Markov models for longitudinal course of youth bipolar disorder.

In this dissertation, mixture of first order Markov chains and Hidden Markov models were used to model variable length sequences in order to find longitudinal patterns.

Data from the Course and Outcome of Bipolar Youth (COBY) study was used to estimate these models.

A mixture of four first order Markov chains found patterns of movers and stayers.

Cluster 4 is the stayers. Cluster 3 are movers among the depression, well and submania states.

Cluster 2 are movers that tend to stay in the well state.

Cluster 1 are movers that tend to go to the submania/subdepression state.

On the other hand, a hidden Markov model with ten hidden states justifies the use of a scale with syndromal, subsyndromal and asymptomatic episodes defined by psychiatrists.

The inclusion of covariates in hidden Markov models showed that: males move more than females, children move more than teenagers, and patients who live in another situation move more than patients who live with both natural parents.

For bipolar diagnosis, BPII and BPNOS patients show similar transition patterns.

Age of bipolar onset sheds light on the stability of patients with a childhood and an early adolescence onset.

Thus, the possibility of an early diagnosis of the disorder would consequently lead to provide appropriate treatment, and that would lessen the impairment of bipolar youth.

Socio-economic status showed patients with low socio-economic status staying more weeks with subsyndromal submanic and mixed episodes, and less weeks with subsyndromal depression and asymptomatic episodes.

Quite the opposite behavior observed for their counterparts in with high socio-economic status.

This is the first research using these two Markov models to analyze the longitudinal course of bipolar disorder in children and adolescents.

No previous study has modeled the longitudinal course of bipolar disorder using Markov models that estimate the transitions among the different episodes of bipolar disorder.

Furthermore, no previous study has modeled the effects of covariates consistently with the longitudinal nature of the disease.