Proximity to Crops and Residential Exposure to Agricultural Herbicides in Iowa

Ward, Mary H.; Lubin, Jay H.; Giglierano, James D.; Colt, Joanne S.; Wolter, Calvin F.; Bekiroğlu, Nural; Camann, David; Hartge, Patricia; Nuckols, John R.

doi:10.1289/ehp.8770

Cited by 159 publications

(155 citation statements)

References 21 publications

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“…Other ambiguities exist when using typical geocoding procedures, especially in rural areas. Actual housing structures may lie up to 200 ft away from a road or be obstructed by a crop field or vineyard (Rull et al 2006;Ward et al 2006); the geocoding procedure, however, cannot automatically detect this and places the housing structure on the road or somewhere near the crop field or vineyard. This results in misclassification error.…”

Section: Discussionmentioning

confidence: 99%

“…Proximity to agricultural crops is often used to assess exposure to herbicides, fertilizers, and pesticides (Brody et al, 2004;Lu et al, 2006;Meyer et al, 2006;Ward et al, 2006). GIS and satellite imagery have been applied repeatedly to create historical crop maps that support estimation of exposures to agricultural pesticides based on residential proximity to different crop types (Ward et al, 2000;Xiang et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland

Meliker

Jacquez

2007

J Expo Sci Environ Epidemiol

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Exposure misclassification is a major concern in epidemiologic studies. The potential for misclassification becomes even more problematic when participants are asked to recall historical information. Yet, historical information is important in cancer studies, where latency is long and causative exposures may have occurred years or even decades prior to diagnosis. Even though self-reported proximity to farmland is a commonly used exposure measure, the accuracy of recall is seldom, if ever validated. Geographic Information Systems (GISs) and land cover information derived from satellite imagery can allow researchers to assess the accuracy of this exposure measure, and to quantify the extent and importance of exposure misclassification. As part of a bladder cancer case-control study in Michigan, participants were asked whether they lived on a farm, or within a distance of 1/4, 1/4-1, 1-5, or 45 miles from farmland for each residence over their lifespan. Responses from 531 participants over two time periods F 1978 and 2001 F were investigated. Self reported proximity to farmland was compared to a ''gold standard'' derived from Michigan land cover files for the same time periods. Logistic regression and other statistical measures including sensitivity, specificity, and percentage matching were evaluated. In comparing self-reported and land cover-derived proximity to farmland, cases exhibited better agreement than controls in 2001 (adjusted OR ¼ 1.74; 95% CI ¼ 1.01, 2.99) and worse agreement in 1978, although not significantly (adjusted OR ¼ 0.74; 95% CI ¼ 0.47, 1.16). When comparing 2001 with 1978, both cases and controls showed better agreement in 2001, but only cases showed a significant difference (adjusted OR ¼ 2.36; 95% CI ¼ 1.33, 4.18). These differences in agreement may be influenced by differences in educational attainment between cases and controls, although adjustment for education did not diminish the association. Gender, age, number of years at residence, and geocoding accuracy did not influence agreement between the proximity approaches. This study suggests that proximity measures taken from satellite-derived land cover imagery may be useful for assessing proximity to farmland, and it raises some concerns about the use of self-reported proximity to farmland in exposure assessments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland

Meliker

Jacquez

2007

J Expo Sci Environ Epidemiol

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“…For instance, Brody [89] used land cover maps from Landsat ETM+ to estimate individual historical exposure levels to pesticides based on a number of spatial factors, including residential proximity to land cover classes most likely to contribute pesticide exposure and size of source area in Cape Cod, Massachusetts. Similarly, Landsat imagery was used by Ward et al [90] to determine whether crop maps were useful for predicting residential levels of exposure to crop herbicides in Iowa. They concluded that satellite-based crop maps may be useful for estimating levels of herbicides in homes near crop fields, serving as a surrogate measure of potential exposure to agricultural pesticides [91].…”

Section: Review Of the Use Of Discrete Products In Public Health Resementioning

confidence: 99%

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

et al. 2011

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Abstract:The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or "OBIA") can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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“…Yet Landsat data have had only limited use in pesticide exposure studies [9,[20][21][22] likely due to the previous high cost of the imagery and the lack of effective and efficient methods for linking the imagery to pesticide use data. The US Geological Survey (USGS) is now providing Landsat imagery free of charge eliminating one of the major barriers to using this data [23].…”

Section: Open Accessmentioning

confidence: 99%

“…Populations living near agricultural fields are at higher risk of exposure due to their proximity to areas where pesticides are frequently applied [9][10][11]. To understand the relationships between exposure and health outcomes, epidemiologists need accurate, unbiased exposure estimates as study participants rarely, if ever, know what, when, or where pesticides were applied to crops grown near their residence.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study

Maxwell

2011

Remote Sensing

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Exposure to pesticides has been associated with increased risk of many adverse health effects. To understand the relationships between pesticide exposure and health outcomes, epidemiologists need information on where pesticides are applied in the environment. California maintains one of the most comprehensive pesticide use reporting systems in the world, yet the data are only recorded at a coarse geographic scale of approximately 2.6 km 2 area. A method is presented that uses Landsat image time series to downscale California pesticide use data to the crop field-level. The approach is demonstrated using paraquat applied to vineyard and cotton fields.

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Proximity to Crops and Residential Exposure to Agricultural Herbicides in Iowa

Cited by 159 publications

References 21 publications

Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland

Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study

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