Counting Uncounted Gunshot Injuries: A Capture‐recapture Study Of People Minding Their Own Business
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Objectives. To apply a novel statistical method to create a comprehensive estimate of incidence of firearm related injuries. Methods. A database of firearms injuries in New Haven, Connecticut, during a fivemonth period was created with records from law enforcement, emergency departments, emergency medical services (EMS), news media, and the medical examiner. The overlap of these various sources was operationalized in a capture-recapture model to generate an estimate of uncounted firearms injuries, and log linear modeling was used to control for positive and negative dependencies. Results. The combined data sources revealed 49 firearms injuries occurring during the study period within our defined geographical area. No single source recorded more than 43 of these injuries. Log-linear capture recapture methods estimated that the actual number of injuries was 49.7 (95% CI 49-52.3). Conclusions. No single source reaches complete case ascertainment for firearms injures. Combining multiple sources improves the estimate of injury incidence, but still results in an undercount. Log-linear capture-recapture methods can be used to improve the estimate of firearms injuries. Acknowledgements This
work
could
not
have
been
completed
without
the
hard
work
of
a
number
of individuals.
Thank
you
to
Brandy
Dionne
and
Pilar
Ciravolo
from
the
Yale
Human Research
Protection
Program
for
their
endless
patience.
Thank
you
to
Desmond Walker
for
his
assistance
with
obtaining
data
from
Yale’s
EMR.
Thank
you
to American
Medical
Response-‐New
Haven,
Yale-‐New
Haven
Hospital,
the
Connecticut Medical
Examiner,
and
the
New
Haven
Police
Department
for
graciously
sharing their
records,
without
which
we
could
not
have
undertaken
this
research.
Thank you
to
Christal
Esposito,
Douglas
Barber,
Vanessa
Kuhlor,
and
Michelle
Wu
for
help collecting
data
and
establishing
the
trajectory
of
the
project.
Thank
you
to
David Cone
and
Liudvikas
Jagminas
for
helping
to
access
hospital
and
pre-‐hospital
data. Thank
you
to
Alexei
Nelayev
for
advice
and
assistance
regarding
the
Institutional Review
Board.
Thank
you
to
Richard
Spano
for
helping
to
shape
the
project,
for contributing
data
and
knowledge
regarding
the
police
department,
and
for
many discussions
along
the
way
that
guided
the
development
of
the
research.
Finally, thank
you
to
Lori
Post
for
her
wise
and
careful
oversight
of
the
project.