related mortality is dominated by short-term exposure to hot and cold temperatures leading to cardiovascular and respiratory failure. We find additional underappreciated health outcomes, especially related to long-exposure hazards, including heat-related renal disease, cold-related musculoskeletal health, and infectious diseases from compound hazards. We show potential future worsening of cause-specific mortality, including mental health from flooding or heat, and changes in infectious diseases. Ultimately, this work could serve to develop an expert-based understanding of the climate-related health burden in other countries.

Spatial asymmetry of temporal ecological processes can shift in riverine macroinvertebrates responding to fluctuating climate conditions.

Exploring the temporal dynamics of biological communities can offer valuable insights into the underlying mechanisms driving changes in biodiversity in the context of short and long-term effects of climate fluctuations. However, an understanding of how temporal shifts in climatic fluctuations influence the spatial patterns of the temporary ecological processes remains unexplored. This study examined the relative importance of temporary deterministic and stochastic processes (i.e., the influence of environmental filtering compared to stochastic variation within the same community) on community dynamics across watersheds in 15 rivers of the European Iberian Peninsula using 21 years of data. This study was divided into two time periods (i.e., 1997-2006 and 2007-2017). The climatic differences between the periods included decreasing levels and heightened variability of precipitation. Additionally, there were declining minimum temperatures and rising maximum temperatures, accompanied by reduced fluctuations in both minimum and maximum temperatures. Water quality and its variations also occur along an elevation pattern and changed over the time period studied. Spatial patterns of the relative importance of the ecological processes shifted between the two decades. The significance of stochastic processes increased with elevation in the earlier period, although no clear elevation pattern emerged in the later period. At the same time, the importance of deterministic processes decreased with elevation in the earlier period, and there was no clear pattern of elevation in the later period. An understanding of the patterns in community dynamics existing at various elevations over time can lay the groundwork for predicting and mitigating the impacts of short-term climate changes on biodiversity and guide appropriate conservation actions.

The ant and the grasshopper: Contrasting responses and behaviors to water stress of riparian trees along a hydroclimatic gradient.

Riparian trees are particularly vulnerable to drought because they are highly dependent on water availability for their survival. However, the response of riparian tree species to water stress varies depending on regional hydroclimatic conditions, making them unevenly vulnerable to changing drought patterns. Understanding this spatial variability in stress responses requires a comprehensive assessment of water stress across broader spatial and temporal scales. Yet, the precise ecophysiological mechanisms underlying these responses remain poorly linked to remotely sensed indices. To address this gap, the implementation of remote sensing methods coupled with in situ validation is essential to obtain consistent results across diverse spatial and temporal contexts. We conducted a multi-tool analysis combining multispectral and thermal remote sensing indices with in situ ecophysiological measurements at different temporal scales to analyze the responses of white poplar (Populus alba) to seasonal changes in drought along a hydroclimatic gradient. Using this approach, we demonstrate that white poplars along the Rhône River (France) exhibit contrasting responses and behaviors during drought depending on the latitudinal context. White poplars in a Mediterranean climate show rapid stomatal closure to reduce water loss and maintain high minimum water potential levels, although this results in a decrease in remotely sensed greenness. Conversely, white poplars located upstream in a temperate climate show high transpiration and stable greenness but lower minimum water potential and water content. A site in the middle of the gradient has intermediate responses. These results demonstrate that white poplars along a climate gradient can have a range of responses to drought along the iso/anisohydricity continuum. These results are important for future climatic conditions because they show that the same species can have different mechanisms of drought resilience, even in the same river valley. This raises questions regarding how these riparian tree populations will respond to future climatic and hydrological conditions.

[Global quantification of the spatial variability and temporal stability of throughfall]. / æž—å† ç©¿é€é›¨ç©ºé—´å¼‚è´¨æ€§åŠå ¶æ—¶é—´ç¨³å®šæ€§ç‰¹å¾çš„å ¨çƒé‡åŒ–.

Spatial variability of throughfall (i.e. the non-uniform characteristics of throughfall at different canopy positions) and its temporal persistence (i.e. time stability) are related to the quantity and efficiency of soil moisture replenishment, and affect plant competition and community succession dynamics by affecting resource availability. We carried out a meta-analysis with 554 papers (from 2000 to 2022) retrieved from Web of Science and China National Knowledge Infrastructure (CNKI) based on keyword search, quantified and compared the amount, spatial heterogeneity, and temporal stability characteristics of penetrating rain in different climate zones and plant functional types. Our results that throughfall proportion was lower in arid regions (72.0%±13.6%) than humid (75.1%±9.3%) and semi-humid areas (79.9%±10.4%). Cold climates had lower values (74.1%±14.6%) than temperate (74.2%±7.5%) and tropical climates (80.9%±14.6%). Shrubs (68.9%±14.9%) generally had lower throughfall proportion than trees (76.7%±9.1%). Broad-leaved trees (75.2%±11.1%) and conifers (75.1%±9.9%) showed similar throughfall proportions, as did evergreen (76.7%±10.0%) and deciduous species (74.7%±11.9%). Additionally, spatial variability (coefficient of variation) did not significantly differ across rainfall zones, temperature zones, or vegetation types. The spatial distribution of throughfall was relatively stable. Canopy structure was the dominant factor affecting temporal stability of throughfall. However, there was a lack of comparison between typical geographic units (i.e. spatial units with basically consistent geographical environmental conditions) at various temporal scales. Future research should expand upwards to the summary of global spatial scale rules and downwards to the analysis of process based temporal scale mechanisms, to depict the dynamic distribution of penetrating rain and unify observation standards to enhance comparability of different studies, in order to efficiently promote research on canopy penetrating rain and provide ecological and hydrological basis for protecting nature, managing artificial activities, and restoring degraded ecosystems.

A climate threshold for ocean deoxygenation during the Early Cretaceous.

Oceanic anoxic events (OAEs) are historical intervals of global-scale ocean deoxygenation associated with hyperthermal climate states and biological crises1,2. Massive volcanic carbon dioxide (CO2) emissions frequently associated with these events are thought to be a common driver of ocean deoxygenation through several climate-warming-related mechanisms1,3,4. The Early Cretaceous OAE1a is one of the most intense ocean deoxygenation events, persisting for more than 1 Myr (refs. 5,6). However, existing records of marine chemistry and climate across OAE1a are insufficient to fully resolve the timing and dynamics of the underlying processes, thus obscuring cause-and-effect relationships between climate forcing and ocean oxygenation states. Here we show that rapid ocean deoxygenation during OAE1a is linked to volcanic CO2 emissions and the crossing of an associated climate threshold, after which the sluggish pace of the silicate-weathering feedback and climate recovery delayed reoxygenation for >1 Myr. At the end of OAE1a, recrossing this threshold allowed for ocean reoxygenation. Following OAE1a, however, the Earth system remained sufficiently warm such that orbitally forced climate dynamics led to continued cyclic ocean deoxygenation on approximately 100-kyr timescales for another 1 Myr. Our results thus imply a tight coupling between volcanism, weathering and ocean oxygen content that is characterized by a climate threshold.

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran's precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

Bayesian spatio-temporal analysis of dengue transmission in Lao PDR.

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People's Democratic Republic (Lao PDR). This study aimed to describe spatial-temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR.

Effect of low climate impact vs. high climate impact inhalers for patients with asthma and COPD-a nationwide cohort analysis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and asthma can be treated with inhaled corticosteroids (ICS) delivered by low climate impact inhalers (dry powder inhalers) or high climate impact inhalers (pressurized metered-dose inhalers containing potent greenhouse gasses). ICS delivered with greenhouse gasses is prescribed ubiquitously and frequent despite limited evidence of superior effect. Our aim was to examine the beneficial and harmful events of ICS delivered by low and high climate impact inhalers in patients with asthma and COPD. METHODS: Nationwide retrospective cohort study of Danish outpatients with asthma and COPD treated with ICS delivered by low and high climate impact inhalers. Patients were propensity score matched by the following variables; age, gender, tobacco exposure, exacerbations, dyspnoea, body mass index, pulmonary function, ICS dose and entry year. The primary outcome was a composite of hospitalisation with exacerbations and all-cause mortality analysed by Cox proportional hazards regression. RESULTS: Of the 10,947 patients with asthma and COPD who collected ICS by low or high climate impact inhalers, 2,535 + 2,535 patients were propensity score matched to form the population for the primary analysis. We found no association between high climate impact inhalers and risk of exacerbations requiring hospitalization and all-cause mortality (HR 1.02, CI 0.92-1.12, p = 0.77), nor on pneumonia, exacerbations requiring hospitalization, all-cause mortality, or all-cause admissions. Delivery with high climate impact inhalers was associated with a slightly increased risk of exacerbations not requiring hospitalization (HR 1.10, CI 1.01-1.21, p = 0.03). Even with low lung function there was no sign of a superior effect of high climate impact inhalers. CONCLUSION: Low climate impact inhalers were not inferior to high climate impact inhalers for any risk analysed in patients with asthma and COPD.

The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019.

BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya. METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality. RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level. CONCLUSION: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.