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A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response

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Abstract

Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents’ health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.

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References

  1. Morrow BH. Identifying and mapping community vulnerability. Disasters 1999;23(1):1–18.

    Article  Google Scholar 

  2. Fernandez LS, Byard D, Lin CC, Benson S, Barbera JA. Frail elderly as disaster victims: emergency management strategies. Prehosp Disaster Med. 2002;17(2):67–74.

    Article  Google Scholar 

  3. Shaughnessy PW, Kramer AM. The increased needs of patients in nursing homes and patients receiving home health care. N Engl J Med. 1990;322(1):21–7.

    Article  Google Scholar 

  4. Dosa D, Feng Z, Hyer K, Brown LM, Thomas K, Mor V. Effects of Hurricane Katrina on nursing facility resident mortality, hospitalization, and functional decline. Disaster Med Public Health Preparedness 2010;4(01):S28.

    Article  Google Scholar 

  5. Dosa D, Hyer K, Thomas K, Swaminathan S, Feng Z, Brown L, Mor V. To evacuate or shelter in place: implications of universal hurricane evacuation policies on nursing home residents. J Am Med Directors Assoc. 2012;13(2):190-e1.

    Article  Google Scholar 

  6. Harris G. In NH, a fight lost to rising waters. New York Times. 2005;7:A1.

  7. Thomas KS, Dosa D, Hyer K, Brown LM, Swaminathan S, Feng Z, Mor V. Effect of forced transitions on the most functionally impaired nursing home residents. J Am Geriatr Soc. 2012;60(10):1895–900.

    Article  Google Scholar 

  8. Baker EJ. Hurricane evacuation behavior. Int J Mass Emerg Disasters 1991;9(2):287–310.

    Google Scholar 

  9. Wolshon B, Urbina E, Wilmot C, Levitan M. Review of policies and practices for hurricane evacuation. I: Transportation planning, preparedness, and response. Nat Hazards Rev. 2005;6(3):129–42.

    Article  Google Scholar 

  10. Whitehead JC, Edwards B, Van Willigen M, Maiolo JR, Wilson K, Smith KT. Heading for higher ground: factors affecting real and hypothetical hurricane evacuation behavior. Glob Environ Change Part B Environ Hazards. 2000;2(4):133–42.

    Article  Google Scholar 

  11. Hasan S, Ukkusuri S, Gladwin H, Murray-Tuite P. Behavioral model to understand household-level hurricane evacuation decision making. J Transp Eng. 2011;137(5):341–8.

    Article  Google Scholar 

  12. Dosa DM, Grossman N, Wetle T, Mor V. To evacuate or not to evacuate: lessons learned from Louisiana nursing home administrators following Hurricanes Katrina and Rita. J Am Med Directors Assoc. 2007;8(3):142–9.

    Article  Google Scholar 

  13. Dosa DM, Hyer K, Brown LM, Artenstein AW, Polivka-West L, Mor V. The controversy inherent in managing frail nursing home residents during complex hurricane emergencies. J Am Med Directors Assoc. 2008;9(8):599–604.

    Article  Google Scholar 

  14. Blanchard G, Dosa D. A comparison of the nursing home evacuation experience between Hurricanes Katrina (2005) and Gustav (2008). J Am Med Directors Assoc. 2009;10(9):639–43.

    Article  Google Scholar 

  15. Brown LM, Dosa DM, Thomas K, Hyer K, Feng Z, Mor V. The effects of evacuation on nursing home residents with dementia. Am J Alzheimer’s Dis Other Dementias ®. 2012;27(6):406–12.

  16. Florida Agency for Healthcare Administration. 2021. Accessed https://ahca.myflorida.com/

  17. Environmental Systems Research Institute (ESRI). ArcGIS Desktop. Redlands: Environmental Systems Research Institute; 2021.

  18. National Oceanic and Atmospheric Administration (NOAA). NHC Data in GIS Formats; 2021. Accessed https://www.nhc.noaa.gov/gis/

  19. Barua DK. Beaufort wind scale. In: Encyclopedia of coastal science. Encyclopedia of earth science series. Dordrecht: Springer; 2005. Accessed https://doi.org/10.1007/1-4020-3880-1_45

  20. Jelesnianski CP. SLOSH: Sea, lake, and overland surges from hurricanes (Vol. 48). US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service; 1992.

  21. University of Florida GeoPlan Center. Florida Digital Elevation Model (DEM) Mosaic; 2012. Accessed https://www.fgdl.org/metadata/fgdl_html/flidar_mosaic_m.htm

  22. Centers for Medicare and Medicaid Services (CMS). Certification and Survey Provider Enhanced Reports (CASPER); 2017. Accessed https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Public-Reporting

  23. Florida Health Care Association. National criteria for evacuation decision-making in nursing homes. Tallahassee: Florida Health Care Association; 2008.

  24. Brown LM, Hyer K, Polivka-West L. A comparative study of laws, rules, codes and other influences on nursing homes’ disaster preparedness in the Gulf Coast states. Behav Sci Law. 2007;25(5):655–75.

    Article  Google Scholar 

  25. Jester DJ, Thomas KS, Peterson LJ, Dosa DM, Andel R, Hyer K. Effect of Hurricane Irma on daily direct-care nurse staffing in nursing homes. J Am Geriatr Soc. 2021;69(8):2298–305.

    Article  Google Scholar 

  26. Konetzka RT, Stearns SC, Park J. The staffing-outcomes relationship in nursing homes. Health Serv Res. 2008;43(3):1025–42.

    Article  Google Scholar 

  27. Cowles CM. Nursing home statistical yearbook, 2002. Montgomery Village: Cowles Research Group; 2002.

  28. Hyer K, Thomas KS, Branch LG, Harman JS, Johnson CE, Weech-Maldonado R. The influence of nurse staffing levels on quality of care in nursing homes. Gerontologist. 2011;51(5):610–6.

    Article  Google Scholar 

  29. Chawla NV, Japkowicz N, Kotcz A. Special issue on learning from imbalanced data sets. ACM SIGKDD Explor Newslett. 2004;6(1):1–6.

    Article  Google Scholar 

  30. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

    Article  MATH  Google Scholar 

  31. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2009.

    Book  MATH  Google Scholar 

  32. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.

    MATH  Google Scholar 

  33. National Weather Service (NWS). Hurricane Irma Local Report/Summary; 2017. Accessed https://www.weather.gov/mfl/hurricaneirma

  34. LaVito A. Irma may have caused 42.5 billion to 65 billion in property damage, report says. CNBC; 2017. Accessed https://www.cnbc.com/2017/09/19/irma-may-have-caused-42-point-5-billion-to-65-billion-in-property-damage-report.html

  35. Natarajan N. Coastal storm planning, the Healthcare Facility Evacuation Center (HEC), and Patient Tracking. New York State; 2013.

  36. Chen X, Meaker JW, Zhan FB. Agent-based modeling and analysis of hurricane evacuation procedures for the Florida Keys. Nat Hazards. 2006;38(3):321.

    Article  Google Scholar 

  37. Breiman L, Cutler A. Random forest-manual; 2004. Accessed https://www.stat.berkeley.edu/~breiman/RandomForests/cc_manual.htm

  38. Castle NG. Nursing home evacuation plans. Am J Public Health. 2008;98(7):1235–40.

    Article  Google Scholar 

  39. Lindell MK, Prater CS, Peacock WG. Organizational communication and decision making for hurricane emergencies. Nat Hazards Rev. 2007;8(3):50–60.

    Article  Google Scholar 

  40. Laditka SB, Laditka JN, Xirasagar S, Cornman CB, Davis CB, Richter JV. Providing shelter to nursing home evacuees in disasters: lessons from Hurricane Katrina. Am J Public Health. 2008;98(7):1288–93.

    Article  Google Scholar 

  41. Claver M, Dobalian A, Fickel JJ, Ricci KA, Mallers MH. Comprehensive care for vulnerable elderly veterans during disasters. Arch Gerontol Geriatr. 2013;56(1):205–13.

    Article  Google Scholar 

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Funding

This study was supoorted by National Institute on Aging (Grant No. R01AG060581) and Division of Civil, Mechanical and Manufacturing Innovation (Grant Nos. 1825761 and 1825725).

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, 33620, USA

    Nazmus Sakib & Mingyang Li

  2. School of Aging Studies, University of South Florida, Tampa, FL, 33620, USA

    Kathryn Hyer, Debra Dobbs & Lindsay Peterson

  3. Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA

    Dylan J. Jester

  4. Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, 92093, USA

    Dylan J. Jester

  5. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Nan Kong

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  1. Nazmus Sakib
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  2. Kathryn Hyer
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  4. Lindsay Peterson
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  6. Nan Kong
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  7. Mingyang Li
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Correspondence to Mingyang Li.

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Sakib, N., Hyer, K., Dobbs, D. et al. A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response. Health Inf Sci Syst 10, 28 (2022). https://doi.org/10.1007/s13755-022-00190-y

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