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Measuring Individual Disaster Recovery: A Socioecological Framework

Published online by Cambridge University Press:  08 April 2013

David M. Abramson ,
Tasha Stehling-Ariza ,
Yoon Soo Park ,
Lauren Walsh  and
Derrin Culp
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Abstract

Background: Disaster recovery is a complex phenomenon. Too often, recovery is measured in singular fashion, such as quantifying rebuilt infrastructure or lifelines, without taking in to account the affected population's individual and community recovery. A comprehensive framework is needed that encompasses a much broader and far-reaching construct with multiple underlying dimensions and numerous causal pathways; without the consideration of a comprehensive framework that investigates relationships between these factors, an accurate measurement of recovery may not be valid. This study proposes a model that encapsulates these ideas into a single framework, the Socio-Ecological Model of Recovery.

Methods: Using confirmatory factor analysis, an operational measure of recovery was developed and validated using the five measures of housing stability, economic stability, physical health, mental health, and social role adaptation. The data were drawn from a sample of displaced households following Hurricane Katrina. Measures of psychological strength, risk, disaster exposure, neighborhood contextual effects, and formal and informal help were modeled to examine their direct and indirect effects on recovery using a structural equation model.

Findings: All five elements of the recovery measure were positively correlated with a latent measure of recovery, although mental health and social role adaptation displayed the strongest associations. An individual's psychological strength had the greatest association with positive recovery, followed by having a household income greater than $20 000 and having informal social support. Those factors most strongly associated with an absence of recovery included the time displaced since the hurricane, being disabled, and living in a community with substantial social disorder.

Discussion: The socio-ecological framework provides a robust means for measuring recovery, and for testing those factors associated with the presence or absence of recovery.

(Disaster Med Public Health Preparedness. 2010;4:S46-S54)

Keywords

recoverysocioecological modelmediatorsstructural equation model
Type
Original Article
Information
Disaster Medicine and Public Health Preparedness , Volume 4 , Issue S1 , September 2010 , pp. S46 - S54
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2010

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References

REFERENCES

1.Cutter, SL, Boruff, BJ, Shirley, WL.Social vulnerability to environmental hazards. Soc Sci Q. 2003;84:242–261.CrossRefGoogle Scholar
2.Green, B, Solomon, S.The mental health impact of natural and technological disasters. In: Hobfoll JFaS, ed. Traumatic Stress: From Theory to Practice. New York: Plenum; 1995:163-180.Google Scholar
3.Benight, CC, Bandura, A.Social cognitive theory of posttraumatic recovery: the role of perceived self-efficacy. Behav Res Ther. 2004;42 (10):1129–1148.Google Scholar
4.Norris, FH, Stevens, SP, Pfefferbaum, B, Wyche, KF, Pfefferbaum, RL.Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. Am J Community Psychol. 2008;41 (1-2):127–150.Google Scholar
5.Bates, FL, Pelanda, C.An ecological approach to disasters. In: Dynes RR, Tierney KJ, eds. Disasters, Collective Behavior, and Social Organization. Newark: University of Delaware Press; 1993:145-159.Google Scholar
6.Edwards, MLK.An interdisciplinary perspective on disasters and stress: the promise of an ecological framework. Sociol Forum. 1998;13:115–132.CrossRefGoogle Scholar
7.Kothari, A, Edwards, N, Yanicki, S, et alSocioecological models: strengthening intervention research in tobacco control. Drogues Sante Societe. 2007;6:iii5–iii6.Google Scholar
8.Tatsuki, S.Long-term life recovery processes among survivors of the 1995 Kobe earthquake: 1999, 2001, 2003, and 2005 Life Recovery Social Survey results. J Disaster Res. 2007;2:484–501.CrossRefGoogle Scholar
9.Koshiyama, K.Analysis of sense of recovery of residents living in disaster recovery public housing. J Inst Soc Safety Sci. 2003;3:237–244.Google Scholar
10.Tatsuki, S, Hayashi, H.Family system adjustment and adaptive reconstruction of social reality among the 1995 earthquake survivors. Int J Jpn Sociol. 2000;9:81–110.CrossRefGoogle Scholar
11.Bratt, RG.Housing and family well-being. Housing Stud. 2002;17:13–26.Google Scholar
12.Peacock, WG, Dash, N, Zhang, Y.Sheltering and housing recovery following disaster. In: Rodriguez H, Quarantelli EL, Dynes RR, eds. Handbook of Disaster Research. New York: Springer; 2006:258-274.Google Scholar
13.Fothergill, A, Peek, LA.Poverty and disasters in the United States: a review of recent sociological findings. Nat Hazards. 2004;32:89–110.Google Scholar
14.Enarson, E, Morrow, BH.The Gendered Terrain of Disaster: Through Women's Eyes. Westport, CT: Praeger.; 1998.Google Scholar
15.Morrow, BH.Stretching the bonds: the families of Andrew. In: Peacock WG, Morrow BH, Gladwin H, eds. Hurricane Andrew: Ethnicity, Gender and the Sociology of Disasters. London: Routledge; 1997:141-170.Google Scholar
16.National Research Council. Facing Hazards and Disasters: Understanding Human Dimensions. Washington, DC: National Academies Press.; 2006.Google Scholar
17.Smith, GP, Wenger, D.Sustainable disaster recovery: operationalizing an existing agenda. In: Rodriguez H, Quarantelli EL, Dynes RR, eds. Handbook of Disaster Research. New York: Springer; 2006:234-257.Google Scholar
18.Heller, T.The effects of involuntary residential relocation: a review. Am J Community Psychol. 1982;10 (4):471–492.CrossRefGoogle ScholarPubMed
19.Reich, JW.Three psychological principles of resilience in natural disasters. Disaster Prev Manage. 2006;15:793–798.Google Scholar
20.Silove, D, Steel, Z.Understanding community psychosocial needs after disasters: implications for mental health services. J Postgrad Med. 2006;52 (2):121–125.Google ScholarPubMed
21.Chamlee-Wright, ER.The long road back: signal noise in the post-Katrina context. Independent Rev. 2007;12:235–259.Google Scholar
22.Chamlee-Wright, ER, Daniel, M.Disastrous Uncertainty: How Government Disaster Policy Undermines Community Rebound. Mercatus Policy Series, Policy Comment No. 9. Arlington, VA: Mercatus Center at George Mason University.; 2007:29.Google Scholar
23.Abramson, D, Stehling-Ariza, T, Garfield, R, Redlener, I.Prevalence and predictors of mental health distress post-Katrina: findings from the Gulf Coast Child and Family Health Study. Disaster Med Public Health Prep. 2008;2 (2):77–86.Google Scholar
24.Lee, SY.Structural Equation Modeling: A Bayesian Approach. New York: Wiley.; 2007.Google Scholar
25.Ware, JE Jr, Kosinski, M, Keller, SD.A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34 (3):220–233.Google Scholar
26.Ware, JE JrImprovements in short-form measures of health status: introduction to a series. J Clin Epidemiol. 2008;61 (1):1–5.CrossRefGoogle ScholarPubMed
27.Bollen, KA.Structural Equations with Latent Variables. New York: John Wiley & Sons.; 1989.Google Scholar
28.Schwarzer, R, Jerusalem, S.Generalized self-efficacy scale. In: Weinman J, Wright S, Johnston M, eds. Measures in Health Psychology: A User's Portfolio. Causal and Control Beliefs. Windsor, UK: NFER-NELSON; 1995:35-37.Google Scholar
29.Allen, MJ, Yen, WM.Introduction to Measurement Theory. Long Grove, IL: Waveland Press.; 2002.Google Scholar
30.Cook, TD, Campbell, DT.Quasi-Experimentation: Design and Analysis Issues in Field Settings. Boston: Houghton Mifflin.; 1979.Google Scholar
31.Bagozzi, RP, Yi, Y, Phillips, LW.Assessing construct validity in organizational research. Adm Sci Q. 1991;36:421–458.Google Scholar
32.Hu, LT, Bentler, PM.Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6:1–55.Google Scholar