Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-10-31T19:37:53.706Z Has data issue: false hasContentIssue false

A Spatial and Temporal Investigation of Medical Surge in Dallas–Fort Worth During Hurricane Harvey, Texas 2017

Published online by Cambridge University Press:  30 January 2020

William Stephens
Affiliation:
Tarrant County Public Health, Office of Public Health Informatics, Fort Worth, Texas
Grete E. Wilt*
Affiliation:
CDC, Division of Toxicology and Human Health Sciences, Geospatial Research, Analysis, and Services Program, Atlanta, Georgia
Erica Adams Lehnert
Affiliation:
CDC, Division of Toxicology and Human Health Sciences, Geospatial Research, Analysis, and Services Program, Atlanta, Georgia
NoelleAngelique M. Molinari
Affiliation:
Centers for Disease Control and Prevention (CDC), Center for Preparedness and Response, Division of State and Local Readiness, Applied Science and Evaluation Branch, Atlanta, Georgia
Tanya Telfair LeBlanc
Affiliation:
Centers for Disease Control and Prevention (CDC), Center for Preparedness and Response, Division of State and Local Readiness, Applied Science and Evaluation Branch, Atlanta, Georgia
*
Correspondence and reprint requests to Grete Wilt, Centers for Disease Control and Prevention, Geospatial Research Analysis and Services Program, Atlanta, Georgia, 30329-4018 (e-mail: gretewilt@gmail.com)

Abstract

Objective:

When 2017 Hurricane Harvey struck the coastline of Texas on August 25, 2017, it resulted in 88 fatalities and more than US $125 billion in damage to infrastructure. The floods associated with the storm created a toxic mix of chemicals, sewage and other biohazards, and over 6 million cubic meters of garbage in Houston alone. The level of biohazard exposure and injuries from trauma among persons residing in affected areas was widespread and likely contributed to increases in emergency department (ED) visits in Houston and cities receiving hurricane evacuees. We investigated medical surge resulting from these evacuations in Dallas–Fort Worth (DFW) metroplex EDs.

Methods:

We used data sourced from the North Texas Syndromic Surveillance Region 2/3 in ESSENCE to investigate ED visit surge following the storm in DFW hospitals because this area received evacuees from the 60 counties with disaster declarations due to the storm. We used the interrupted time series (ITS) analysis to estimate the magnitude and duration of the ED surge. ITS was applied to all ED visits in DFW and visits made by patients residing in any of the 60 counties with disaster declarations due to the storm. The DFW metropolitan statistical area included 55 hospitals. Time series analyses examined data from March 1, 2017–January 6, 2018 with focus on the storm impact period, August 14–September 15, 2017. Data from before, during, and after the storm were visualized spatially and temporally to characterize magnitude, duration, and spatial variation of medical surge attributable to Hurricane Harvey.

Results:

During the study period overall, ED visits in the DFW area rose immediately by about 11% (95% CI: 9%, 13%), amounting to ~16 500 excess total visits before returning to the baseline on September 21, 2017. Visits by patients identified as residing in disaster declaration counties to DFW hospitals rose immediately by 127% (95% CI: 125%, 129%), amounting to 654 excess visits by September 29, 2017, when visits returned to the baseline. A spatial analysis revealed that evacuated patients were strongly clustered (Moran’s I = 0.35, P < 0.0001) among 5 of the counties with disaster declarations in the 11-day window during the storm surge.

Conclusions:

The observed increase in ED visits in DFW due to Hurricane Harvey and ensuing evacuation was significant. Anticipating medical surge following large-scale hurricanes is critical for community preparedness planning. Coordinated planning across stakeholders is necessary to safeguard the population and for a skillful response to medical surge needs. Plans that address hurricane response, in particular, should have contingencies for support beyond the expected disaster areas.

Type
Original Research
Copyright
© 2020 Society for Disaster Medicine and Public Health, Inc.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Vick, DJ, Wilson, AB, Fisher, M, Roseamelia, C. Assessment of community hospital disaster preparedness in New York. J Emerg Manage. 2018;16(4):213227.10.5055/jem.2018.0371CrossRefGoogle ScholarPubMed
Krause, E, Reeves, R. Hurricanes hit the poor the hardest. Brookings Institute. Social Mobility Memos. Monday, September 17, 2017. www.brookings.edu/blog/social-mobility-memos/2017/09/18/hurricanes-hit-the-poor-the-hardest. Accessed September 19, 2019.Google Scholar
Shartar, SE, Moore, BL, Wood, LM. Developing a mass casualty surge capacity protocol for emergency medical services to use for patient distribution. South Med J. 2017;110(12):792795.10.14423/SMJ.0000000000000740CrossRefGoogle ScholarPubMed
Pines, JM. Freestanding emergency department visits and disasters: the case of Hurricane Harvey. Am J Emerg Med. 2018;36:15131514.10.1016/j.ajem.2018.01.016CrossRefGoogle ScholarPubMed
Friedrich, MJ.Determining health effects of hazardous materials released during Hurricane Harvey. J Am Med Assoc. 2017;318(23):2283.10.1001/jama.2017.15558CrossRefGoogle ScholarPubMed
Liu, EL, Morshedi, B, Miller, BL, et al.Dallas Mega Shelter Medical Operations response to Hurricane Harvey. Report from the field. Disaster Med Public Health Prep. 2019;13(1):9093.10.1017/dmp.2017.123CrossRefGoogle Scholar
Texas Department of State Health Services. Texas Syndromic Surveillance (TxS2). 2018. https://dshs.texas.gov/txs2/. Accessed January 2019.Google Scholar
Wilt, G, Adams, E, Thomas, E, et al.A space time analysis evaluating the impact of Hurricane Sandy on HIV testing rates. Int J Disaster Risk Reduct. 2018;28:839844.10.1016/j.ijdrr.2018.04.003CrossRefGoogle Scholar
Ekperi, L, Thomas, E, LeBlanc, TT, et al.The impact of Hurricane Sandy on HIV testing rates: an interrupted time series analysis, January 1, 2011 to December 31, 2013. PLoS Curr Disasters. 2018. doi:10.1371/currents.dis.ea09f9573dc292951b7eb0cf9f395003.CrossRefGoogle Scholar
Jandoc, R, Burden, AM, Mamdani, M, et al.Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68:950956.10.1016/j.jclinepi.2014.12.018CrossRefGoogle ScholarPubMed
Molinari, N, LeBlanc, T, Stephens, W.The impact of a case of Ebola virus disease on emergency department visits in Metropolitan Dallas-Fort Worth, TX, July 2013–July 2015: an interrupted time series analysis. PLoS Curr Outbreaks. 2018. doi:10.1371/currents.outbreaks.e62bdea371ef5454d56f71fe217aead0.CrossRefGoogle ScholarPubMed
Burkom, HS, Elbert, Y, Magruder, SF, et al.Developments in the roles, features, and evaluation of alerting algorithms for disease outbreak monitoring. Johns Hopkins APL Tech Dig. 2008;27:313.Google Scholar
Wagner, AK, Soumerai, SB, Zhang, F, Ross-Degnan, D.Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299309.10.1046/j.1365-2710.2002.00430.xCrossRefGoogle ScholarPubMed
Zhang, F, Wagner, AK, Soumerai, SB, Ross-Degnan, D.Methods for estimating confidence intervals in interrupted time series analyses of health interventions. J Clin Epidemiol. 2009;62:143148.10.1016/j.jclinepi.2008.08.007CrossRefGoogle ScholarPubMed
Bickel, WK, Moody, L, Higgins, ST. Some current dimensions of the behavioral economics of health-related behavior change. Prev Med. 2016;92(2016):1623.10.1016/j.ypmed.2016.06.002CrossRefGoogle ScholarPubMed