Geohealth最新文献

Since its introduction to North America in 1999, West Nile virus (WNV) has become the most widespread mosquito-borne disease in the United States. Climatic conditions significantly influence transmission dynamics. While temperature, precipitation, and humidity are known to affect mosquito populations and virus replication, wind speed is often neglected in transmission models despite its potential to alter mosquito behavior and facilitate mosquito dispersal. This study incorporates wind speed into climate-based WNV models to compare its effects in Louisiana and South Dakota, two U.S. states with contrasting climates, land cover, and vector and host species. From 2004 to 2022, we analyzed weekly WNV human case data in relation to daily meteorological data. The relationships were modeled using logistic regression with distributed lag effects. Incorporating wind speed consistently enhanced the fit of climate-based models across both states, as evidenced by the Akaike Information Criterion. Higher-than-normal wind speeds were associated with decreased WNV cases over specific lag periods, suggesting that increased wind speed may inhibit mosquito activity and reduce virus transmission. Differences in how temperature and moisture-related variables influenced the two regions highlight the importance of considering regional climatic contexts. These findings demonstrate that incorporating wind speed can enhance meteorological models of mosquito-borne diseases and reinforce the importance of considering a broader range of climatic factors beyond temperature and precipitation. Understanding these regional variations is essential for predicting local climatic influences on disease transmission, which can support the implementation of more targeted and effective public health strategies.

Phenological shifts due to climate change have been extensively studied in plants and animals. Yet, the responses of fungal spores—organisms important to ecosystems and major airborne allergens—remain understudied. This knowledge gap limits our understanding of their ecological and public health implications. To address this, we analyzed a long-term (2003–2022), large-scale (the continental US) data set of airborne fungal spores collected by the US National Allergy Bureau. We first pre-processed the spore data by gap-filling and smoothing. Afterward, we extracted 10 metrics describing the phenology (e.g., start and end of season) and intensity (e.g., peak concentration and integral) of fungal spore seasons. These metrics were derived using two complementary but not mutually exclusive approaches—ecological and public health approaches, defined as percentiles of total spore concentration and allergenic thresholds of spore concentration, respectively. Using linear mixed-effects models, we quantified annual shifts in these metrics across the continental US. We revealed a significant advancement in the onset of the spore seasons defined in both ecological (11 days, 95% confidence interval: 0.4–23 days) and public health (22 days, 6–38 days) approaches over two decades. Meanwhile, total spore concentrations in an annual cycle and in a spore allergy season tended to decrease over time. The earlier start of the spore season was significantly correlated with climatic variables, such as warmer temperatures and altered precipitations. Overall, our findings suggest possible climate-driven advanced fungal spore seasons, highlighting the importance of climate change mitigation and adaptation in public health decision-making.

Extreme heat is worsening due to climate change, and, in combination with increasing urban growth, is an escalating public health concern. In the Arabian Peninsula, the wet-bulb temperature is projected to surpass theoretical human tolerance limits during the 21st century. Yet, heat research in the region has generally not focused on health impacts, and it is unclear how epidemiologic literature has investigated this. We performed a scoping review to examine the existing literature that assessed the relationship between extreme heat and health outcomes in the Arabian Peninsula, collecting papers published from 2010 to 2024 from three databases. We identified and extracted detailed information from a limited number of studies (n = 12). The greatest number of studies were conducted in Kuwait (n = 8), with others in Saudi Arabia (n = 4), and Qatar, Oman, Yemen, and the United Arab Emirates (n = 1 each). Average temperature was the most used exposure (n = 9) assessed at the daily level (n = 10), using one or several meteorological stations (n = 9) from a single city (n = 8). The outcome was predominantly daily-level (n = 10) mortality (n = 9) assessed at an ecological scale (n = 10) as opposed to the individual scale. While most studies included confounders (n = 10), their selection was not always consistent with best practices. Most papers did not assess effect modification (n = 8), and none investigated modification by land-cover and land-use change on the heat-health relationship. We provide future research recommendations based on our findings. Additional studies are critical to better understand the heat-health relationship in the Arabian Peninsula, which can aid intervention implementation.

Tropical and sub-tropical regions mostly provide favorable conditions for the spread of vector-borne diseases especially those transmitted by mosquito vectors. Viral disease outbreaks such as dengue, chikungunya, Japanese encephalitis; and parasitic diseases such as malaria, and filariasis; are common health problems caused by mosquitos. According to the World Health Organization (WHO), globally around 40% of people are at high risk of mosquito-borne diseases (MBD) (WHO Fact-sheets, 2020, https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases). In the present study, Remote Sensing methods integrated with Geographic Information System (GIS) have been used to predict the risk zones for MBD in Cuttack, a district of Odisha, the eastern state of India. The findings of this study could be utilized to develop and implement MBD control and prevention strategies in identified high-risk areas. Under this study, the Landsat-8 multispectral temporal images from 2018 to 2021 were used to identify and demarcate the water-logged areas and sites favorable for mosquito breeding. The goal is to identify risk zones for MBD using different indices such as Normalized Difference Water Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index, and Land Surface Temperature by evaluating water, moisture, vegetation, and temperature parameters. Applying Arc GIS software analysis models,we found 11,730.07 Ha. as a high-risk zone, 28,053.99 Ha. as a medium-risk zone, and 12,669.69 Ha. as a low-risk zone. This study has the potential to enable informed decision-making and proactive mosquito-borne disease prevention.

Droughts, historically recognized as drivers of societal transformation, have been implicated in the emergence of infectious diseases. While existing research has concentrated on the impact of climate change on infectious diseases outbreaks in modern, industrialized, and urban settings, there is a dearth of epidemiological evidence regarding the historical interplay between drought and disease. Here, we analyze the 1585–1590 extreme drought in Ming Dynasty China to investigate the concurrent development of drought and infectious diseases, as well as the temporal and spatial effects of drought on disease outbreaks. The findings reveal a positive correlation between drought and infectious diseases in both temporal and spatial dimensions, with famine identified as a critical intermediate factor. Drought's influence on famine and disease is both immediate and delayed, with the most significant effects occurring within the same year. Additionally, a north-to-south pattern in the occurrence of drought and infectious diseases is observed, with northern droughts more likely to precipitate disease outbreaks. These insights offer valuable perspectives for future strategies.

Climate change is creating more frequent extreme weather events, but impacts on violence are not well understood. We explored associations of extreme weather with firearm violence and child maltreatment in Wayne County, Michigan. To understand contextual influences, we estimated models using data from before (2018–2019), during (March 2020–March 2021), and after (2022–2023) the onset of the COVID-19 pandemic. In both 2018–2019 and 2022–2023, firearm violence was lower in the first couple days after extreme weather compared to no prior extreme weather (relative risks [RR]: 0.44–0.54; 95% confidence intervals [CI]: 0.21–0.37, 0.79–0.90), with the influence waning over the following week. We did not observe any reduction, however, during the first year of the COVID-19 pandemic. Associations differed for child maltreatment. In 2018–2019, we observed no change in maltreatment in the days following extreme weather. Yet, during the first year of the COVID-19 pandemic, child maltreatment was higher over the 10 days following extreme weather (RR: 1.24; 95% CI: 0.97, 1.58). In 2022–2023, child maltreatment was lower following extreme weather events, but this finding is likely an artifact of reduced reporting after extreme weather during this period. In Wayne County, extreme weather immediately reduced firearm violence, with effects waning as people likely resumed regular activities. The first year of the COVID-19 pandemic suspended this influence, as extreme weather may not have changed daily activities beyond pandemic-related disruptions. For child maltreatment, however, extreme weather created accumulating risk over several days when it disrupted an already stressed environment.

The contribution of occupational exposures to the extent of cumulative environmental impacts, and their implications for environmental justice (EJ), have not been investigated. We (a) characterized communities with cumulatively high occupational and environmental exposures, (b) examined whether marginalized, historically redlined neighborhoods were disproportionately affected by these exposures, and (c) evaluated the implications of failing to consider workplace exposures in EJ screening tools in Michigan. At the census tract-level, we combined occupational exposure estimates of six common workplace hazards, environmental exposures from EJScreen and the National Transportation Noise Map, demographic information from the American Community Survey, and redlining information from the 1930s Home Owners' Loan Corporation maps to test the first two objectives using supervised and unsupervised statistical methods. The last objective incorporated the occupational indicators into the Michigan-specific EJ screening tool (MiEJScreen) to test the third objective. Among 2,772 Michigan census tracts, 738 (27%) had cumulatively high occupational and environmental exposures, primarily in urban areas. Tracts with >90% (compared to <10%) of racial and ethnic minority individuals had 2.31 (95% CI: 1.78–3.03) times higher odds of cumulatively high exposures. A simultaneous increase to the 90th percentile (relative to the 50th) in all 13 occupational and environmental exposures was associated with 2.47 (95% CI: 1.20–5.36) times higher odds of a tract having been historically redlined. Not incorporating occupational exposures into the MiEJScreen would overlook 90 census tracts with cumulatively high environmental and occupational impacts, affecting around 255,000 individuals. Ignoring occupational exposures in cumulative environmental impact assessments may overlook important EJ hotspots.

The spread of malaria is a major health burden, which affects many people in Africa, depends on climate but also socio-economic conditions. Thus, it is important to gauge the impact of anthropogenic global warming on malaria and attribute anthropogenic causes. Here we compute the Time Of Emergence of vector density and of the entomological inoculation rate (EIR) in the SSP3-7.0 scenario using 50 bias-corrected members of Community Earth System Model version 2 Large Ensemble simulations. This reveals that vector density, which depends on climate conditions, and EIR, which depends on both climate and population density, will rise significantly and permanently above the pre-industrial background variability due to anthropogenic causes in Africa. Both the vector density and EIR have areas, mainly in central Africa, where anthropogenic causes have already significantly changed, and many more areas will experience anthropogenic caused changes in the period 2030–2050 and toward the end of this century. Our simulations also show clear evidence that extremes of vector density and EIR increase in the future by almost 100%, suggesting that major malaria epidemic outbreaks will become much more likely. We also perform simulations with constant population and with no global warming which partly reveal underlying malaria dynamics. Our results highlight the need to prepare for an expansion and intensification of the malaria burden if no health interventions are being taken.

Globally gridded precipitation products (GGPPs) are commonly used in impact assessments as substitutes for weather station data, each with unique strengths and limitations. Reanalysis products are among the most widely used for driving impact models, evaluating climate models, or bias-correcting and downscaling model outputs to generate climate change projections. However, they are often outperformed in accuracy by other GGPPs, particularly in tropical regions, including areas of the Global South. Therefore, we assessed the utility and suitability of GGPPs for climate and health research by examining how differences and uncertainties in these products affect area-level precipitation estimates, often used in health studies when epidemiological data are linked to administrative units. We compared reanalysis (ERA5/-Land) with satellite-based (CHIRPS, PERSIANN-CDR) and interpolated gauge-based products (CRUTS, GPCC), each a viable candidate to serve as reference climatology in climate change impact assessments. We focused on seasonal patterns, disease-related bioclimatic variables, and climate change-relevant indices, such as the number of wet or dry periods. Our findings revealed substantial variation in the accuracy of local precipitation estimates across GGPPs, with differences in maximum pixel precipitation values exceeding 75% between ERA5-Land and CHIRPS. These differences in GGPPs translated into area-level precipitation and, consequently, in vector carrying capacity estimates, demonstrating their impact on health assessments. Our analysis focused on Brazil and Colombia, two diverse countries differing for example, in orography, climate, and size. Each product was evaluated against national station data. Our results indicate that estimating tropical precipitation is particularly challenging for reanalysis, while CHIRPS demonstrated the best overall performance.

The objective of this scoping review is to explore the systemic barriers that impact health inequities among vulnerable populations (e.g., racial/ethnic and gender groups, people with disabilities, refugees, immigrants, elders, young children, agricultural and fishery workers, and low-income individuals) when facing climate change stressors. We conducted an extensive review using nine search engines, which yielded 21 publications that focused on the health outcomes and barriers on the topic of climate change among vulnerable populations. Our findings indicated that poverty is the largest challenge preventing people from adequate health access and achieving positive outcomes, particularly for vulnerable populations globally. In addition, institutional and systemic barriers also differ based on regional differences, which suggests that health inequities are context dependent. Our scoping review has implications for (a) enhancing the effectiveness of climate change mitigation strategies and (b) addressing the healthcare barriers of vulnerable populations based on country-specific challenges.