The Deepwater Horizon Oil Spill Disaster: Contextual and Individual Characteristics Predicting Individual Resilience Outcomes of Gulf Coast Residents

Background and Purpose: Disasters can affect millions of people, inhibiting people’s psychosocial capacity. In response to the increase in disaster impacts, a multilevel study was undertaken to investigate factors contributing to individual resilience in the Gulf Coast region between 2010-2011.  Recent disaster events such as Hurricane Katrina, and the Deepwater Horizon oil spill have become more common and severe, mainly due to the region being highly vulnerable and susceptible to disaster.

The conceptual model that drove this study was a modified version of Andersen’s Behavioral Model of Health Utilization (Andersen, Rice & Kominiski, 2007). The model was modified with the community disaster resilience model (Cutter et al. 2010). According to this model, equitable disaster mitigation would occur when demographic (e.g. age, gender) and need variables (e.g. contextual disaster risk and population health) accounted for most of the variance in individual health outcomes, while inequitable disaster mitigation would occur when social structure (e.g. contextual social resilience, community capital, individual social vulnerability), and enabling resources (e.g. institutional resilience, contextual and individual economic resilience, infrastructure resilience) determined health outcomes for individuals exposed to the Deepwater Horizon Oil Spill.

Methods: The multilevel study was a repeated cross-sectional design with a three-level, nested structure (individuals nested within Gulf Coast region cohorts and Gulf Coast county cohorts nested within counties). Data was derived from the Centers for Disease Control and Prevention (CDC) Gulf States Population Survey (GSPS). The final sample included 29,480 individuals from Florida, Alabama, Mississippi and Louisiana.  A total of 25 counties affected by the oil spill were included. Because of the nature of the outcome variable (ordinal, Pearlin Self-Mastery Resilience Scale ranging between 1 and 5), a nonlinear analysis was done using an ordered multinomial response model with a log-link function and empirical Bayes Markov chain Monte Carlo (MCMC) estimation (Raudenbush & Bryk, 2002). Steps in model building included (a) fitting the unconditional model, (b) fitting the unconditional growth model, (c) fitting the model with the main effects; and (d) fitting the model with the interaction effects.

Results: Overall, 25% of the sample reported high resilience. The results of the model testing indicated inequitable disaster mitigation, with social resilience, individual social vulnerability, individual economic resilience and contextual economic resilience indicators explaining the most variance in the resilience levels of Gulf Coast residents. Oil Spill exposure indicators did play a role in explaining resilience, but not as strong as the variables mentioned.

Implications and Conclusion: A great deal of disaster research has mainly focused on community indicators such as disaster resilience, and not taking into account the role the individual plays within the community. By focusing on the importance of building disaster resilience from a systems perspective, and raising the awareness of the problem of inequitable disaster mitigation, in-depth empirical investigations incorporating an individual and community approach can help guide decision makers on better resilience outcomes for individuals living in disaster prone areas.