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At least 19 recordsLinked to original sources
USGS2005

Workgroup report: Drinking-water nitrate and health - Recent findings and research needs

Human alteration of the nitrogen cycle has resulted in steadily accumulating nitrate in our water resources. The U.S. maximum contaminant level and World Health Organization guidelines for nitrate in drinking water were promulgated to protect infants from developing methemoglobinemia, an acute condition. Some scientists have recently suggested that the regulatory limit for nitrate is overly conservative; however, they have not thoroughly considered chronic health outcomes. In August 2004, a symposium on drinking-water nitrate and health was held at the International Society for Environmental Epidemiology meeting to evaluate nitrate exposures and associated health effects in relation to the current regulatory limit. The contribution of drinking-water nitrate toward endogenous formation of N-nitroso compounds was evaluated with a focus toward identifying subpopulations with increased rates of nitrosation. Adverse health effects may be the result of a complex interaction of the amount of nitrate ingested, the concomitant ingestion of nitrosation cofactors and precursors, and specific medical conditions that increase nitrosation. Workshop participants concluded that more experimental studies are needed and that a particularly fruitful approach may be to conduct epidemiologic studies among susceptible subgroups with increased endogenous nitrosation. The few epidemiologic studies that have evaluated intake of nitrosation precursors and/or nitrosation inhibitors have observed elevated risks for colon cancer and neural tube defects associated with drinking-water nitrate concentrations below the regulatory limit. The role of drinking-water nitrate exposure as a risk factor for specific cancers, reproductive outcomes, and other chronic health effects must be studied more thoroughly before changes to the regulatory level for nitrate in drinking water can be considered.

Environmental Health Perspectives↗
USGS2004

An automated approach to mapping corn from Landsat imagery

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

Computers and Electronics in Agriculture↗
USGS2021

Evaluation of Indoor PM2.5 concentrations in a Native American community: A pilot study

Background Indoor air pollution is associated with adverse health effects; however, few studies exist studying indoor air pollution on the Navajo Nation in the southwest U.S., a community with high rates of respiratory disease. Methods Indoor PM 2.5 concentration was evaluated in 26 homes on the Navajo Nation using real-time PM 2.5 monitors. Household risk factors and daily activities were evaluated with three metrics of indoor PM 2.5 : time-weighted average (TWA), 90th percentile of concentration, and daily minutes exceeding 100 μg/m 3 . A questionnaire and recall sheet were used to record baseline household characteristics and daily activities. Results The median TWA, 90th percentile, and daily minutes exceeding 100 μg/m 3 were 7.9 μg/m 3 , 14.0 μg/m 3 , and 17 min, respectively. TWAs tended to be higher in autumn and in houses that used fuel the previous day. Other characteristics associated with elevated PM exposure in all metrics included overcrowded houses, nonmobile houses, and houses with current smokers, pets, and longer cooking time. Conclusions Some residents of the Navajo Nation have higher risk of exposure to indoor air pollution by Environmental Protection Agency (EPA) standards. Efforts to identify the causes and associations with adverse health effects are needed to ensure that exposure to risks and possible health impacts are mitigated.

Journal of Exposure Science and Environmental Epid↗
USGS2023

Contribution of arsenic and uranium in private wells and community water systems to urinary biomarkers in US adults: The Strong Heart Study and the Multi-Ethnic Study of Atherosclerosis

Background Chronic exposure to inorganic arsenic (As) and uranium (U) in the United States (US) occurs from unregulated private wells and federally regulated community water systems (CWSs). The contribution of water to total exposure is assumed to be low when water As and U concentrations are low. Objective We examined the contribution of water As and U to urinary biomarkers in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially/ethnically diverse urban U.S. communities. Methods We assigned residential zip code-level estimates in CWSs (µg/L) and private wells (90th percentile probability of As >10 µg/L) to up to 1485 and 6722 participants with dietary information and urinary biomarkers in the SHFS (2001–2003) and MESA (2000–2002; 2010–2011), respectively. Urine As was estimated as the sum of inorganic and methylated species, and urine U was total uranium. We used linear mixed-effects models to account for participant clustering and removed the effect of dietary sources via regression adjustment. Results The median (interquartile range) urine As was 5.32 (3.29, 8.53) and 6.32 (3.34, 12.48) µg/L for SHFS and MESA, respectively, and urine U was 0.037 (0.014, 0.071) and 0.007 (0.003, 0.018) µg/L. In a meta-analysis across both studies, urine As was 11% (95% CI: 3, 20%) higher and urine U was 35% (5, 73%) higher per twofold higher CWS As and U, respectively. In the SHFS, zip-code level factors such as private well and CWS As contributed 46% of variation in urine As, while in MESA, zip-code level factors, e.g., CWS As and U, contribute 30 and 49% of variation in urine As and U, respectively. Impact statement We found that water from unregulated private wells and regulated CWSs is a major contributor to urinary As and U (an estimated measure of internal dose) in both rural, American Indian populations and urban, racially/ethnically diverse populations nationwide, even at levels below the current regulatory standard. Our findings indicate that additional drinking water interventions, regulations, and policies can have a major impact on reducing total exposures to As and U, which are linked to adverse health effects even at low levels.

Journal of Exposure Science and Environmental Epid↗
USGS2026

Cyanobacterial bloom occurrence and emergency department visits for asthma or wheeze, Wisconsin, 2017–2019

Background: Cyanobacterial harmful algal blooms (cyanoHABs) pose risks to human and animal health. Methods: We investigated the relationship between cyanoHABs and asthma or wheeze-related emergency department (ED) visits near three Wisconsin cities (Green Bay, Madison, and Oshkosh) during 2017–2019. CyanoHAB exposure was approximated using the Cyanobacterial Assessment Network remotely sensed satellite indicator of cyanobacterial biomass, a chlorophyl algorithm (Chl BS ) aggregated by water-adjacent ZIP Code Tabulation Areas (ZCTA), and distance weighted from the nearest waterbody. Weekly counts of ED visits for asthma or wheeze were aggregated by ZCTA. Poisson generalized linear models estimated the association between the weekly number of ED visits and weekly Chl BS, adjusting for maximum temperature, dewpoint, fine particulate matter (PM 2.5 ), month, and correlation within ZCTA. Results: During 2017–2019, 7,057 ED visits for asthma or wheeze occurred in the study area (42 ZCTAs). Peaks in Chl BS occurred between June and October, with higher values in Lake Winnebago and Lake Mendota compared to Green Bay. Chl BS was not associated with ED visits for asthma or wheeze (adjusted rate ratio = 1.00, 95% confidence interval = 0.99, 1.00), and the presence of onshore winds did not change this result. Monthly aggregations of ED visits and Chl BS showed a monotonic trend between increasing Chl BS and ED visits during July–September. Conclusion: This study demonstrates the utility of remote sensing data in environmental health research. Future studies could explore individual-level exposure and outcomes to refine health risks associated with cyanoHABs.

Wisconsin↗
USGS2009

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Journal of Exposure Science and Environmental Epid↗
USGS2011

Estimating water supply arsenic levels in the New England bladder cancer study

Background: Ingestion of inorganic arsenic in drinking water is recognized as a cause of bladder cancer when levels are relatively high (≥ 150 μg/L). The epidemiologic evidence is less clear at the low-to-moderate concentrations typically observed in the United States. Accurate retrospective exposure assessment over a long time period is a major challenge in conducting epidemiologic studies of environmental factors and diseases with long latency, such as cancer. Objective: We estimated arsenic concentrations in the water supplies of 2,611 participants in a population-based case–control study in northern New England. Methods: Estimates covered the lifetimes of most study participants and were based on a combination of arsenic measurements at the homes of the participants and statistical modeling of arsenic concentrations in the water supply of both past and current homes. We assigned a residential water supply arsenic concentration for 165,138 (95%) of the total 173,361 lifetime exposure years (EYs) and a workplace water supply arsenic level for 85,195 EYs (86% of reported occupational years). Results: Three methods accounted for 93% of the residential estimates of arsenic concentration: direct measurement of water samples (27%; median, 0.3 μg/L; range, 0.1–11.5), statistical models of water utility measurement data (49%; median, 0.4 μg/L; range, 0.3–3.3), and statistical models of arsenic concentrations in wells using aquifers in New England (17%; median, 1.6 μg/L; range, 0.6–22.4). Conclusions: We used a different validation procedure for each of the three methods, and found our estimated levels to be comparable with available measured concentrations. This methodology allowed us to calculate potential drinking water exposure over long periods.

Environmental Health Perspectives↗
USGS2019

Simultaneous autoregressive (SAR) model

Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.

Book chapter↗
USGS2016

U.S. Geological Survey science strategy for highly pathogenic avian influenza in wildlife and the environment (2016–2020)

Introduction Through the Science Strategy for Highly Pathogenic Avian Influenza (HPAI) in Wildlife and the Environment, the USGS will assess avian influenza (AI) dynamics in an ecological context to inform decisions made by resource managers and policymakers from the local to national level. Through collection of unbiased scientific information on the ecology of AI viruses and wildlife hosts in a changing world, the U.S. Geological Survey (USGS) will enhance the development of AI forecasting tools and ensure this information is integrated with a quality decision process for managing HPAI. The overall goal of this USGS Science Strategy for HPAI in Wildlife and the Environment goes beyond document­ing the occurrence and distribution of AI viruses in wild birds. The USGS aims to understand the epidemiological processes and environmental factors that influence HPAI distribution and describe the mechanisms of transmission between wild birds and poultry. USGS scientists developed a conceptual model describing the process linking HPAI dispersal in wild waterfowl to the outbreaks in poul­try. This strategy focuses on five long-term science goals, which include: Science Goal 1—Augment the National HPAI Surveillance Plan; Science Goal 2—Determine mechanisms of HPAI disease spread in wildlife and the environment; Science Goal 3—Characterize HPAI viruses circulating in wildlife; Science Goal 4—Understand implications of avian ecol­ogy on HPAI spread; and Science Goal 5—Develop HPAI forecasting and decision-making tools. These goals will help define and describe the processes outlined in the conceptual model with the ultimate goal of facilitating biosecurity and minimizing transfer of diseases across the wildlife-poultry interface. The first four science goals are focused on scientific discovery and the fifth goal is application-based. Decision analyses in the fifth goal will guide prioritization of proposed actions in the first four goals.

Open-File Report↗
USGS2022

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement-pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

Ecology Letters↗
USGS1991

Environmental contaminants and the reproductive success of lake trout in the Great Lakes: An epidemiological approach

Epidemiological criteria were used to examine the influence of environmental contamination on reproductive success of lake trout ( Salvelinus namaycush ,) in the Laurentian Great Lakes. Most of the information was obtained from lake trout eggs collected in southeastern Lake Michigan and reared in the laboratory. Two separate end points that measure reproductive success—egg hatchability and fry survival‐were used in the evaluation. Strong evidence for maternally derived polychlorinated biphenyls causing reduced egg hatchability were observed for the time order, strength of association, and coherence criteria. Equally strong evidence for organic environmental contaminants, also of maternal origin, causing a swim‐up fry mortality syndrome were presented for the strength of association, specificity, replication, and coherence criteria. The epidemiological approach for demonstrating cause‐and‐effect relations was useful because of the difficulty in demonstrating definite proof of causality between specific environmental contaminants and reproductive dysfunction in feral fish.

Journal of Toxicology and Environmental Health↗
USGS1970

Geochemical Survey of Missouri: Plans and progress for third six-month period (July-December 1970)

This third six-month progress report to the Environmental Health Center of the University of Missouri marks the midway point in our studies of the environmental geochemistry of the State. Over 4,000 samples of rocks, soils, vegetation, and groundwater have been collected from throughout the State; most of these are currently in the Geological Survey laboratories under various stages of preparation and analysis. Some preliminary results have been reported in the two previous progress reports, and others are presented here. It is expected that the major part of the analytical task will be completed in the next 6 to 12 months, however, and forthcoming reports should include information of direct application to the epidemiological investigations of the Environmental Health Center. The present report is intended to inform those concerned of our progress, our plans for the forthcoming final field season of the investigation, and to offer some additional preliminary results. Of specific interest to the Environmental Health Center will be the maps showing localities throughout the State where samples, or groups of samples, of rocks, soils, vegetation, and water have been collected so far; the sampling plans have been discussed briefly in the previous progress reports, and will be described in detail, of course, in forthcoming reports along with analyses and interpretations of the laboratory data.

Missouri↗
USGS2020

Spatiotemporal modeling of dengue fever risk in Puerto Rico

Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.

Puerto Rico↗
USGS2022

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Biological Reviews↗
USGS2020

Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms

The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.

Baja California, British Columbia, California, Ore↗
USGS2024

Predicting the odds of chronic wasting disease with Habitat Risk software

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer ( Odocoileus virginianus ) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

Tennessee↗
USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗