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Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries

Nature Energy volume 7, pages 177–185 (2022)Cite this article

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Abstract

Little is known about whether exposure to unconventional oil and gas development is associated with higher mortality risks in the elderly and whether related air pollutants are exposure pathways. We studied a cohort of 15,198,496 Medicare beneficiaries (136,215,059 person-years) in all major US unconventional exploration regions from 2001 to 2015. We gathered data from records of more than 2.5 million oil and gas wells. For each beneficiary’s ZIP code of residence and year in the cohort, we calculated a proximity-based and a downwind-based pollutant exposure. We analysed the data using two methods: a Cox proportional hazards model and a difference-in-differences design. We found evidence of a statistically significant higher mortality risk associated with living in proximity to and downwind of unconventional oil and gas wells. Our results suggest that primary air pollutants sourced from unconventional oil and gas exploration can be a major exposure pathway with adverse health effects in the elderly.

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Fig. 1: Process diagram of our study design.
Fig. 2: Map of the study area, which contains more than 120,000 active UOGD wells located in 9,244 ZIP codes as of December 2015.
Fig. 3: UOGD exposure assessment in an example ZIP code and month (Washington, Pennsylvania 15301, August 2015).
Fig. 4: The results of Model I and Model II in Analysis Set I.
Fig. 5: Trends in all-cause mortality rate in the treatment group and comparison group pre- and post-drilling.
Fig. 6: The results of a pre-test of the assumption of parallel trends in the mortality rate between the treatment and comparison groups (DiD in Analysis Set II).

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Data availability

Medicare beneficiary data are available from https://data.medicare.gov/ for researchers who meet the criteria for access to confidential data. UOGD data are available from Enverus (https://www.enverus.com/) via subscription. The UOGD exposure data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

All model codes are available at https://github.com/longxiang1025/Fracking_Health.

References

  1. The Distribution of US Oil and Natural Gas Wells by Production Rate (US Energy Information Administration, 2020); https://www.eia.gov/petroleum/wells/

  2. Czolowski, E. D., Santoro, R. L., Srebotnjak, T. & Shonkoff, S. B. C. Toward consistent methodology to quantify populations in proximity to oil and gas development: a national spatial analysis and review. Environ. Health Perspect. 125, 086004 (2017).

    Article  Google Scholar 

  3. Hydraulic Fracturing for Oil and Gas: Impacts from the Hydraulic Fracturing Water Cycle on Drinking Water Resources in the United States (Final Report) (United States Environmental Protection Agency, 2016); https://cfpub.epa.gov/ncea/hfstudy/recordisplay.cfm?deid=332990

  4. Health Effects Institute-Energy Research Committee Human Exposure to Unconventional Oil and Gas Development: A Literature Survey for Research Planning (Draft for Public Comment) (HEI-Energy, 2019)

  5. Adgate, J. L., Goldstein, B. D. & McKenzie, L. M. Potential public health hazards, exposures and health effects from unconventional natural gas development. Environ. Sci. Technol. 48, 8307–8320 (2014).

    Article  Google Scholar 

  6. Garcia-Gonzales, D. A., Shonkoff, S. B. C., Hays, J. & Jerrett, M. Hazardous air pollutants associated with upstream oil and natural gas development: a critical synthesis of current peer-reviewed literature. Annu. Rev. Public Health 40, 283–304 (2019).

    Article  Google Scholar 

  7. Shonkoff, S. B. C., Hays, J. & Finkel, M. L. Environmental public health dimensions of shale and tight gas development. Environ. Health Perspect. 122, 787–795 (2014).

    Article  Google Scholar 

  8. Allen, D. T. Atmospheric emissions and air quality impacts from natural gas production and use. Annu. Rev. Chem. Biomol. Eng. 5, 55–75 (2014).

    Article  Google Scholar 

  9. Cheadle, L. C. et al. Surface ozone in the Colorado northern Front Range and the influence of oil and gas development during FRAPPE/DISCOVER-AQ in summer 2014. Elementa 5, 61 (2017).

    Google Scholar 

  10. Casey, J. A. et al. Predictors of indoor radon concentrations in Pennsylvania, 1989–2013. Environ. Health Perspect. 123, 1130–1137 (2015).

    Article  Google Scholar 

  11. Li, L. et al. Unconventional oil and gas development and ambient particle radioactivity. Nat. Commun. 11, 5002 (2020).

    Article  Google Scholar 

  12. Hill, E. & Ma, L. Shale gas development and drinking water quality. AEA Pap. Proc. 107, 522–525 (2017).

    Google Scholar 

  13. Olmstead, S. M., Muehlenbachs, L. A., Shih, J. S., Chu, Z. & Krupnick, A. J. Shale gas development impacts on surface water quality in Pennsylvania. Proc. Natl Acad. Sci. USA 110, 4962–4967 (2013).

    Article  Google Scholar 

  14. Blair, B. D., Brindley, S., Dinkeloo, E., McKenzie, L. M. & Adgate, J. L. Residential noise from nearby oil and gas well construction and drilling. J. Expo. Sci. Environ. Epidemiol. 28, 538–547 (2018).

    Article  Google Scholar 

  15. Franklin, M., Chau, K., Cushing, L. J. & Johnston, J. E. Characterizing flaring from unconventional oil and gas operations in south Texas using satellite observations. Environ. Sci. Technol. 53, 2220–2228 (2019).

    Article  Google Scholar 

  16. Casey, J. A. et al. Unconventional natural gas development and birth outcomes in Pennsylvania, USA. Epidemiology 27, 163–172 (2016).

    Article  Google Scholar 

  17. Hill, E. L. Shale gas development and infant health: evidence from Pennsylvania. J. Health Econ. 61, 134–150 (2018).

    Article  Google Scholar 

  18. Apergis, N., Hayat, T. & Saeed, T. Fracking and infant mortality: fresh evidence from Oklahoma. Environ. Sci. Pollut. Res. Int. 26, 32360–32367 (2019).

    Article  Google Scholar 

  19. Currie, J., Greenstone, M. & Meckel, K. Hydraulic fracturing and infant health: new evidence from Pennsylvania. Sci. Adv. 3, e1603021 (2017).

    Article  Google Scholar 

  20. Rasmussen, S. G. et al. Association between unconventional natural gas development in the Marcellus shale and asthma exacerbations. JAMA Intern. Med. 176, 1334–1343 (2016).

    Article  Google Scholar 

  21. McKenzie, L. M. et al. Relationships between indicators of cardiovascular disease and intensity of oil and natural gas activity in Northeastern Colorado. Environ. Res. 170, 56–64 (2019).

    Article  Google Scholar 

  22. Elliott, E. G. et al. Unconventional oil and gas development and risk of childhood leukemia: assessing the evidence. Sci. Total Environ. 576, 138–147 (2017).

    Article  Google Scholar 

  23. Koehler, K. et al. Exposure assessment using secondary data sources in unconventional natural gas development and health studies. Environ. Sci. Technol. 52, 6061–6069 (2018).

    Article  Google Scholar 

  24. Brown, D. R., Greiner, L. H., Weinberger, B. I., Walleigh, L. & Glaser, D. Assessing exposure to unconventional natural gas development: using an air pollution dispersal screening model to predict new-onset respiratory symptoms. J. Environ. Sci. Health A 54, 1357–1363 (2019).

    Article  Google Scholar 

  25. VanderWeele, T. J. & Ding, P. Sensitivity analysis in observational research: introducing the E-value. Ann. Intern. Med. 167, 268–274 (2017).

    Article  Google Scholar 

  26. Mathur, M. B., Ding, P., Riddell, C. A. & VanderWeele, T. J. Web site and R package for computing E-values. Epidemiology 29, e45–e47 (2018).

    Article  Google Scholar 

  27. Giles, J. A. & Giles, D. E. A. Pre‐test estimation and testing in econometrics: recent developments. J. Econ. Surv. 7, 145–197 (1993).

    Article  Google Scholar 

  28. Health Effects Institute-Energy Research Committee Potential Human Health Effects Associated With Unconventional Oil and Gas Development: A Systematic Review of the Epidemiology Literature (HEI-Energy, 2019).

  29. Wing, C., Simon, K. & Bello-Gomez, R. A. Designing difference in difference studies: best practices for public health policy research. Annu. Rev. Public Health 39, 453–469 (2018).

    Article  Google Scholar 

  30. Drilling Productivity Report (US Energy Information Administration, 2019); https://www.eia.gov/petroleum/drilling/

  31. Research Data Assistance Center Master Beneficiary Summary File (MBSF) Base (ResDAC, 2018); https://www.resdac.org/cms-data/files/mbsf-base

  32. Enverus Drillinginfo Direct Access Application Programming Interface. https://app.drillinginfo.com/direct/ (2019).

  33. Doxsey-Whitfield, E. et al. Taking advantage of the improved availability of census data: a first look at the Gridded Population of the World, version 4. Pap. Appl. Geogr. 1, 226–234 (2015).

    Article  Google Scholar 

  34. Mesinger, F. et al. North American Regional Reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006).

    Article  Google Scholar 

  35. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  36. Therneau, T. M. A Package for Survival Analysis in S (XXXX, 2019); https://cran.r-project.org/package=survival

  37. Gaure, S. lfe: linear group fixed effects. R. J. 5, 104–117 (2013).

    Article  Google Scholar 

  38. Andersen, P. K. & Gill, R. D. Cox’s regression model for counting processes: a large sample study. Ann. Stat. 10, 1100–1120 (1982).

    Article  MathSciNet  Google Scholar 

  39. Lee, E. W., Wei, L. J., Amato, D. A. & Leurgans, S. in Survival Analysis: State of the Art (eds Klein, J. P. & Goel P. K.) 237–247 (Springer, 1992); https://doi.org/10.1007/978-94-015-7983-4_14

  40. Behavioral Risk Factor Surveillance System BRFSS 2013 Survey Data and Documentation (Centers for Disease Control and Prevention, 2013); https://www.cdc.gov/brfss/annual_data/annual_2013.html

  41. Stringfellow, W. T., Camarillo, M. K., Domen, J. K. & Shonkoff, S. B. C. Comparison of chemical-use between hydraulic fracturing, acidizing, and routine oil and gas development. PLoS ONE 12, e0175344 (2017).

    Article  Google Scholar 

  42. Di, Q. et al. Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ. Sci. Technol. 50, 4712–4721 (2016).

    Article  Google Scholar 

  43. Earth Resources Observation and Science (EROS) Center The National Land Cover Database (United States Geological Survey, 2012); https://www.usgs.gov/centers/eros/science/national-land-cover-database

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Acknowledgements

This work was made possible by support from the US Environmental Protection Agency (EPA) grant RD-835872 (L.L., A.J.B., J.D.S., B.A.C., J.L., Y.W. and P.K.), the National Institutes of Health (NIH) grant R01 MD012769 (F.D.) and the Climate Change Solutions Fund at Harvard University (F.D.). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the US EPA, NIH or Harvard University. Furthermore, the US EPA does not endorse the purchase of any commercial products or services mentioned in the publication. We sincerely thank J. M. Wolfson, J. Buonocore and L. Goodwin for editing the manuscript.

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Author notes

  1. Annelise J. Blomberg

    Present address: Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden

Authors and Affiliations

  1. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Longxiang Li, Annelise J. Blomberg, Joel D. Schwartz, Brent A. Coull, John D. Spengler, Yaguang Wei, Joy Lawrence & Petros Koutrakis

  2. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Francesca Dominici, Falco J. Bargagli-Stoffi & Brent A. Coull

  3. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Joel D. Schwartz

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Contributions

P.K. and L.L. initiated the study; L.L. synthesized data and performed research; L.L., B.A.C., J.D.S. and F.D. developed the model; and L.L., F.D., A.J.B., F.J.B.-S., Y.W. and P.K. wrote the manuscript. J.L. and J.D.S. helped interpret the results and provided comments.

Corresponding author

Correspondence to Longxiang Li.

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Competing interests

F.D. has served on the HEI Research Committee. The remaining authors declare no competing interests.

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Nature Energy thanks Seth B. C. Shonkoff, Michael Hendryx and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–10, Notes 1–9 and Tables 1–6.

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Li, L., Dominici, F., Blomberg, A.J. et al. Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries. Nat Energy 7, 177–185 (2022). https://doi.org/10.1038/s41560-021-00970-y

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