Contextual and Individual Characteristics Predicting Individual Health Outcomes for Louisiana

Many scholars in the field of disaster mitigation have studied the impact of individual and contextual indicators on the ability of individuals and communities to adjust, moderate the effects, and cope with disaster disturbances. More specifically, scholars are trying to untangle both the individual and the contextual determinants of good health outcomes for individuals when faced with disasters. This research is extremely relevant in a state like Louisiana who has been affected by disasters for centuries.

A modified version of Andersen’s Behavioral Model of Health Utilization is the conceptual model that drove this study. The model was modified with the disaster resilience of place model as well as the hazards-of-place model of vulnerability used in disaster management literature. According to the model, equitable disaster mitigation would occur when demographic (age, gender) and need variables (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 (contextual social resilience, community capital, individual social vulnerability), and enabling resources (institutional resilience, contextual and individual economic resilience, infrastructure resilience) determined health outcomes for individuals exposed to disaster events.

The study was designed as a multilevel, repeated cross-sectional design with a three-level, nested structure (individuals nested within Louisiana parish cohorts and Louisiana parish cohorts nested within parishes). It used secondary data gathered from the Behavioral Risk Factor Surveillance System for 7 cohorts from 2004-2010. Parish-level data came from 12 additional sources. A representative sample of 34,685 individuals representing Louisiana was used to test the model. Because of the nature of the outcome variable (self-rated health on an ordinal 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. 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.

Overall, 20% of the sample reported fair to poor health in 2004. By 2010, this number has increased to 25%. The results of the model testing clearly indicated inequitable disaster mitigation, with social resilience, individual social vulnerability, individual economic resilience and contextual economic resilience indicators explaining the most variance in the self-rated health levels of parish residents. Disaster risk indicators did play a role in explaining health outcomes, but not as strong as the variables mentioned.

A great deal of disaster research has focused on community disaster resilience, not taking into account the individual and its resilience within the community. By educating social work practitioners on the importance of building resilience not only within communities but within individuals as well, and raising the awareness of the problem of inequitable disaster mitigation, future macro focused social workers can help foster better health outcomes for individuals living in disaster prone areas in the USA.