  Opioid overdose rates have increased in the United States over the past
decade and reflect a major public health crisis. Modeling and prediction of
drug and opioid hotspots, where a high percentage of events fall in a small
percentage of space-time, could help better focus limited social and health
services. In this work we present a spatial-temporal point process model for
drug overdose clustering. The data input into the model comes from two
heterogeneous sources: 1) high volume emergency medical calls for service (EMS)
records containing location and time, but no information on the type of
non-fatal overdose and 2) fatal overdose toxicology reports from the coroner
containing location and high-dimensional information from the toxicology screen
on the drugs present at the time of death. We first use non-negative matrix
factorization to cluster toxicology reports into drug overdose categories and
we then develop an EM algorithm for integrating the two heterogeneous data
sets, where the mark corresponding to overdose category is inferred for the EMS
data and the high volume EMS data is used to more accurately predict drug
overdose death hotspots. We apply the algorithm to drug overdose data from
Indianapolis, showing that the point process defined on the integrated data
outperforms point processes that use only homogeneous EMS (AUC improvement .72
to .8) or coroner data (AUC improvement .81 to .85).We also investigate the
extent to which overdoses are contagious, as a function of the type of
overdose, while controlling for exogenous fluctuations in the background rate
that might also contribute to clustering. We find that drug and opioid overdose
deaths exhibit significant excitation, with branching ratio ranging from .72 to
.98.
