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Heterogeneity in Temporal Ordering of Depression and Participation After Traumatic Brain Injury

      Abstract

      Objective

      To examine heterogeneity in the temporal patterns of depression and participation over the first 2 years post traumatic brain injury (TBI).

      Design

      Observational prospective longitudinal study.

      Setting

      Inpatient rehabilitation centers, with 1- and 2-year follow-up conducted primarily by telephone.

      Participants

      Persons with TBI (N=2307) enrolled in the Traumatic Brain Injury Model Systems database, followed at 1 and 2 years post injury.

      Interventions

      Not applicable.

      Main Outcome Measure

      Patient Health Questionnaire-9 (PHQ-9) and Participation Assessment With Recombined Tools–Objective (PART-O).

      Results

      Using latent class modeling we examined heterogeneity in the longitudinal relationship between PHQ-9 and PART-O. The identified 6 classes were most distinct in terms of (1) level of PHQ-9 score and (2) association between the year 1 PART-O score and year 2 PHQ-9 score. For most participants, PART-O at year 1 predicted PHQ-9 at year 2 more than the reverse. However, there was a subgroup of participants that demonstrated the reverse pattern, PHQ-9 predicting later PART-O, who were on average, older and in the “other” employment category.

      Conclusions

      Results suggest that links between participation and depression are stronger for some people living with TBI than for others and that variation in the temporal sequencing of these 2 constructs is associated with demographic characteristics. These findings illustrate the value in accounting for population heterogeneity when evaluating temporal among outcome domains.

      Keywords

      List of abbreviations:

      BIC (Bayesian information criterion), PART-O (Participation Assessment With Recombined Tools–Objective), PHQ-9 (Patient Health Questionnaire-9), PTA (posttraumatic amnesia), TBI (traumatic brain injury), TBIMS (Traumatic Brain Injury Model Systems)
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