  We consider the problem of individual-specific medication level
recommendation (initiation, removal, increase, or decrease) for asthma
sufferers. Asthma is one of the most common chronic diseases in both adults and
children, affecting 8% of the US population and costing $37-63 billion/year in
the US. Asthma is a complex disease, whose symptoms may wax and wane, making it
difficult for clinicians to predict outcomes and prognosis. Improved ability to
predict prognosis can inform decision making and may promote conversations
between clinician and provider around optimizing medication therapy. Data from
the US Medical Expenditure Panel Survey (MEPS) years 2000-2010 were used to fit
a longitudinal model for a multivariate response of adverse events (Emergency
Department or In-patient visits, excessive rescue inhaler use, and oral steroid
use). To reduce bias in the estimation of medication effects, medication level
was treated as a latent process which was restricted to be consistent with
prescription refill data. This approach is demonstrated to be effective in the
MEPS cohort via predictions on a validation hold out set and a synthetic data
simulation study. This framework can be easily generalized to medication
decisions for other conditions as well.
