Long-term Recovery From Hurricane Sandy: Evidence From a Survey in New York City

Disaster Med Public Health Prep. 2018 Apr;12(2):172-175. doi: 10.1017/dmp.2017.57. Epub 2017 Aug 23.

Abstract

Objectives: This study aimed to examine a range of factors influencing the long-term recovery of New York City residents affected by Hurricane Sandy.

Methods: In a series of logistic regressions, we analyzed data from a survey of New York City residents to assess self-reported recovery status from Hurricane Sandy.

Results: General health, displacement from home, and household income had substantial influences on recovery. Individuals with excellent or fair health were more likely to have recovered than were individuals with poor health. Those with high and middle income were more likely to have recovered than were those with low income. Also, individuals who had not experienced a decrease in household income following Hurricane Sandy had higher odds of recovery than the odds for those with decreased income. Additionally, displacement from the home decreased the odds of recovery. Individuals who applied for assistance from the Build it Back program and the Federal Emergency Management Agency had lower odds of recovering than did those who did not apply.

Conclusions: The study outlines the critical importance of health and socioeconomic factors in long-term disaster recovery and highlights the need for increased consideration of those factors in post-disaster interventions and recovery monitoring. More research is needed to assess the effectiveness of state and federal assistance programs, particularly among disadvantaged populations. (Disaster Med Public Health Preparedness. 2018;12:172-175).

Keywords: Hurricane Sandy; New York City; displacement; health; recovery.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Cyclonic Storms*
  • Disaster Victims / rehabilitation*
  • Disaster Victims / statistics & numerical data
  • Health Status
  • Housing / standards
  • Housing / statistics & numerical data
  • Humans
  • Income / statistics & numerical data
  • Logistic Models
  • New York City
  • Public Health / methods*
  • Public Health / statistics & numerical data
  • Socioeconomic Factors
  • Surveys and Questionnaires