Multilevel Interventions in Health Care Conference: Presentation by Joseph Morrissey, PhD

Multilevel Interventions in Health Care Conference: Presentation by Joseph Morrissey, PhD


>>>DR. STEVE CLAUSER: In
continuing our theme on research methods and design
issues, we are now going to move to two new
presentations. The first one is by Joe Morrissey. He’s a
sociologist with interests in interorganizational
network analysis, system assessment,
program evaluations, health utilization, health
outcomes. And he’s been involved in a number of
foundation and federal agency multi-site
research demonstrations throughout the U.S. He’s
presently a professor of health policy
and management at the Gillings School of Global
Public Health, and he’s Deputy Director of Research at
the Cecil Sheps Center for Health Services Research at the
University of North Carolina in Chapel Hill. So without
further ado, Dr. Morrisey.>>>[APPLAUSE]>>>DR. JOE MORRISEY: Thank
you. I’d like to start by introducing my colleagues
on the paper. Christine Hasmiller Litsch
is a colleague of mine in Chapel Hill. She’s an engineer
and an operational researcher. Rebecca Ann Hank
Price is a health policy, health services researcher
currently at the Rand Corporation, and formerly
was at the National Cancer Institute working with both of
the Steve’s and with part of the planning group
for this conference. And Gene Mendelblatt is a
physician oncologist and a simulation modeler at
Georgetown University. And we tried to sort of pool
our thoughts and come up with some observations on the
role of computer simulation modeling in this context. So
again, the idea of simulation modelings is usually
mathematical representations to portray cancer in the dynamic
multifaceted influences that we’ve been talking
about all morning. There are a whole family of
things that go under the name of simulation, it’s not
one strategy. It’s not like hierarchical linear modeling
or linear regression. There are a whole bunch of
models that are simulations. You can sort of look at the
diversity by looking at these four parameters that get built
into different models as to whether they’re
Socratic or deterministic, whether they’re focusing on
a steady state situation or trying to model the
dynamic influences. Whether the outcome variables
are continuous or discrete event simulations, and whether
they’re local or distributed in the sense of the computer
networks that may be involved in building the models.
Simulation, of course, can be applied to all of these
different levels that we’ve been talking about. You can
simulate policy options, choices, resource allocations
at the organizational level, you can simulate organizational
strategies with regard to marketplace or
interventions or treatments. You can treat at risk
populations as aggregates, you can look at individuals
within those populations, you can look at
events, screenings, assessments,
outcomes, mortality. You can look at, beneath the
skin or biological models. One of the things that we did
in this paper which broke a little bit from the multi-level
definitions and so forth that we were talking about which
tend to focus on the social policy organizational
intervention levels, we spent a lot of time trying
to understand the connections between those levels
and biological levels. Oncologists who are working in
the biological level talk about scales rather than
levels, and they differentiate, you could have a clam shell
very similar to what we have here for the biological levels
going from the monocular up to the person level. And so
simulation becomes a way of helping measure things at
different levels and try to see their mutuality, or
synergy to use Brian’s term. And so the question is in
this context, why simulate? And there are a number of
both practical and theoretical reasons that we point to.
One of the most basic ones is that simulation is a way of
doing numerical or virtual experience in situations when
real ones can’t be done. Despite the obvious strengths
of the experimental model and randomization that
we’re all familiar with, very often in terms of the
kinds of questions you want to get at, and this came up
several times this morning, it’s just impossible to
randomize the units of observations of the situations
that we’re looking at. As I said, this is a strategy
for bridging above the skin the social-ecological level, and
below the skin influences. And it’s a way of sort of
combining multiple data sets and time periods to create
realistic estimates when forming policy making as
well as helping patients make individual choices. On
the theory side, people talk to the heuristic
value of simulations. It’s a way of sort of
generating a variety of what if scenarios that sort of go
beyond the immediate data that we have and to try to
specify what connections that we might expect to occur.
So it’s a way of generating hypotheses and theories
about the mechanisms in the causal influences that may
be producing the effects that we are observing. From
an intervention strategy simulation is another kind of
powerful way of identifying leverage points in a
system of influences, to try to determine
at what point in an intervention might have the
greatest impact on system outcomes, and then to be
able to quantify those system outcomes and impacts.
Identifying gaps most likely to alter intervention strategies,
and estimating the value of obtaining better information
in a particular situation. Cancer control
simulations are not new. CISNET, the cancer intervention
surveillance network that the Cancer Institute has created
about a dozen years or so ago, has led many of the
advances in this area. But most of the models deal
with only one or two versus three or more levels as we
have mentioned this morning. The core of our paper
identifies four models all of which I believe have
CISNET connections to them, that use simulation, that
deal with one or two levels. At least four have
biological components to them. And we discuss ways in which
socio-ecological measures and variables could be added to
these models to sort of bump up to the level of multi-level
as we have been talking about this morning. Tobacco
control, looking at David Levy’s Sim Smoke
model which is perhaps one of the most elaborated
socio-ecological up to the policy level. The MISCAN
model and colorectal cancer screening, the cervical
cancer screening where (inaud.) is done, and my
co-author and colleague Gene Mendelblatt work on
breast cancer and racial disparities with regard
to access to care. So why haven’t we
made more use of this, why haven’t we
made more progress, why don’t we have more
simulation models to deal with multi-levels? And so the
paper ends up identifying three buckets, three categories
of challenges we face to try to figure out how to better
apply simulation approaches. The first one is a data problem,
and again we’ve spoken about this several times. Much
of the data that are needed to measure the causal
relationships that we’re interested in do not exist
or are very fragmented. They exist in different places
for different populations, different samples and so
forth, and it’s difficult to aggregate them together. We’ve
talked about the challenges of figuring out how to integrate
data and to measure the interactions between patients,
providers, policies, and then try to figure out,
once we’ve constructed a model, how do we about validating
the results of that model. To really get at the levels
of complexity that we’ve been talking about, much more
efficient computational algorithms and distributed
computer networks are going to be needed to begin
processing the amount of time and effort to deal with
these complex models. So for anyone sort of starting
out wanting to use simulation as a research strategy in
this area, there’s really a substantial learning curve to
try to understand and apply those techniques. There are a
number of structural challenges here as well. We talk about
multi-disciplinary teams. Here I think there’s a need
to have the whole spectrum of individuals going from
basic scientists to clinical oncologists, to health
services researchers, to system modelers. All of
them need to be working on the same research teams if
we’re going to be able to make the kinds of connections that
we’ve been talking about. A real shortage of training
programs around the country that are specific to cancer
or other health conditions. The home of simulation
modeling tends to be in engineering schools around
the country and with very few applications, with some
exceptions. There’s work at the University of Michigan and
at Harvard in these areas. But in general
there is a shortage. It’s difficult to find a
place to go to get the kind of training that would be needed
to really advance this effort. We talked a little
bit about grants, but there’s really a lack of
a grant review and funding infrastructure specific to
the modeling disciplines. In other words if you want to
apply to get funding for these things through NIH you’ve
got to go through the regular committees and so forth, and
we’ve talked a little bit this morning about how you don’t
often get the kind of review and sensitive review because
people are not focused on this particular area of work.
Without an infrastructure of grant funding and
program announcements, it’s difficult to imagine
advances in this area. Over the last decade or so the
Office of Behavioral and Social Science Research at NIH
has begun to do a number of promising things in this area.
They have an R21 systems methods RFA out, they have
been encouraging systems approaches, not only simulation
modeling but network analysis, system dynamic modeling.
They’ve developed a special relationship recently with
the University of Michigan again looking at tobacco
control and trying to bring system perspectives on that.
So I think a lot of that work is an indication of the
kinds of things that are sort of needed. But to really
expect much progress here, I think these kinds of
opportunities need to be opened and expanded. And then
communication challenges. There are diverse array of
specialties and disciplines that are doing
simulation modeling. Many of them have
their own language. It’s difficult to describe
things in a common framework and to understand
across those research areas. And some effort to sort of
bring that together in kind of a commonality would be a
real basis for moving forward. I think CISNET, the Cancer
Intervention Surveillance Network which is a
consortium investigators, both domestically and
internationally that the Cancer Institute has sponsored
over the past 10 or 12 years, is an exemplar of the kind of
learning community that would really be required to
sort of make these advances. The question we ended up with
is the idea that you can build complex multi-level models.
That’s readily easy to do. The question is, can we really
get anybody to believe them in terms of personal
decision making, having patients deciding between
alternative interventions. Getting physicians in terms of
clinical practice to follow the implications of these
modelings. And how do we use these models to try and gain
the kind of national effort and support we need to support
cancer control interventions. I think that’s a big
challenge. Thank you.>>>[APPLAUSE]

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