Geohealth最新文献

Dengue is an arboviral fever. Weather modulates dengue transmission by influencing the life cycles of vector mosquitoes and the virus. Three teleconnections are known to affect the weather in Sri Lanka. Those are El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and ENSO Modoki. We studied correlations between dengue incidence (DI) in the Western Province (WP) of Sri Lanka as a whole and three districts of the province and indices of ENSO, IOD and ENSO Modoki. We used four indices of ENSO and one index each of IOD and ENSO Modoki. We acquired notified dengue cases in WP, population data and monthly indices of three teleconnections for the 2005–2019 period. We used wavelet time series analysis to determine correlations between indices of teleconnections and DI. Two indices of ENSO were correlated with the DI of the WP and all three districts of the WP individually. The other two indices were correlated with the DI of two districts. The index of IOD was correlated with DI of two districts. The index of ENSO Modoki was correlated with the DI of WP and one district of it. Both positive and negative extremes of at least one teleconnection index were followed by the rise of DI in all districts. We concluded that three teleconnections modulate DI of different districts of WP in different ways. Monitoring of indices of these teleconnections and escalating dengue preventive work after extremes of indices can potentially blunt impending dengue peaks.

Exposure to wildfire smoke is a well-known concern for public health and is anticipated to worsen with an increase in wildfire activity related to climate change. This study uses satellite and ground-based carbon monoxide (CO) measurements from 2004 to 2019 to evaluate a change in its seasonal cycle due to wildfire emissions. Monthly average CO total columns from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument over Alberta and Ontario, and from a ground-based Fourier transform infrared spectrometer in downtown Toronto are compared before and after 1 January 2012, following previous literature. Between the two time periods, a new peak emerges in the seasonal cycle of CO, centered around August. Monthly emergency room admissions from Alberta and Ontario for nine cardiovascular and respiratory diseases are assessed with a difference in difference analysis, using MOPITT CO as the exposure metric. This analysis was used to calculate the change in monthly hospital admissions per 100,000 people, given a 1 ppb increase in XCO post-2012 compared to pre-2012, along with the 95% confidence interval (CI). For Ontario, this term is positive and significant for hypertension (change = 1.88, CI = 1.18–2.57), ischemic heart disease (0.50, CI = 0.12–0.88), arrhythmia (0.12, CI = 0.03–0.22), and asthma (0.31, CI = 0.05–0.57). For Alberta, there is a significant and positive interaction for arrhythmia (0.48, CI = 0.12–0.85). These results indicate that there was a statistically significant increase in adverse health outcomes for five of the eighteen disease-province pairings associated with the increase in atmospheric CO after 2011 coinciding with enhanced wildfire emissions.

Eighty-four percent of sub-Saharan African households rely on polluting fuels (e.g., wood, charcoal) for cooking, leading to high levels of household air pollution (HAP). While switching to modern fuels/stoves could decrease HAP levels, they are not always available or affordable. Improved biomass cookstoves could provide an intermediate step supporting transitions from traditional biomass to clean burning fuels/stoves. We conducted two stove intervention trials in Lusaka, Zambia using targeted marketing/incentives to motivate participants to use improved biomass stoves, either the Mimi Moto (pellet) or the EcoZoom (charcoal). Before the intervention, 65% of participants exclusively used charcoal, while 27% relied on electricity to some extent for cooking. We measured 24-hr personal exposure to CO (n = 747) and PM_2.5 (n = 90) of primary cooks. We implemented several statistical approaches to estimate the effects of interventions on exposure: household-specific endline minus baseline exposure, ranksum testing, difference-in-differences analyses, and cross-sectional analyses. We found that switching from traditional charcoal stoves to either intervention stove was not associated with significantly reduced exposures. However, cooks using electric stoves independent of the intervention did have significantly lower CO exposures than those using traditional charcoal, with greater electric stove use corresponding to greater exposure reductions. Variability in exposure was dominated by seasonal, regional, and neighborhood differences rather than household stove/fuel choices. A focus on HAP exposure from cooking in urban settings is unlikely to yield expected exposure reductions. Policy makers should consider pollution reduction policies/interventions that target ambient air quality in tandem with HAP-mitigating strategies to address air pollution health burden.

PM_2.5 is a critical air pollutant that significantly impacts human health and the environment. To develop effective air quality management and mitigation strategies, understanding PM_2.5 source attribution and associated health risks is essential. This study investigates the source attribution and health burden of PM_2.5 focusing on Mainland Thailand (MT), North Thailand (NT), and the Bangkok Metropolitan Region (BMR), using the WRF-Chem model and a brute-force method for source attribution. PM_2.5 contributions from biomass burning including both crop and non-crop burning are quantified, along with contributions from transportation, industry, energy, residential, and other anthropogenic sectors. This study focuses on the haze season (February–April) in 2019. Our research shows that in-domain foreign country's biomass burning is a major contributor to PM_2.5, accounting for 23%–38% of PM_2.5 concentrations in MT. In NT, non-crop burning within MT contributes the most (21%–36%) to PM_2.5 levels, while crop burning within MT has a minimal impact (less than 6%). In the BMR, PM_2.5 is strongly impacted by sources outside the model domain. Overall, industrial and transportation emissions are the most impactful anthropogenic sources. We further estimate the total health burden, associated with long-term PM_2.5 exposure during the haze season contributes to 46% of this PM_2.5 health burden in MT in 2019, 66% in NT, and 37% in the BMR. These findings suggest that reducing biomass burning within MT and from in-domain foreign countries during February–April could reduce the annual health burden in MT by up to 20%.

Air quality has improved in recent decades across most of the United States. However, decreases in pollution have not been uniform, potentially exacerbating inequalities in air pollution exposure by race and ethnicity. These inequalities exist, in part, because of spatial differences in source(s), for example, power plants or roadways. Determining which sources are driving inequality across racial and ethnic groups is critical to determining which policies (e.g., targeting power plant vs. vehicle emissions) would reduce inequalities. Our study determines which pollutant sources should be decreased to address inequalities in four pollutants (NO_x, SO₂, VOCs, and PM_2.5) in the Commonwealth of Virginia. We derived emissions from eight source categories for 134 Virginia counties from the National Emissions Inventory and the MOtor Vehicle Emissions Simulator mobile source emissions model. We used race and ethnicity data from the American Community Survey from 2011 to 2020. We applied the Atkinson Index to obtain a single summary of inequality for each source-pollutant pair (e.g., NO_x from electricity generation) across all race and ethnic groups. Most source category emissions were unequally distributed for at least once pollutant. Compared to other sources, electricity generation resulted in the largest inequalities across pollutants. Mobile sources increased in inequality from 2011 to 2020 even as emissions decreased. These results show the importance of identifying sources that contribute most to inequalities when developing policies to promote environmental justice.

The mosquito-borne disease dengue is sensitive to climate, in part because of the influence climate has on breeding habitats of dengue's Aedes mosquito vectors. Dengue risk assessment models currently leverage climate-dengue statistical associations, yet what remain understudied are the mechanistic pathways that yield different statistical relationships in different locations. We hypothesize that elucidating the mechanisms by which spatiotemporal variability in climate influences dengue incidence will improve dengue dynamics predictions across climatically distinct locations and beyond dengue's well-known seasonal cycles. We test this hypothesis by investigating a key pathway in the climate-dengue process chain: climate impacts on Aedes breeding habitats. We have implemented a mechanistic modeling pipeline that simulates climatic influence on habitat water dynamics and thereby on relative population size of the vector. We use this modeling pipeline, driven by meteorological data, to simulate monthly Aedes populations for three climatically distinct cities in Sri Lanka. We find that simulated vector abundance is plausibly associated with climate conditions and that climate drivers of vector abundance vary among locations. Moreover, tercile-tercile comparisons of dengue incidence against model variables indicate that risk assessments based on predicted vector abundance perform similarly to those based on meteorology alone—the signal of weather variability and its relationship to dengue propagates through the modeling pipeline. These results justify future testing of this modeling pipeline within a dengue risk assessment framework, where its process-based structure may be leveraged to guide proactive dengue control efforts in high-risk years and to simulate impacts of future climate conditions on dengue dynamics.

The widespread concern surrounding the enhanced spillover risk of infectious diseases due to dramatic global land use changes has sparked significant discussion. However, the specific implications of these changes on scrub typhus, a vector-borne infectious disease facing increasing incidence and substantial expansion, remain unclear. Here, we constructed a comprehensive landscape fragmentation index (LFI), which reflects the interaction between human activities and natural habitats. Then we utilized a generalized additive model (GAM) to estimate the comprehensive and segmented impacts of LFI on scrub typhus incidence in China, grouping the results by year, land use type and fragmentation level. Additionally, we projected changes in such impacts under four shared socioeconomic pathways (SSPs), including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Key results were: (a) The LFI exhibited a nonlinear positive correlation with scrub typhus incidence. Each 0.1 increase in the LFI was associated with a relative risk of 1.20 (95% CI:1.19–1.21) for scrub typhus. Notably, at higher fragmentation levels, scrub typhus incidence tended to decrease. (b) Forest fragmentation had the most significant impact on scrub typhus, followed by cropland fragmentation, whereas construction land fragmentation was negatively associated. (c) The future areas of elevated scrub typhus risk varied among the SSPs, but they were mainly concentrated at the interface between urban expansion and natural habitats. Our results indicate that human interference with the natural ecosystem is a critical factor for the incidence of scrub typhus. These findings are conducive to promoting ecological protection and the prevention and control of scrub typhus.

Most of the United States (US) population resides in cities, where they are subjected to the urban heat island effect. In this study, we develop a method to estimate hourly air temperatures at $��0.01�°�\times �0.01�°�$ resolution, improving exposure assessment of US population when compared to existing gridded products. We use an extensive network of personal weather stations to capture the intra-urban variability. The uncertainty associated with this crowdsourced data set is addressed through a spatiotemporal Bayesian model implemented with the Integrated Nested Laplace Approximation-Stochastic Partial Differential Equation approach. We evaluate the model on Philadelphia (PA), New York City (NY), Phoenix (AZ), and the Triangle area (NC). These case studies span different climatic zones and urban landscapes. They cover several meteorological events including a deadly heatwave in Phoenix and a snowstorm hitting part of the US in winter 2021. We obtain an overall root mean square error of $��1.06�°�C�$, demonstrating the versatility of our model, and its applicability across various regions in the US. The high granularity of our model allows for the precise identification of hotspots that were previously undetected with daymet and gridMET products. Using the data generated by our method, we show that neighborhoods with high population concentration are more likely to experience elevated temperatures and prolonged hot nights, thus encouraging the use of our model for further epidemiological investigations on the impact of heat or cold stress on human health.

Dengue fever is a mosquito-borne viral disease rapidly creating a significant global public health burden, particularly in urban areas of tropical and sub-tropical countries. Hydroclimatic variables, particularly local temperature, precipitation, relative humidity, and large-scale climate teleconnections, can influence the prevalence of dengue by impacting vector population development, viral replication, and human-mosquito interactions. Leveraging predictions of these variables at lead times of weeks to months can facilitate early warning system preparatory actions such as allocating funding, acquisition and preparation of medical supplies, or implementation of vector control strategies. We develop hydroclimate-based statistical forecast models for dengue virus (DENV) at 1-, 3-, and 6- month lead times for four cities across Colombia (Cali, Cúcuta, Medellín, and Leticia) and compare with standard autoregressive models conditioned on dengue case counts. Our results indicate that (a) hydroclimate-based models are particularly skillful at 3- and 6- month lead times when autoregressive models often fail, (b) sea surface temperatures are the most skillful predictor at 3- and 6- month leads and (c) application of hydroclimate models are most beneficial when average DENV incidence is low, autoregressive relationships are weak, but outbreaks may still occur.

Satellite observations provide continuous and global coverage observations of air pollutants, widely used to inform health impacts and air pollution disparities. Linking satellite retrievals with socioeconomic or health data involves matching the irregularly shaped satellite observations with administrative units. Here, we develop a physics-based approach to spatially oversample nitrogen dioxide (NO₂) retrievals from TROPOspheric Monitoring Instrument (TROPOMI) directly to United States (US) neighborhoods (i.e., block groups). The physics-based oversampling approach considers each satellite pixel as a sensitivity distribution, meaning that satellite instruments are more sensitive to the neighborhoods at the center than at the edge of the observations. We show that directly oversampling satellite observations to administrative shapes is a more accurate and computationally efficient approach than the commonly used gridding approaches, and it is advantageous for shorter temporal windows. Combining the newly developed NO₂ data set with demographic data, we find widespread racial/ethnic and income-related NO₂ disparities across the US. NO₂ disparities are even more pronounced during the most polluted days, suggesting greater acute health effects for overburdened communities. We expect that the resolution-adaptive, neighborhood-level, and GIS-compatible NO₂ data set would lower barriers of the public to access and interpret satellite observations, facilitating the actionable applications of satellite observations.