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Clark CJ, Casey JA, Bell ML, Plata DL, Saiers JE, Deziel NC. Accuracy of self-reported distance to nearest unconventional oil and gas well in Pennsylvania, Ohio, and West Virginia residents and implications for exposure assessment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:512-517. [PMID: 38448680 DOI: 10.1038/s41370-023-00637-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 03/08/2024]
Abstract
Self-reported distances to industrial sources have been used in epidemiology as proxies for exposure to environmental hazards and indicators of awareness and perception of sources. Unconventional oil and gas development (UOG) emits pollutants and has been associated with adverse health outcomes. We compared self-reported distance to the nearest UOG well to the geographic information system-calculated distance for 303 Pennsylvania, Ohio, and West Virginia residents using Cohen's Weighted Kappa. Agreement was low (Kappa = 0.18), and self-reports by Ohioans (39% accuracy) were more accurate than West Virginians (22%) or Pennsylvanians (13%, both p < 0.05). Of the demographic characteristics studied, only educational attainment was related to reporting accuracy; residents with 12-16 years of education were more accurate (31.3% of group) than those with <12 or >16 years (both 16.7%). Understanding differences between objective and subjective measures of UOG proximity could inform studies of perceived exposures or risks and may also be relevant to adverse health effects. IMPACT: We compared objective and self-reported measures of distance to the nearest UOG well for 303 Appalachian Basin residents. We found that residents' self-reported distance to the nearest UOG well had limited agreement with the true calculated distance category. Our results can be used to inform the collection and contextualize the use of self-reported data in communities exposed to UOGD. Self-reported metrics can be used in conjunction with objective assessments and can be informative regarding how potentially exposed populations perceive environmental exposures or risks and could provide insights into awareness of distance-related policies, such as setbacks.
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Affiliation(s)
- Cassandra J Clark
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College St., New Haven, CT, 06510, USA.
| | - Joan A Casey
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Michelle L Bell
- Yale School of the Environment, 195 Prospect Street, New Haven, CT, 06511, USA
| | - Desiree L Plata
- Department of Civil and Environmental Engineering, Parsons Laboratory, Massachusetts Institute of Technology, 15 Vassar Street, Cambridge, MA, 02139, USA
| | - James E Saiers
- Yale School of the Environment, 195 Prospect Street, New Haven, CT, 06511, USA
| | - Nicole C Deziel
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College St., New Haven, CT, 06510, USA
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Deziel NC, Clark CJ, Casey JA, Bell ML, Plata DL, Saiers JE. Assessing Exposure to Unconventional Oil and Gas Development: Strengths, Challenges, and Implications for Epidemiologic Research. Curr Environ Health Rep 2022; 9:436-450. [PMID: 35522388 PMCID: PMC9363472 DOI: 10.1007/s40572-022-00358-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2022] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Epidemiologic studies have observed elevated health risks in populations living near unconventional oil and gas development (UOGD). In this narrative review, we discuss strengths and limitations of UOG exposure assessment approaches used in or available for epidemiologic studies, emphasizing studies of children's health outcomes. RECENT FINDINGS Exposure assessment challenges include (1) numerous potential stressors with distinct spatiotemporal patterns, (2) critical exposure windows that cover long periods and occur in the past, and (3) limited existing monitoring data coupled with the resource-intensiveness of collecting new exposure measurements to capture spatiotemporal variation. All epidemiologic studies used proximity-based models for exposure assessment as opposed to surveys, biomonitoring, or environmental measurements. Nearly all studies used aggregate (rather than pathway-specific) models, which are useful surrogates for the complex mix of potential hazards. Simple and less-specific exposure assessment approaches have benefits in terms of scalability, interpretability, and relevance to specific policy initiatives such as set-back distances. More detailed and specific models and metrics, including dispersion methods and stressor-specific models, could reduce exposure misclassification, illuminate underlying exposure pathways, and inform emission control and exposure mitigation strategies. While less practical in a large population, collection of multi-media environmental and biological exposure measurements would be feasible in cohort subsets. Such assessments are well-suited to provide insights into the presence and magnitude of exposures to UOG-related stressors in relation to spatial surrogates and to better elucidate the plausibility of observed effects in both children and adults.
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Affiliation(s)
- Nicole C. Deziel
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College St., New Haven, CT 06510 USA
| | - Cassandra J. Clark
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College St., New Haven, CT 06510 USA
| | - Joan A. Casey
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 630 West 168th Street, Room 16-416, New York, NY 10032 USA
| | - Michelle L. Bell
- Yale School of the Environment, 195 Prospect St., New Haven, CT 06511 USA
| | - Desiree L. Plata
- Department of Civil and Environmental Engineering, Parsons Laboratory, Massachusetts Institute of Technology, 15 Vassar Street, Cambridge, MA 02139 USA
| | - James E. Saiers
- Yale School of the Environment, 195 Prospect St., New Haven, CT 06511 USA
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Clark CJ, Xiong B, Soriano MA, Gutchess K, Siegel HG, Ryan EC, Johnson NP, Cassell K, Elliott EG, Li Y, Cox AJ, Bugher N, Glist L, Brenneis RJ, Sorrentino KM, Plano J, Ma X, Warren JL, Plata DL, Saiers JE, Deziel NC. Assessing Unconventional Oil and Gas Exposure in the Appalachian Basin: Comparison of Exposure Surrogates and Residential Drinking Water Measurements. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:1091-1103. [PMID: 34982938 PMCID: PMC10259677 DOI: 10.1021/acs.est.1c05081] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Health studies report associations between metrics of residential proximity to unconventional oil and gas (UOG) development and adverse health endpoints. We investigated whether exposure through household groundwater is captured by existing metrics and a newly developed metric incorporating groundwater flow paths. We compared metrics with detection frequencies/concentrations of 64 organic and inorganic UOG-related chemicals/groups in residential groundwater from 255 homes (Pennsylvania n = 94 and Ohio n = 161). Twenty-seven chemicals were detected in ≥20% of water samples at concentrations generally below U.S. Environmental Protection Agency standards. In Pennsylvania, two organic chemicals/groups had reduced odds of detection with increasing distance to the nearest well: 1,2-dichloroethene and benzene (Odds Ratio [OR]: 0.46, 95% confidence interval [CI]: 0.23-0.93) and m- and p-xylene (OR: 0.28, 95% CI: 0.10-0.80); results were consistent across metrics. In Ohio, the odds of detecting toluene increased with increasing distance to the nearest well (OR: 1.48, 95% CI: 1.12-1.95), also consistent across metrics. Correlations between inorganic chemicals and metrics were limited (all |ρ| ≤ 0.28). Limited associations between metrics and chemicals may indicate that UOG-related water contamination occurs rarely/episodically, more complex metrics may be needed to capture drinking water exposure, and/or spatial metrics in health studies may better reflect exposure to other stressors.
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Affiliation(s)
- Cassandra J Clark
- Yale School of Public Health, Department of Environmental Health Sciences, 60 College Street, New Haven, Connecticut 06510, United States
| | - Boya Xiong
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
- University of Minnesota, Department of Civil, Environmental and Geo-Engineering, 500 Pillsbury Dr. SE, Minneapolis, Minnesota 55455, United States
| | - Mario A Soriano
- Yale School of the Environment, 195 Prospect Street, New Haven, Connecticut 06511, United States
| | - Kristina Gutchess
- Yale School of the Environment, 195 Prospect Street, New Haven, Connecticut 06511, United States
| | - Helen G Siegel
- Yale School of the Environment, 195 Prospect Street, New Haven, Connecticut 06511, United States
| | - Emma C Ryan
- Tufts University, Department of Public Health and Community Medicine, 136 Harrison Avenue, Boston, Massachusetts 02111, United States
| | - Nicholaus P Johnson
- Yale School of Public Health, Department of Environmental Health Sciences, 60 College Street, New Haven, Connecticut 06510, United States
| | - Kelsie Cassell
- Yale School of Public Health, Department of Epidemiology of Microbial Diseases, 60 College Street, New Haven, Connecticut 06510, United States
| | - Elise G Elliott
- Yale School of Public Health, Department of Environmental Health Sciences, 60 College Street, New Haven, Connecticut 06510, United States
- Harvard T.H. Chan School of Public Health, Department of Environmental Health, Boston, Massachusetts 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Yunpo Li
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - Austin J Cox
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - Nicolette Bugher
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - Lukas Glist
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - Rebecca J Brenneis
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - Keli M Sorrentino
- Center for Perinatal, Pediatric and Environmental Epidemiology, Yale University Schools of Public Health and Medicine, 1 Church Street, New Haven, Connecticut 06510, United States
| | - Julie Plano
- Center for Perinatal, Pediatric and Environmental Epidemiology, Yale University Schools of Public Health and Medicine, 1 Church Street, New Haven, Connecticut 06510, United States
| | - Xiaomei Ma
- Yale School of Public Health, Department of Chronic Disease Epidemiology, 60 College Street, New Haven, Connecticut 06510, United States
| | - Joshua L Warren
- Yale School of Public Health, Department of Biostatistics, 60 College Street, New Haven, Connecticut 06510, United States
| | - Desiree L Plata
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Parsons Laboratory, 15 Vassar Street, Cambridge, Massachusetts 02139, United States
| | - James E Saiers
- Yale School of the Environment, 195 Prospect Street, New Haven, Connecticut 06511, United States
| | - Nicole C Deziel
- Yale School of Public Health, Department of Environmental Health Sciences, 60 College Street, New Haven, Connecticut 06510, United States
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Clark CJ, Warren JL, Kadan-Lottick N, Ma X, Bell ML, Saiers JE, Deziel NC. Community concern and government response: Identifying socio-economic and demographic predictors of oil and gas complaints and drinking water impairments in Pennsylvania. ENERGY RESEARCH & SOCIAL SCIENCE 2021; 76:102070. [PMID: 34123731 PMCID: PMC8192069 DOI: 10.1016/j.erss.2021.102070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Oil and gas development has led to environmental hazards and community concerns, particularly in relation to water supply issues. Filing complaints with state agencies enables citizens to register concerns and seek investigations. We evaluated associations between county-level socio-economic and demographic factors, oil and gas drilling, and three outcomes in Pennsylvania between 2004-2016: number of oil and gas complaints filed, and both the number and proportion of state investigations of water supply complaints yielding a confirmed water supply impairment (i.e., "positive determination"). We used hierarchical Bayesian Poisson and binomial regression analyses. From 2004-2016, 9,404 oil and gas-related complaints were filed, of which 4,099 were water supply complaints. Of those, 3,906 received investigations, and 215 yielded positive determinations. We observed a 47% increase in complaints filed per $10,000 increase in annual median household income (MHI) (Rate Ratio [RR]: 1.47, 95% credible interval [CI]: 1.09-1.96) and an 18% increase per 1% increase in educational attainment (RR: 1.18, 95% CI: 1.11-1.26). While the number of complaints filed did not vary by race/ethnicity, the odds of a complaint yielding a positive determination were 0.81 times lower in counties with a higher proportion of marginalized populations (Odds Ratio [OR]: 0.81 per 1% increase in percent Black, Asian, and Native American populations combined, 95% CI: 0.64-0.99). The odds of positive determinations were also lower in areas with higher income (OR per $10,000 increase in MHI: 0.35, 95% CI: 0.09-0.96). Our results suggest these relationships are complex and may indicate potential environmental and procedural inequities, warranting further investigation.
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Affiliation(s)
- Cassandra J. Clark
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven CT, 06510, United States
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven CT, 06510, United States
| | - Nina Kadan-Lottick
- Department of Pediatric Hematology and Oncology, Yale School of Medicine, 333 Cedar Street, New Haven CT 06510, United States
| | - Xiaomei Ma
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven CT, 06510, United States
| | - Michelle L. Bell
- Yale School of the Environment, 195 Prospect Street, New Haven CT, 06511, United States
| | - James E. Saiers
- Yale School of the Environment, 195 Prospect Street, New Haven CT, 06511, United States
| | - Nicole C. Deziel
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven CT, 06510, United States
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