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Time Series Analysis of Congestive Heart Failure Discharges in Florida (USA) Post Tropical Cyclones

Published online by Cambridge University Press:  24 January 2023

Inkyu Kim*
Affiliation:
Harvard Medical School, Boston, Massachusetts USA; currently: Harvard-Affiliated Emergency Medicine Residency at Massachusetts General Hospital and Brigham and Women’s Hospital, Boston, Massachusetts USA
Joseph J. Locascio
Affiliation:
Massachusetts General Hospital, Boston, Massachusetts USA
Ritu Sarin
Affiliation:
Beth Israel Deaconess Medical Center, Disaster Medicine Fellowship, Boston, Massachusetts USA
Alexander Hart
Affiliation:
Beth Israel Deaconess Medical Center, Disaster Medicine Fellowship, Boston, Massachusetts USA
Gregory R. Ciottone
Affiliation:
Beth Israel Deaconess Medical Center, Disaster Medicine Fellowship, Boston, Massachusetts USA
*
Correspondence: Inkyu Kim, MD Harvard Medical School Boston, Massachusetts USA E-mail: inkyukim91@gmail.com

Abstract

Objectives:

The aim of this study was to analyze congestive heart failure (CHF) discharges in Florida (USA) post tropical cyclones from 2007 through 2017.

Methods:

This was a retrospective longitudinal time series analysis of hospital CHF quarterly discharges across Florida using the Healthcare Cost and Utilization Project (HCUP) database. The autoregressive integrated moving average (ARIMA) model was used with correlated seasonal regressor variables such as cyclone frequency, maximum cyclone wind speed, average temperature, and reports of influenza-like illness (ILI).

Results:

A total of 3,372,993 patients were identified, with average age in each quarter ranging 72.2 to 73.9 years and overall mortality ranging 4.3% to 6.4%. The CHF discharges within each year peaked from October through December and nadired from April through June with an increasing overall time trend. Significant correlation was found between CHF discharge and the average temperature (P <.001), with approximately 331.8 less CHF discharges (SE = 91.7) per degree of increase in temperature. However, no significant correlation was found between CHF discharges and frequency of cyclones, the maximum wind speed, and reported ILI.

Conclusions:

This study suggests that with the current methods and the HCUP dataset, there is no significant increase in overall CHF discharges in Florida as a result of recent previous cyclone occurrences.

Type
Original Research
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the World Association for Disaster and Emergency Medicine

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References

Ciottone, GR, Biddinger, PD, Darling, RG, et al. Ciottone’s Disaster Medicine. Philadelphia, Pennsylvania USA: Elsevier Mosby; 2015.Google Scholar
Wisner, B, Adams, J. Chapter 5 – Recovery and Sustainable Development. In: Environmental Health in Emergencies and Disasters: A Practical Guide. Geneva, Switzerland: WHO; 1991:p7182.Google Scholar
Task Force on Quality Control of Disaster Management, World Association for Disaster and Emergency Medicine, Nordic Society for Disaster Medicine. Health Disaster Management: Guidelines for Evaluation and Research in the Utstein Style. Volume I. Conceptual Framework of Disasters. Prehosp Disaster Med. 2003;17(Suppl 3):1177.Google Scholar
Redlener, I, Reilly, MJ. Lessons from Sandy – preparing health systems for future disasters. N Engl J Med. 2012;367(24):22692271.CrossRefGoogle ScholarPubMed
Hurricane Katrina facts and information. National Geographic Web site. https://www.nationalgeographic.com/environment/natural-disasters/reference/hurricane-katrina/. Accessed January 21, 2020.Google Scholar
Mazer-Amirshahi, M, Fox, ER. Saline shortages – many causes, no simple solution. N Engl J Med. 2018;378(16):14721474.Google ScholarPubMed
Sharpe, JD, Clennon, JA. Pharmacy functionality during the Hurricane Florence disaster. Disaster Med Public Health Prep. 2019;14(1):93102.CrossRefGoogle Scholar
Mongin, SJ, Baron, SL, Schwartz, RM, Liu, B, Taioli, E, Kim, H. Measuring the impact of disasters using publicly available data: application to 2012 Hurricane Sandy. Am J Epidemiol. 2017;186(11):12901299.Google Scholar
George-McDowell, N, Landron, F, Glenn, J, et al. Deaths associated with Hurricanes Marilyn and Opal - United States, September-October 1995. JAMA. 1996;275(8):586587.Google Scholar
S B, Al E. Morbidity and mortality associated with Hurricane Floyd, North Carolina, September-October 1999. MMWR Morb Mortal Wkly Rep. 2000;49(23):518.Google Scholar
Centers for Disease Control and Prevention (CDC). Deaths associated with Hurricane Sandy - October-November 2012. MMWR Morb Mortal Wkly Rep. 2013;62(20):393397.Google Scholar
Keenan, HT, Marshall, SW, Nocera, MA, Runyan, DK. Increased incidence of inflicted traumatic brain injury in children after a natural disaster. Am J Prev Med. 2004;26(3):189193.CrossRefGoogle ScholarPubMed
Dunne-Sosa, A, Cotter, T. The hidden wounds of Hurricane Dorian: why emergency response must look beyond physical trauma. Disaster Med Public Health Prep. 2019;13(5-6):10921094.CrossRefGoogle ScholarPubMed
Allweiss, P. Diabetes and disasters: recent studies and resources for preparedness. Curr Diab Rep. 2019;19(11):131.CrossRefGoogle ScholarPubMed
Kelman, J, Finne, K, Bogdanov, A, et al. Dialysis care and death following hurricane sandy. Am J Kidney Dis. 2015;65(1):109115.CrossRefGoogle ScholarPubMed
Miller, AC, Arquilla, B. Chronic diseases and natural hazards: impact of disasters on diabetic, renal, and cardiac patients. Prehosp Disaster Med. 2008;23(2):185194.Google ScholarPubMed
An, R, Qiu, Y, Guan, C, Xiang, X, Ji, M. Impact of hurricane Katrina on mental health among US Adults. Am J Health Behav. 2019;43(6):11861199.CrossRefGoogle ScholarPubMed
Espinel, Z, Kossin, JP, Galea, S, Richardson, AS, Shultz, JM. Forecast: increasing mental health consequences from Atlantic hurricanes throughout the 21st Century. Psychiatr Serv. 2019;70(12):11651167.Google ScholarPubMed
Hall, MJ, Leavant, S, DeFrances, CJ. Hospitalization for congestive heart failure: United States, 2000-2010. NCHS Data Brief. 2012;(108):18.Google ScholarPubMed
Babaie, J, Asl, YP, Naghipour, B, Faridaalaee, G. Cardiovascular diseases in natural disasters: a systematic review. Arch Acad Emerg Med. 2021;9(1):116.Google ScholarPubMed
HCUP State Inpatient Databases (SID). Healthcare Cost and Utilization Project (HCUP). Rockville, Maryland USA. www.hcup-us.ahrq.gov/sidoverview.jsp. Accessed January 21, 2021.Google Scholar
World Health Organization. International classification of diseases: [9th] ninth revision, basic tabulation list with alphabetic index. 1978. World Health Organization Web site. https://apps.who.int/iris/handle/10665/39473. Accessed January 21, 2021.Google Scholar
World Health Organization. ICD-10: international statistical classification of diseases and related health problems: tenth revision, 2nd ed. 2004. World Health Organization Web site. https://apps.who.int/iris/handle/10665/42980. Accessed January 21, 2021.Google Scholar
HCUP Quality Control Procedures. American Health Research and Quality Web site. https://www.hcup-us.ahrq.gov/db/quality.jsp. Accessed October 27, 2022.Google Scholar
Mutter, R, Stocks, C. Using Healthcare Cost and Utilization Project (HCUP) data for emergency medicine research. Ann Emerg Med. 2014;64(5):458460.Google ScholarPubMed
2019 Atlantic Hurricane Season. NOAA Web Site. https://www.nhc.noaa.gov/data/tcr/. Accessed January 28, 2020.Google Scholar
Kytömaa, S, Hegde, S, Claggett, B, et al. Association of influenza-like illness activity with hospitalizations for heart failure: the atherosclerosis risk in communities’ study. JAMA Cardiol. 2019;4(4):363369.CrossRefGoogle ScholarPubMed
Panhwar, MS, Kalra, A, Gupta, T, et al. Relation of concomitant heart failure to outcomes in patients hospitalized with influenza. Am J Cardiol. 2019;123(9):14781480.CrossRefGoogle ScholarPubMed
ILINet State Activity Indicator Map. CDC Web Site. https://gis.cdc.gov/grasp/fluview/main.html. Accessed January 28, 2020.Google Scholar
Fares, A. Winter cardiovascular diseases phenomenon. N Am J Med Sci. 2013;5(4):266279.Google ScholarPubMed
Stewart, S, Moholdt, TT, Burrell, LM, et al. Winter peaks in heart failure: an inevitable or preventable consequence of seasonal vulnerability? Card Fail Rev. 2019;5(2):8385.CrossRefGoogle ScholarPubMed
Temperature - Florida Climate Center. https://climatecenter.fsu.edu/products-services/data/statewide-averages/temperature. Accessed January 28, 2020.Google Scholar
Box, GEP. Time Series Analysis: Forecasting and Control. San Francisco, California USA: Holden-Day; 1970.Google Scholar
Locascio, JJ, Jennings, PJ, Moore, CI, Corkin, S. Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging. Hum Brain Mapp. 1997;5(3):168193.Google ScholarPubMed
Bea, K, Halchin, E, Hogue, H, et al. Federal Emergency Management Policy Changes After Hurricane Katrina: A Summary of Statutory Provisions. Washington, DC USA: Library of Congress Washington, DC Congressional Research Service; 2006.Google Scholar
Ahmad Lone, N. What should be the minimum number of observations for a time series model? https://www.researchgate.net/post/What_should_be_the_minimum_number_of_observations_for_a_time_series_model. Accessed January 29, 2020.Google Scholar