ResearchPad - geohealth https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Exploring the Paradox of Increased Global Health and Degraded Global Environment: How Much Borrowed Time Is Humanity Living on?]]> https://www.researchpad.co/article/Naac92970-d402-4070-a3c1-7b4596659e95 We have overlooked the apparent paradox of increasing global health status and declining ecological and environmental qualityResource banks, and their largely undervalued nature, hold the key to understanding the global health‐environment balanceMuch more work needs to focus on ripple effects from exploitation of nonrenewable, and nonreplaceable resources

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<![CDATA[Stringent Emission Control Policies Can Provide Large Improvements in Air Quality and Public Health in India]]> https://www.researchpad.co/article/Nb1098b2b-f8d1-46e9-80dc-86448468f6a3 Air pollution is a major risk factor for human health in IndiaPopulation aging and growth will increase the disease burden due to exposure to particulate air pollution even under no emission changeStringent emission control reduces mortality rate in 2050 below 2015 levels although total premature mortality increases

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<![CDATA[New Approaches to Identifying and Reducing the Global Burden of Disease From Pollution]]> https://www.researchpad.co/article/Nbf7723dd-5647-4f8e-be7f-e9600ebe8e30

Abstract

Pollution from multiple sources causes significant disease and death worldwide. Some sources are legacy, such as heavy metals accumulated in soils, and some are current, such as particulate matter. Because the global burden of disease from pollution is so high, it is important to identify legacy and current sources and to develop and implement effective techniques to reduce human exposure. But many limitations exist in our understanding of the distribution and transport processes of pollutants themselves, as well as the complicated overprint of human behavior and susceptibility.

New approaches are being developed to identify and eliminate pollution in multiple environments. Community‐scale detection of geogenic arsenic and fluoride in Bangladesh is helping to map the distribution of these harmful elements in drinking water. Biosensors such as bees and their honey are being used to measure heavy metal contamination in cities such as Vancouver and Sydney. Drone‐based remote sensors are being used to map metal hot spots in soils from former mining regions in Zambia and Mozambique. The explosion of low‐cost air monitors has allowed researchers to build dense air quality sensing networks to capture ephemeral and local releases of harmful materials, building on other developments in personal exposure sensing. And citizen science is helping communities without adequate resources measure their own environments and in this way gain agency in controlling local pollution exposure sources and/or alerting authorities to environmental hazards. The future of GeoHealth will depend on building on these developments and others to protect a growing population from multiple pollution exposure risks.

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<![CDATA[Modeling the Relationship of Groundwater Salinity to Neonatal and Infant Mortality From the Bangladesh Demographic Health Survey 2000 to 2014]]> https://www.researchpad.co/article/N282f9af1-03b6-46af-ab8d-637967f089d1

Abstract

We evaluated the relationship of drinking water salinity to neonatal and infant mortality using Bangladesh Demographic Health Surveys of 2000, 2004, 2007, 2011, and 2014. Point data of groundwater electrical conductivity (EC)— a measure of salinity—were collated from the Bangladesh Water Development Board and digitizing salinity contour map. Data for groundwater dissolved elements (sodium, calcium, magnesium, and potassium) data came from a national hydrochemistry survey in Bangladesh. Point EC and dissolved minerals data were then interpolated over entire Bangladesh and extracted to each cluster location, the primary sampling unit of Bangladesh Demographic Health Surveys. We used restricted cubic splines and survey design‐specific logistic regression models to determine the relationship of water salinity to neonatal and infant mortality. A U‐shaped association between drinking water salinity and neonatal and infant mortality was found, suggesting higher mortality when salinity was very low and high. Compared to mildly saline (EC ≥0.7 and < 2 mS/cm) water drinkers, freshwater (EC < 0.7 mS/cm) drinkers had 1.37 (95% CI: 1.01, 1.84) times higher neonatal mortality and 1.43 (95% CI: 1.08, 1.89) times higher infant mortality. Compared to mildly saline water drinkers, severe‐saline (EC ≥10 mS/cm) water drinkers had 1.77 (95% CI: 1.17, 2.68) times higher neonatal mortality and 1.93 (95% CI: 1.35, 2.76) times higher infant mortality. We found that mild‐salinity water had a high concentration of calcium and magnesium, whereas severe‐salinity water had a high concentration of sodium. Freshwater had the least concentrations of salubrious calcium and magnesium.

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<![CDATA[Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America]]> https://www.researchpad.co/article/N1df86112-92c0-47c4-bc7e-b31b90b1d872

Abstract

Understanding the geographic distribution of mosquito‐borne disease and mapping disease risk are important for prevention and control efforts. Mosquito‐borne viruses (arboviruses), such as West Nile virus (WNV), are highly dependent on environmental conditions. Therefore, the use of environmental data can help in making spatial predictions of disease distribution. We used geocoded human case data for 2004–2017 and population‐weighted control points in combination with multiple geospatial environmental data sets to assess the environmental drivers of WNV cases and to map relative infection risk in South Dakota, USA. We compared the effectiveness of (1) land cover and physiography data, (2) climate data, and (3) spectral data for mapping the risk of WNV in South Dakota. A final model combining all data sets was used to predict spatial patterns of disease transmission and characterize the associations between environmental factors and WNV risk. We used a boosted regression tree model to identify the most important variables driving WNV risk and generated risk maps by applying this model across the entire state. We found that combining multiple sources of environmental data resulted in the most accurate predictions. Elevation, late‐season humidity, and early‐season surface moisture were the most important predictors of disease distribution. Indices that quantified interannual variability of climatic conditions and land surface moisture were better predictors than interannual means. We suggest that combining measures of interannual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk.

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