Treffer: Using the Generalized Synthetic Control Method to Estimate the Impact of Extreme Weather Events on Population Health.
Title:
Using the Generalized Synthetic Control Method to Estimate the Impact of Extreme Weather Events on Population Health.
Authors:
Sheridan P; From the Department of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA.; Department of Epidemiology and Biostatistics, San Diego State University, San Diego, CA., McElroy S; From the Department of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA.; Department of Epidemiology and Biostatistics, San Diego State University, San Diego, CA., Casey J; Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY., Benmarhnia T; From the Department of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA.; Climate, Atmospheric Science & Physical Oceanography, Scripps Institute of Oceanography, La Jolla, CA.
Source:
Epidemiology (Cambridge, Mass.) [Epidemiology] 2022 Nov 01; Vol. 33 (6), pp. 788-796. Date of Electronic Publication: 2022 Sep 27.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9009644 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5487 (Electronic) Linking ISSN: 10443983 NLM ISO Abbreviation: Epidemiology Subsets: MEDLINE
Imprint Name(s):
Publication: <2000>- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
MeSH Terms:
References:
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Hutchinson JA, Vargo J, Milet M, et al. The San Diego 2007 wildfires and Medi-Cal emergency department presentations, inpatient hospitalizations, and outpatient visits: an observational study of smoke exposure periods and a bidirectional case–crossover analysis. PLoS Med. 2018;15:e1002601.
Carracedo-Martínez E, Taracido M, Tobias A, et al. Case–crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118:1173–1182.
Semenza JC, Rubin CH, Falter KH, et al. Heat-related deaths during the July 1995 heat wave in Chicago. N Engl J Med. 1996;335:84–90.
Cruz-Cano R, Mead EL. Excess deaths after Hurricane Maria in Puerto Rico. JAMA. 2019;321:1005–1005.
Bouttell J, Craig P, Lewsey J, et al. Synthetic control methodology as a tool for evaluating population-level health interventions. J Epidemiol Community Health. 2018;72:673–678.
Bor J. Capitalizing on natural experiments to improve our understanding of population health. Am J Public Health. 2016;106:1388–.
O’Neill S, Kreif N, Sutton M, et al. A comparison of methods for health policy evaluation with controlled pre-post designs. Health Services Res. 2020;55:328–338.
Benmarhnia T, Bailey Z, Kaiser D, et al. A difference-in-differences approach to assess the effect of a heat action plan on heat-related mortality, and differences in effectiveness according to sex, age, and socioeconomic status (Montreal, Quebec). Environ Health Perspect. 2016;124:16941694.–169411699.
Goin DE, Rudolph KE, Ahern J. Impact of drought on crime in California: a synthetic control approach. PLoS One. 2017;12:e0185629e0185629.
Matthay EC, Hagan E, Gottlieb LM, et al. Alternative causal inference methods in population health research: evaluating tradeoffs and triangulating evidence. SSM Popul Health. 2020;10:100526.
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2017;45:1866–1886.
Basu S, Meghani A, Siddiqi A. Evaluating the health impact of large-scale public policy changes: classical and novel approaches. Annu Rev Public Health. 2017;38:351–370.
Craig P, Katikireddi SV, Leyland A, et al. Natural experiments: an overview of methods, approaches, and contributions to public health intervention research. Annu Rev Public Health. 2017;38:39–56.
Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81:968945–968960.
Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31:422–429.
Boon MH, Craig P, Thomson H, et al. Regression discontinuity designs in health: a systematic review. Epidemiology. 2021;32:87.
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Abadie A. Semiparametric difference-in-differences estimators. Rev Econ Stud. 2005;72:1–19.
Fry CE, Hatfield LA. Birds of a feather flock together: comparing controlled pre–post designs. Health Services Res. 2021;56:942–952.
Benmarhnia T, Rudolph KE. A rose by any other name still needs to be identified (with plausible assumptions). Int J Epidemiol. 2019;48:2061–2062.
Abadie A, Diamond A, Hainmueller J. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J Am Stat Assoc. 2010;105:493–505.
Abadie A, Diamond A, Hainmueller J. Comparative politics and the synthetic control method. Am J Polit Sci. 2015;59:495–510.
Abadie A, Gardeazabal J. The economic costs of conflict: a case study of the Basque country. Amer Econ Rev. 2003;93:113–132.
Rehkopf DH, Basu S. A new tool for case studies in epidemiology-the synthetic control method. Epidemiology. 2018;29:503–505.
Athey S, Bayati M, Doudchenko N, et al. Matrix Completion Methods for Causal Panel Data Models. National Bureau of Economic Research; 2018.
Ben-Michael E, Feller A, Rothstein J. The augmented synthetic control method. Journal of the American Statistical Association. 2021;116:1789–1803.
Abadie A, L’Hour J. A Penalized Synthetic Control Estimator for Disaggregated Data. Journal of the American Statistical Association. 2018;116:1817–1834.
Hazlett C, Xu Y. Trajectory balancing: a general reweighting approach to causal inference with time-series cross-sectional data. Available at SSRN 3214231. 2018. https://ssrn.com/abstract=3214231 or http://dx.doi.org/10.2139/ssrn.3214231 . (PMID: 10.2139/ssrn.3214231)
Bai J. Panel data models with interactive fixed effects. Econometrica. 2009;77:1229–1279.
Xu Y. Generalized synthetic control method: causal inference with interactive fixed effects models. Polit Anal. 2017;25:57–76.
O’Neill S, Kreif N, Sutton M, et al. A comparison of methods for health policy evaluation with controlled pre-post designs. Health Serv Res. 2020;55:328–338.
Anenberg SC, Haines S, Wang E, et al. Synergistic health effects of air pollution, temperature, and pollen exposure: a systematic review of epidemiological evidence. Environ Health. 2020;19:130.
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688–.
Liu JC, Pereira G, Uhl SA, et al. A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke. Environ Res. 2015;136:120–132.
Reid CE, Brauer M, Johnston FH, et al. Critical review of health impacts of wildfire smoke exposure. Environ Health Perspect. 2016;124:1334–1343.
Aguilera R, Gershunov A, Ilango SD, et al. Santa Ana winds of Southern California impact PM2. 5 with and without smoke from wildfires. GeoHealth. 2020;4:e2019GH–e000225.
Guzman-Morales J, Gershunov A, Theiss J, et al. Santa Ana winds of Southern California: their climatology, extremes, and behavior spanning six and a half decades. Geophys Res Lett. 2016;43:2827–2834.
Livneh B, Bohn TJ, Pierce DW, et al. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Sci Data. 2015;2:150042.
Cascio WE. Wildland fire smoke and human health. Sci Total Environ. 2018;624:586–595.
Delfino RJ, Brummel S, Wu J, et al. The relationship of respiratory and cardiovascular hospital admissions to the southern California wildfires of 2003. Occup Environ Med. 2009;66:189–197.
McGlothlin AE, Viele K. Bayesian hierarchical models. JAMA. 2018;320:2365–2366.
Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression. J R Stat Soc Ser A Stat Soc. 1998;47:431–443.
Callaway B, Goodman-Bacon A, Sant’Anna PH. Difference-in-differences with a continuous treatment. arXiv preprint arXiv:2107.02637. 2021.
Kondo MC, De Roos AJ, White LS, et al. Meta-analysis of heterogeneity in the effects of wildfire smoke exposure on respiratory health in North America. Int J Environ Res Public Health. 2019;16:960.
Liu JC, Wilson A, Mickley LJ, et al. Who among the elderly is most vulnerable to exposure to and health risks of fine particulate matter from wildfire smoke? Am J Epidemiol. 2017;186:730–735.
Benmarhnia T, Deguen S, Kaufman JS, et al. Vulnerability to heat-related mortality. Epidemiology. 2015;26:781–793.
Son J-Y, Liu JC, Bell ML. Temperature-related mortality: a systematic review and investigation of effect modifiers. Environ Res Lett. 2019;14:073004.
Armstrong B. Models for the relationship between ambient temperature and daily mortality. Epidemiology. 2006;17:624–631.
Bhaskaran K, Gasparrini A, Hajat S, et al. Time series regression studies in environmental epidemiology. Int J Epidemiol. 2013:42:1187–95.
Hutchinson JA, Vargo J, Milet M, et al. The San Diego 2007 wildfires and Medi-Cal emergency department presentations, inpatient hospitalizations, and outpatient visits: an observational study of smoke exposure periods and a bidirectional case–crossover analysis. PLoS Med. 2018;15:e1002601.
Carracedo-Martínez E, Taracido M, Tobias A, et al. Case–crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118:1173–1182.
Semenza JC, Rubin CH, Falter KH, et al. Heat-related deaths during the July 1995 heat wave in Chicago. N Engl J Med. 1996;335:84–90.
Cruz-Cano R, Mead EL. Excess deaths after Hurricane Maria in Puerto Rico. JAMA. 2019;321:1005–1005.
Bouttell J, Craig P, Lewsey J, et al. Synthetic control methodology as a tool for evaluating population-level health interventions. J Epidemiol Community Health. 2018;72:673–678.
Bor J. Capitalizing on natural experiments to improve our understanding of population health. Am J Public Health. 2016;106:1388–.
O’Neill S, Kreif N, Sutton M, et al. A comparison of methods for health policy evaluation with controlled pre-post designs. Health Services Res. 2020;55:328–338.
Benmarhnia T, Bailey Z, Kaiser D, et al. A difference-in-differences approach to assess the effect of a heat action plan on heat-related mortality, and differences in effectiveness according to sex, age, and socioeconomic status (Montreal, Quebec). Environ Health Perspect. 2016;124:16941694.–169411699.
Goin DE, Rudolph KE, Ahern J. Impact of drought on crime in California: a synthetic control approach. PLoS One. 2017;12:e0185629e0185629.
Matthay EC, Hagan E, Gottlieb LM, et al. Alternative causal inference methods in population health research: evaluating tradeoffs and triangulating evidence. SSM Popul Health. 2020;10:100526.
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2017;45:1866–1886.
Basu S, Meghani A, Siddiqi A. Evaluating the health impact of large-scale public policy changes: classical and novel approaches. Annu Rev Public Health. 2017;38:351–370.
Craig P, Katikireddi SV, Leyland A, et al. Natural experiments: an overview of methods, approaches, and contributions to public health intervention research. Annu Rev Public Health. 2017;38:39–56.
Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81:968945–968960.
Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31:422–429.
Boon MH, Craig P, Thomson H, et al. Regression discontinuity designs in health: a systematic review. Epidemiology. 2021;32:87.
Chen H, Li Q, Kaufman JS, et al. Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada. Lancet Planet Health. 2018;2:e19–e26.
Card D. The impact of the Mariel boatlift on the Miami labor market. ILR Rev. 1990;43:245–257.
Abadie A, Cattaneo MD. Econometric methods for program evaluation. Annu Rev Econ. 2018;10:465–503.
Abadie A. Semiparametric difference-in-differences estimators. Rev Econ Stud. 2005;72:1–19.
Fry CE, Hatfield LA. Birds of a feather flock together: comparing controlled pre–post designs. Health Services Res. 2021;56:942–952.
Benmarhnia T, Rudolph KE. A rose by any other name still needs to be identified (with plausible assumptions). Int J Epidemiol. 2019;48:2061–2062.
Abadie A, Diamond A, Hainmueller J. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J Am Stat Assoc. 2010;105:493–505.
Abadie A, Diamond A, Hainmueller J. Comparative politics and the synthetic control method. Am J Polit Sci. 2015;59:495–510.
Abadie A, Gardeazabal J. The economic costs of conflict: a case study of the Basque country. Amer Econ Rev. 2003;93:113–132.
Rehkopf DH, Basu S. A new tool for case studies in epidemiology-the synthetic control method. Epidemiology. 2018;29:503–505.
Athey S, Bayati M, Doudchenko N, et al. Matrix Completion Methods for Causal Panel Data Models. National Bureau of Economic Research; 2018.
Ben-Michael E, Feller A, Rothstein J. The augmented synthetic control method. Journal of the American Statistical Association. 2021;116:1789–1803.
Abadie A, L’Hour J. A Penalized Synthetic Control Estimator for Disaggregated Data. Journal of the American Statistical Association. 2018;116:1817–1834.
Hazlett C, Xu Y. Trajectory balancing: a general reweighting approach to causal inference with time-series cross-sectional data. Available at SSRN 3214231. 2018. https://ssrn.com/abstract=3214231 or http://dx.doi.org/10.2139/ssrn.3214231 . (PMID: 10.2139/ssrn.3214231)
Bai J. Panel data models with interactive fixed effects. Econometrica. 2009;77:1229–1279.
Xu Y. Generalized synthetic control method: causal inference with interactive fixed effects models. Polit Anal. 2017;25:57–76.
O’Neill S, Kreif N, Sutton M, et al. A comparison of methods for health policy evaluation with controlled pre-post designs. Health Serv Res. 2020;55:328–338.
Anenberg SC, Haines S, Wang E, et al. Synergistic health effects of air pollution, temperature, and pollen exposure: a systematic review of epidemiological evidence. Environ Health. 2020;19:130.
Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688–.
Liu JC, Pereira G, Uhl SA, et al. A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke. Environ Res. 2015;136:120–132.
Reid CE, Brauer M, Johnston FH, et al. Critical review of health impacts of wildfire smoke exposure. Environ Health Perspect. 2016;124:1334–1343.
Aguilera R, Gershunov A, Ilango SD, et al. Santa Ana winds of Southern California impact PM2. 5 with and without smoke from wildfires. GeoHealth. 2020;4:e2019GH–e000225.
Guzman-Morales J, Gershunov A, Theiss J, et al. Santa Ana winds of Southern California: their climatology, extremes, and behavior spanning six and a half decades. Geophys Res Lett. 2016;43:2827–2834.
Livneh B, Bohn TJ, Pierce DW, et al. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Sci Data. 2015;2:150042.
Cascio WE. Wildland fire smoke and human health. Sci Total Environ. 2018;624:586–595.
Delfino RJ, Brummel S, Wu J, et al. The relationship of respiratory and cardiovascular hospital admissions to the southern California wildfires of 2003. Occup Environ Med. 2009;66:189–197.
McGlothlin AE, Viele K. Bayesian hierarchical models. JAMA. 2018;320:2365–2366.
Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression. J R Stat Soc Ser A Stat Soc. 1998;47:431–443.
Callaway B, Goodman-Bacon A, Sant’Anna PH. Difference-in-differences with a continuous treatment. arXiv preprint arXiv:2107.02637. 2021.
Kondo MC, De Roos AJ, White LS, et al. Meta-analysis of heterogeneity in the effects of wildfire smoke exposure on respiratory health in North America. Int J Environ Res Public Health. 2019;16:960.
Liu JC, Wilson A, Mickley LJ, et al. Who among the elderly is most vulnerable to exposure to and health risks of fine particulate matter from wildfire smoke? Am J Epidemiol. 2017;186:730–735.
Benmarhnia T, Deguen S, Kaufman JS, et al. Vulnerability to heat-related mortality. Epidemiology. 2015;26:781–793.
Son J-Y, Liu JC, Bell ML. Temperature-related mortality: a systematic review and investigation of effect modifiers. Environ Res Lett. 2019;14:073004.
Grant Information:
T32 AG058529 United States AG NIA NIH HHS
Substance Nomenclature:
0 (Smoke)
Entry Date(s):
Date Created: 20220927 Date Completed: 20221013 Latest Revision: 20230117
Update Code:
20250114
DOI:
10.1097/EDE.0000000000001539
PMID:
36166207
Database:
MEDLINE
Weitere Informationen
Background: Traditional epidemiologic approaches such as time-series or case-crossover designs are often used to estimate the effects of extreme weather events but can be limited by unmeasured confounding. Quasi-experimental methods are a family of methods that leverage natural experiments to adjust for unmeasured confounding indirectly. The recently developed generalized synthetic control method that exploits the timing of an exposure is well suited to estimate the impact of acute environmental events on health outcomes. To demonstrate how this method can be used to study extreme weather events, we examined the impact of the 20-26 October 2007 Southern California wildfire storm on respiratory hospitalizations.
Methods: We used generalized synthetic control to compare the average number of ZIP code-level respiratory hospitalizations during the wildfire storm between ZIP codes that were classified as exposed versus unexposed to wildfire smoke. We considered wildfire exposure eligibility for each ZIP code using fire perimeters and satellite-based smoke plume data. We retrieved respiratory hospitalization discharge data from the Office of Statewide Health Planning and Development. R code to implement the generalized synthetic control method is included for reproducibility.
Results: The analysis included 172 exposed and 578 unexposed ZIP codes. We estimated that the average effect of the wildfire storm among the exposed ZIP codes was an 18% (95% confidence interval: 10% to 29%) increase in respiratory hospitalizations.
Conclusions: We illustrate the use of generalized synthetic control to leverage natural experiments to quantify the health impacts of extreme weather events when traditional approaches are unavailable or limited by assumptions.
(Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)
The authors report no conflicts of interest.