Predicting Major Depressive Disorder In Disaster Victims
Major depressive disorder (MDD) is a relatively common psychiatric illness that is one of the major sources of disability worldwide and may lead to suicide. MDD is a well-known consequence of disaster trauma, typically second in psychiatric frequency only to posttraumatic stress disorder (PTSD). Disaster studies using full diagnostic assessments have identified MDD in 14% of directly-exposed survivors within the first few weeks to months afterward.
Thus, MDD is an important outcome of disasters that warrants public health attention to address the needs of disaster-exposed populations. Treatment is generally effective for this illness, but unfortunately, many cases go unrecognized and untreated.
One review study found the risk for MDD after terrorist attacks to be associated with female sex, injury in the incident, other stressful life events, and loss of psychosocial resources. Research to identify risk for MDD more generally after disasters has been hampered by lack of diagnostic rigor and methodological inconsistency. Research is needed to find better ways to identify those individuals who are most likely to develop MDD after a disaster so that groups with the highest risk can be targeted for systematic assessment and interventions.
We conducted a series of studies with 811 directly-exposed survivors of 10 major disasters of different types, using full diagnostic assessment and consistent methodology across these studies. All types of disasters were represented, including natural disasters (floods, tornado, earthquake), technological accidents (plane crash into hotel, firestorm), and intentionally-caused disasters (four multiple-shooting episodes, a terrorist bombing). We conducted a similar study with 379 employees of New York City organizations affected by the September 11, 2001 (9/11) terrorist attacks, 169 of whom had direct exposure to the disaster trauma.
In the 10-disaster dataset, we predicted the occurrence of postdisaster MDD from several characteristics of the disasters and the survivors examined together within statistical models, and we found that 3 variables predicted postdisaster MDD independent of one another’s influence: predisaster MDD, disaster-related PTSD, and indirect exposure through the disaster experience of a close friend or family member. We then applied the findings from the 10-disaster model to the 9/11 disaster dataset by constructing and testing a similar model using the same 3 variables in the 9/11 data that were significant predictors of MDD in the 10-disaster model. The 9/11 model replicated the results of the 10-disaster model: the same 3 variables from the 10-disaster model significantly predicted postdisaster MDD in the 9/11 dataset. Both the 10-disaster and the 9/11 statistical models discriminated well between survivors with and without postdisaster MDD and demonstrated good fit to the data. Further, the 10-disaster 3-variable model predicted postdisaster MDD from equivalent variables in the 9/11 dataset with a sensitivity of .14 and specificity of .99. Additionally, testing of the application of the 10-disaster model’s performance to the 9/11 model demonstrated a strong correlation.
We then conversely tested the ability of the 3-variable 9/11 model to predict postdisaster MDD in the 10-disaster data using the same procedures. The 9/11 model predicted postdisaster MDD in the 10-disaster model with a sensitivity of .35 and specificity of .98. Testing of the application of the 9/11 model’s performance to the 10-disaster model also demonstrated a strong correlation.
Next, we tested the ability of the 3 individual variables in the 10-disaster and 9/11 models to predict postdisaster MDD. In the 10-disaster data, lifetime predisaster MDD was diagnosed in 57% of those with vs. 7% of those without postdisaster MDD, 44% of those with and 6% of those without disaster-related PTSD, and 19% of those with vs. 12% of those without indirect exposure through the disaster experience of a close friend or family member. In the 9/11 data, postdisaster MDD was diagnosed in 63% of those with and 15% of those without predisaster MDD, 63% of those with and 24% of those without disaster-related PTSD, and 45% of those with vs. 27% of those without indirect exposure through the disaster experience of a close friend or family member.
In the 10-disaster data, postdisaster MDD was diagnosed in 2% of those with 0 positives of the 3 possible predictor variables, 12% with ≥1, 47% with ≥2, and 94% with ≥3. In the 9/11 data, postdisaster MDD was diagnosed in 7% with 0 positive of 3 possible predictor variables, 55% with ≥1, 73% with ≥2, and 80% with ≥3. The count of these positive variables significantly predicted postdisaster MDD in both the 10-disaster and 9/11 databases. The prediction of MDD had a sensitivity (i.e., true positive rate) of about 90% for any positive on >1 of the 3 predictor variables and a specificity (i.e., true negative rate) of about 90%-100% for negatives on >2 or >3 of the 3 predictor variables.
These rates suggest clinical utility for the use of these 3 items to predict postdisaster MDD in post-disaster settings. Based on these findings, about 90% of people with MDD could be identified through detection of at least one positive on any of the 3 predictive variables (predisaster MDD, disaster-related PTSD, and indirect exposure through the disaster experience of a close friend or family member). Additionally, about 90%-100% of people without MDD could be safely excluded from consideration of the diagnosis if they did not have at least 2 or 3 positives on the 3 predictor variables.
Finally, we combined the 9/11 and 10-disaster databases into a single database representing all 11 disasters and including a total of 980 directly-exposed disaster survivors for final modeling. In this model, a variable indicating terrorist disaster typology was also independently predictive of MDD along with the previous 3 predictive variables (predisaster lifetime major depressive disorder, disaster-related PTSD, and indirect exposure through the disaster experience of a close friend or family member). This model discriminated well between survivors with and without postdisaster MDD and demonstrated a good fit to the data. Substituting human-caused and intentionally-caused disasters respectively for terrorist incidents in these models found, however, that these alternative disaster typologies did not independently predict postdisaster MDD.
In summary, we found that postdisaster MDD was independently associated with pre-existing MDD, disaster-related PTSD, and indirect exposure through the disaster experience of a close friend or family member in one dataset and replicated it in another. These results add confidence to the predictive value of these three variables for the prediction of MDD after a disaster. Additionally, terrorism appears to further predict MDD even beyond the likelihood established by these other predictors.
These findings are described in the article entitled Prevalence and predictors of postdisaster major depression: Convergence of evidence from 11 disaster studies using consistent methods, recently published in the Journal of Psychiatric Research. This work was conducted by Carol S. North from the University of Texas Southwestern Medical Center, and David Baron and Anthony F. Chen from the Keck School of Medicine.