Geohealth最新文献

In 2016, unprecedented intense wildfires burned over 150,000 acres in the southern Appalachian Mountains in the United States. Smoke from these fires greatly impacted the region and exposure to this smoke was significant. A bidirectional case-crossover design was applied to assess the relationship between PM_2.5 (a surrogate for wildfire smoke) exposure and respiratory- and cardiovascular-related emergency department (ED) visits in Western North Carolina during these events. For 0-, 3-, and 7-day lags, findings indicated a significant increase in the odds of being admitted to the ED for a respiratory (ORs: 1.055, 95% CI: 1.048–1.063; 1.083, 1.074–1.092; 1.066, 1.058–1.074; respectively) or cardiovascular event (ORs: 1.052, 95% CI: 1.045–1.060; 1.074, 1.066–1.081; 1.067, 1.060–1.075; respectively) for every 5 μg/m³ increase in PM_2.5 over a chosen cutpoint of 20.4 μg/m³. For all endpoints assessed except for emphysema, there were statistically significant increases in odds from 5.1% to 8.3%. In general, this increase was most pronounced 3 days after exposure. Additionally, individuals aged 55+ generally experience higher odds of heart disease at the 3- and 7-day lag points, and Black/African Americans generally experience higher odds of asthma at the 3-day lag point. In general, larger fires and increased numbers of fires within counties resulted in higher health burden at same day exposure. In a secondary analysis, the odds of an ED visit increased by over 40% in several cases among people exposed to days above the Environmental Protection Agency 24-hr PM_2.5 standard of 35 μg/m³. Our findings provide new understanding on the health impacts of wildfires on rural populations in the southeastern US.

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration-response functions (CRF) for PM2.5 from wildfire and non-wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire-related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations.

As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air-mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory-based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air-mass movement for diverse application domains, for example, to assess long-term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke.

The impacts of renewable energy shifting, passenger car electrification, and lightweighting through 2050 on the atmospheric concentrations of PM_2.5 total mass and oxidative stress-inducing metals (PM_2.5-Fe, Cu, and Zn) in Japan were evaluated using a regional meteorology–chemistry model. The surface concentrations of PM_2.5 total mass, Fe, Cu, and Zn in the urban area decreased by 8%, 13%, 18%, and 5%, respectively. Battery electric vehicles (BEVs) have been considered to have no advantage in terms of non-exhaust PM emissions by previous studies. This is because the disadvantages (heavier weight increases tire wear, road wear, and resuspention) offset the advantages (regenerative braking system (RBS) reduces brake wear). However, the future lightweighting of drive battery and body frame were estimated to reduce all non-exhaust PM. Passenger car electrification only reduced PM_2.5 concentration by 2%. However, Fe and Cu concentrations were more reduced (−8% and −13%, respectively) because they have high brake wear-derived and significantly reflects the benefits of BEV's RBS. The water-soluble fraction concentration of metals (induces oxidative stress in the body) was estimated based on aerosol acidity. The reduction of SO_x, NO_x, and NH₃ emissions from on-road and thermal power plants slightly changed the aerosol acidity (pH ± 0.2). However, it had a negligible effect on water-soluble metal concentrations (maximum +2% for Fe and +0.5% for Cu and Zn). Therefore, the metal emissions reduction was more important than gaseous pollutants in decreasing the water-soluble metals that induces respiratory oxidative stress and passenger car electrification and lightweighting were effective means of achieving this.

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high-resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high-accuracy and resolution (5 m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R², +17% Relative Contribution) and over the set of simpler predictors (+18% R², +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities—Dakar, Dar es Salaam, Kampala and Ouagadougou—and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%–40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.

Anthropogenic climate change is causing a rise in global temperatures, with this trend projected to increase into the future. Rising temperatures result in an increase in the frequency and severity of heatwave events, with an associated increase in poor health outcomes for vulnerable individuals. This places an increasing strain on health care services. However, methods calculating future health care costs associated with this trend are poorly understood. We calculated health care costs attributable to heatwave events in Tasmania 2009–2019, using ambulance dispatches as a case study. We also modeled the expected health and economic burden for projected heatwave frequencies between 2010 and 2089. We developed our models based on two possible approaches to describing population adaptation to heatwaves—an adapted population calculated by determining heatwave episodes using a rolling baseline, and a non-adapted population calculated by determining heatwave episodes using a static baseline. Using a rolling baseline calculation for 2010 to 2089, we estimated additional ambulance costs averaging AUD$57,147 per year and totaling AUD$4,571,788. For the same period using a static baseline, we estimated additional ambulance costs averaging AUD$517,342 per year and totaling AUD$41,387,349. While this method is suitable for estimating the health care costs associated with heatwaves, it could be utilized for estimating health care costs related to other climate-related extreme events. Different methods of estimating heatwaves, modeling an adapted versus non-adapted population, provide substantial differences in projected costs. There is potential for considerable health system cost savings when a population is supported to adapt to extreme heat.

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM₁₀, PM_2.5), carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O₃. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO₂ concentration. Cluster 2 included warmer and dried months, and higher PM₁₀, PM_2.5, CO, and O₃ concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value < 0.05) correlation (r) between RSDxPBI (r_RSD = 0.58) and PNEUxPBI (r_PNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r_RSD = 0.48), PNEUxPBI (r_PNEU = 0.47), COPDxPBI (r_COPD = 0.45), CSDxAPI (r_CSD = 0.70), RSDxAPI (r_CSD = 0.71), PNEUxAPI (r_PNEU = 0.49), and COPDxAPI (r_PNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

The objective of this study was to identify spatial disparities in the distribution of cancer hotspots within Romania. Additionally, the research aimed to track prevailing trends in cancer prevalence and mortality according to a cancer type. The study covered the timeframe between 2008 and 2017, examining all 3,181 territorial administrative units. The analysis of spatial distribution relied on two key parameters. The first parameter, persistence, measured the duration for which cancer prevalence exceeded the 75th percentile threshold. Cancer prevalence refers to the total number of individuals in a population who have been diagnosed with cancer at a specific time point, including both newly diagnosed cases (occurrence) and existing cases. The second parameter, the time continuity of persistence, calculated the consecutive months during which cancer prevalence consistently surpassed the 75th percentile threshold. Notably, persistence of elevated values was also evident in lowland regions, devoid of any discernible direct connection to environmental conditions. In conclusion, this work bears substantial relevance to regional health policies, by aiding in the formulation of prevention strategies, while also fostering a deeper comprehension of the socioeconomic and environmental factors contributing to cancer.