“Wrong answers: When Simple Interpretations Create Complex Problems for Addiction Science Research and Policy”

Name: “Wrong answers: When Simple Interpretations Create Complex Problems for Addiction Science Research and Policy”
Uploaded: 2026-03-26T19:33:42.7333333Z
Duration: 55 min 47 s
Description: David S. Fink, PhD - Yale School of Medicine March 5, 2026 Yale GIM “Research in Progress” Meeting Presented by: Yale School of Medicine’s Department of Internal Medicine, Section of General Internal Medicine

March 26, 2026

David S. Fink, PhD - Yale School of Medicine

March 5, 2026

Yale GIM “Research in Progress” Meeting Presented by: Yale School of Medicine’s Department of Internal Medicine, Section of General Internal Medicine

About the speakers

David S. Fink, PhD
Assistant Professor of Medicine (General Medicine)

Information

ID: 14008
To Cite: DCA Citation Guide

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00:11Okay. Welcome everyone
00:14to General Medicine Noon Conference.
00:17CME code for today is
00:18five five nine zero five.
00:22Upcoming,
00:23retreat is, well, next one
00:26is May twenty ninth. I
00:27have a little bit of
00:27time on that. Educational retreat.
00:29Please watch out for information
00:31and opportunities
00:33to sign up.
00:35This is our weekly
00:37is it bouncing for you?
00:38It's not just my okay.
00:40Our weekly bouncing reminder for
00:42the, your FDACs.
00:45We are now entering the
00:46phase where the senior faculty
00:48are meeting and,
00:49trying to come up with
00:50good advice and suggestions for
00:52everybody and then watch out
00:53for opportunities to meet with
00:55your meet with your mentors.
01:00Upcoming research and progress in
01:02grand rounds.
01:03Next week,
01:05doctor Nikkayan will be speaking
01:07about interventional psychiatric treatments,
01:10and other modalities that we
01:12or at least they someone
01:13is using in clinic and,
01:14and in the hospital.
01:16And then next week, we'll
01:17have our section faculty and
01:19staff meeting.
01:23Disclosures.
01:26Okay.
01:28Excited now to, present doctor
01:30David,
01:31Fink,
01:32who, was originally,
01:34an undergrad,
01:35at University of California at
01:37San Diego.
01:38Oh, sorry. San Diego State
01:40University,
01:41in,
01:43biostatistics
01:44and epidemiology
01:45before getting his PhD at
01:47Columbia,
01:48where he focused on understanding
01:50what state level policies,
01:52are affecting,
01:53the care and out outcomes
01:55for patients who are using
01:56a variety of substances using
01:57a prescript looking at prescription
01:59drug monitoring programs at the
02:00state level as well
02:02as legalization of marijuana. So
02:03very timely,
02:05and important interventions to be
02:07studying.
02:08But when doing those types
02:10of studies, critical
02:12area of focus is the
02:14methods and understanding more about
02:16causal inference, which is something
02:17that a lot of us
02:18have, thought about and struggled
02:20with over the years.
02:23Specifically,
02:25how doctor Fink is applying
02:26those, is as as a
02:28substance use and psychiatric
02:30epidemiologist.
02:31His research broadly aims to
02:33develop and apply rigorous
02:35causal inference
02:36methodologies
02:37to study the causes of
02:38addiction and mental illness with
02:40a particular focus on estimating
02:42the effects of federal and
02:43state policies and programs.
02:45His research in mental health,
02:47substance use and health policy
02:49are united by a desire
02:50to not only understand,
02:52but reshape the structural, societal,
02:54and interpersonal factors that shape
02:56health and well-being over the
02:58life course.
02:59So we're really excited to
03:00have doctor Fink with us
03:01here at Yale in general,
03:04but also here at our
03:05noon conference to talk to
03:06us about,
03:09wrong answers when simple
03:11interpretations
03:12create complex problems for addiction
03:14science and policy.
03:15So welcome. Thank you.
03:22No.
03:23Thank you for the introduction.
03:26And thank you all for
03:27for being here on this
03:28pretty, rainy, crummy day, for
03:31and the people on Zoom
03:32who stayed home.
03:33So when I when I
03:35joined the department about a
03:35year and a half ago,
03:36I was I was pretty
03:37overwhelmed, I think, by
03:39the size of it. Even
03:40though I'd come from Columbia
03:41Psychiatry, which is about three
03:43hundred people, it just felt
03:44so different when you were
03:45there for a decade versus
03:46coming somewhere new.
03:48These Thursday meetings have been
03:49a great way for me
03:50to get to know people
03:51and know the department a
03:52little bit. So I really
03:53appreciate that. And this was,
03:55I was very happy to
03:56be asked to speak here
03:57and share my work with
03:59you.
04:00So the the title of
04:01my talk is wrong answers
04:02when simple interpretations
04:04create complex problems.
04:06But to set the stage
04:08for this talk, I think
04:09it's
04:09most important.
04:11And since people don't aren't
04:12familiar with me to to
04:13go back a step and
04:14actually actually talk a little
04:16bit how I got here,
04:17and that'll help explain, I
04:18think, where I'm at and
04:20where I I plan to
04:21kind of be heading. So
04:22if I start at the
04:24beginning, it really started with
04:25the realization that we learn
04:27about in class all the
04:28time in school when you're
04:29you're studying, which is how
04:30systems and politics affect people's
04:32health.
04:33But the part that really
04:34stuck with me is I
04:35hadn't really seen that before.
04:37And on top of not
04:38seeing it before, I didn't
04:39understand how divorced evidence was
04:41from policies
04:42and how much worse that
04:43was when you're talking about
04:44stigmatized populations, which is something
04:46many of us, I think,
04:47deal with. So I got
04:48to become familiar with that
04:49while working on San Diego's,
04:51safe syringe program.
04:54And so
04:55I was really ignorant of
04:56how much politics would affect
04:57day to day life, for
04:59people who used injection drugs
05:01as well as, even the
05:02people who tried to help
05:04people who use injection drugs.
05:07And so in San Diego,
05:09we had to operate under
05:10a public health state of
05:11emergency, which mean every week
05:12someone had to vote to
05:13say this emergency was still
05:15happening or we couldn't even
05:16operate.
05:17Even on when we did
05:18operate, we operated two hours
05:20a day,
05:21for two days of a
05:22week.
05:23So four hours total,
05:24on a one for one
05:26exchange for fifty needles, which
05:27meant that basically if anything
05:28happened in somebody's life and
05:30they couldn't make it, they
05:31were not getting needles that
05:32week because you couldn't exchange
05:33for other people really. And
05:34one for one meant if
05:35you didn't have any, the
05:36person who needed it most
05:38couldn't even get any.
05:39And even with those kind
05:40of restrictions,
05:42there were constant news reports
05:43and protesters and everybody else
05:45saying how unhappy they were
05:46about us even being present,
05:47which mean we couldn't provide
05:49confidentiality and and the services
05:51that we really were trying
05:52to provide.
05:53And even on the best
05:54days,
05:55and everything was working perfectly,
05:57our one of our jobs
05:58was to get people into
05:59detox. And back in the
06:01early two thousands when I
06:02worked there, buprenorphine was almost
06:04unheard of. It was very
06:06rare to see.
06:07Methadone was at two places
06:09in a town of thirty
06:10three million or so. So
06:11there was only cold turkey
06:13detox. That was all that
06:14we had access to. And
06:15there was twenty beds in
06:16the whole city. So you
06:17kinda had to let the
06:18stars align to make things
06:19work. So we had this
06:21population you were trying to
06:22help,
06:23and it seemed like every
06:24single system was working against
06:25you.
06:26And so that's kind of
06:28where I wanted to head
06:29today a little bit, to
06:30understand why that happens.
06:32But one of the the
06:33big takeaways that I learned
06:34from this was
06:35the importance of
06:37learning from the population that's
06:39affected and having conversations and
06:40interactions with them and how
06:41I think some of the
06:42best questions come from that,
06:44from those interactions. And so
06:45one of the things that
06:46I became very aware of
06:47when I started working there
06:48was abscesses. I had not
06:50been exposed to abscesses, not
06:51working in that population before.
06:53I didn't realize how prevalent
06:54they were. I didn't understand
06:55how people dealt with them.
06:56You're dealing with a population
06:57that did not trust any
06:58authority and that included the
06:59medical community for the most
07:00part. So there's a lot
07:02of self treatment happening. I
07:03one conversation sticks with me
07:05even particularly where an individual
07:07tried to take care of
07:08an abscess by pulling out
07:10the fluid with a syringe,
07:11and it looked like heroin,
07:12so they decided to mainline
07:14inject it again.
07:16So there was a lot
07:17of misinformation that was happening
07:18and a lot of choices
07:20that, were happening because there
07:22was systems that weren't in
07:23place. And so I'm particularly
07:24proud. It was my first
07:25paper that I wrote. It
07:27really brought light to self
07:29care or of wounds, which
07:30was something that wasn't in
07:31the literature at that point.
07:34You know, we found that
07:35about half of people in
07:36San Diego or half of
07:37clients of syringe exchange programs
07:38were self treating. Most of
07:40that was with, self lancing
07:41with about a third of
07:43them doing that, and then
07:44about ten percent using,
07:46illegally purchased antibiotics. So it
07:48was something that was prevalent
07:49and it's become a lot
07:50more discussed, but at the
07:51time it really didn't seem
07:53to be part of it.
07:53And I think that
07:55one of those things that
07:56we get away from the
07:56wrong answers is by having
07:58conversations with people most affected.
08:01So this is a place
08:02where I really entered my
08:03research, learning about the struggles
08:05and experiences of people with
08:07addictions,
08:08the risk factors that were
08:09in place, many of them
08:10completely preventable, and
08:12the the national and local
08:14systems and policies that really
08:16worked against,
08:18kinda changing the health environment
08:19where people worked.
08:21So the question then
08:23becomes, why does why are
08:24the systems in place? And
08:26this really includes both research
08:27and political systems,
08:29and how do they produce
08:30these bad policies or harmful
08:32policies even?
08:35And so that's what I'm
08:36gonna talk about here today.
08:37I'm gonna bring up two
08:38different, points and two different
08:40intertwined issues
08:41and give examples from my
08:43own research here. So one
08:44is
08:45the decision that often happens
08:46in prioritizing
08:47policy and research that focuses
08:49on identifying and intervening with
08:51high risk populations.
08:53It's a general approach. It's
08:54what we tend to do
08:55where especially if there's a
08:57very high risk group, there's
08:58a a tendency to other
08:59that group and say they're
09:00not part of our population,
09:02and we can intervene with
09:03just them instead of understanding
09:05that that part of the
09:06population is part of the
09:07whole risk distribution.
09:08We need to see the
09:09whole risk distribution. And so,
09:11but but that's something that's
09:12rarely done, and especially in
09:13addiction,
09:14we don't tend to think
09:15this way.
09:17The second issue is more
09:18of a scientific issue. It's
09:20an approach to causation that's
09:21really prioritizing what's easy to
09:23measure, what's, easily accessible,
09:26the quantitative data, ignoring some
09:28of those harder to measure
09:29and quantify or
09:31quantify, qualitative factors that matter
09:33so much. And I'll introduce
09:34what's called the McNamara fallacy,
09:36and how that plays into
09:38this work in the second
09:39half of this talk.
09:41So I'm gonna try to
09:42cover all this in the
09:42next twenty five minutes or
09:45so.
09:46And as I said, give
09:47some examples from my work
09:48and how we've kind of
09:48looked into this. So if
09:49we start with the high
09:50risk prevention strategy,
09:53so for those who are
09:54not familiar with this, gentleman
09:56here, this is Jeffrey Rose.
09:57He's been been one of
09:58the most influential people, I
09:59think, in my my work.
10:00He wrote this brilliant book
10:01on the strategies of preventative
10:03medicine.
10:05And,
10:07it's one of the big
10:08takeaways of the book was
10:09that the difference in prevalence
10:11of an outcome in different
10:12populations is really due to
10:14the different states
10:15of the health of the
10:16parent communities.
10:18The slide is a lot,
10:19so I'll try to kinda
10:20walk you through it because
10:21I think it's important to
10:22explain what we did to
10:23demonstrate a lot of this
10:24work.
10:26And so what you what
10:26you have here is in
10:27the the light orange or
10:29tan is, a population's distribution.
10:31And in that far right
10:33corner, we have that part
10:34that's at the highest risk.
10:36Right? And so a lot
10:37of times when we think
10:38about what to do with
10:39this situation and what policy
10:40makers tend to do, we
10:42tend to focus on that
10:43far right dark orange corner
10:45and say, how can we
10:45move them out? How can
10:47we move that group over?
10:48So a lot of that's
10:49done through identification of high
10:50risk people and then treatment
10:51of high risk people or
10:53identification and linking to another
10:55care.
10:56And it's less often done,
10:58especially with stigmatized,
11:00conditions and outcomes. Thinking about
11:02how we can shift that
11:03whole distribution, which is more
11:04of that population based approach.
11:06So how can we take
11:08the people that are normal
11:09and bring them to low?
11:10How can we take people
11:10that are high and bring
11:11them to normal? And at
11:12the same time when we
11:13do that, we're also moving
11:14the high group out.
11:16And this is, an approach
11:18that is,
11:20as I said, it's it's
11:21it is used. We see
11:22it frequently used, with
11:25more common conditions and less
11:26stigmatized conditions, things like blood
11:28pressure. You know, you would
11:29definitely have this be a
11:30population based approach usually.
11:32But things like addiction, it
11:33usually is not.
11:35And in in the example
11:36that I'm gonna give, one
11:38of the ways that we
11:38went to look at this
11:39was actually something that comes
11:41up frequently with firearms and
11:43mental illness.
11:44And so a predictable cycle
11:46that happens every time there
11:48seems to be a mass
11:49shooting is a discussion about
11:50the role of mental illness
11:51and firearm deaths and the
11:52need to focus on that
11:53high risk group compared to
11:55your population approach, which is
11:56gonna move the whole distribution.
11:59So in a collaboration with
12:00some colleagues at at Columbia
12:02and NYU, which include, Magdalena
12:04Serta, who's will be joining
12:06the school of public health
12:07as the new chair of
12:08chronic disease epi.
12:10So we carried out a
12:11study to better understand the
12:12narrative of firearm and mental
12:14illness
12:14and to compare a targeted,
12:16high risk approach versus more
12:18of the population based approach,
12:20in an agent based model.
12:22And so
12:23for those who are not
12:24familiar with an agent based
12:25model,
12:27basically, what this is is
12:28it's a way to model
12:29dynamics of a changing system
12:30to better understand how if
12:32you shift or move one
12:33piece of it, how you
12:34have downstream effects.
12:36And you do that by
12:37simulating agents, which are basically
12:40individuals. And those individuals,
12:42they live within a environment.
12:43They have characteristics of that
12:45environment. They interact
12:51with each other. They interact
12:51with their the sit the
12:51situation. And you can do
12:51it for a city like
12:51we did for the adult
12:52population of New York.
12:54And so you place these
12:55individuals
12:56within their communities. And as
12:57people become eighteen years old,
12:59they, you know, move through
13:01different risk strata and different
13:02occurrences.
13:03And they live their life
13:05cycle in that way. And
13:06so this is all done
13:07through a bunch of equations
13:08essentially.
13:09And this is what those
13:11schematic looks like. It's very
13:12complex. It's not the simple
13:14one one cause, you know,
13:15one outcome thing. And this
13:17is a lot to look
13:18at. So if we kinda
13:20zoom in here on the
13:21social network characteristics, you can
13:22see that agents form and
13:24and dissolve social ties. They
13:26have friends who were who
13:27are perpetrators and victims of
13:29violence and some who own
13:30firearms. And with each iteration,
13:32the agents move through their
13:34lives essentially.
13:36And so to look at
13:37these different prevention strategies and
13:39what and demonstrate their utility
13:41in this kind of situation,
13:44We looked at three different
13:45groups for disqualification.
13:47So we looked
13:48at the the first group
13:49is the low prevalence group,
13:50and this actually does include
13:51the psychiatric hospitalizations.
13:54Sorry, I shouldn't say convictions.
13:55It should be just psychiatric
13:56hospitalizations,
13:58as well as people that
13:59are alcohol related misdemeanors. And
14:00so this group is the
14:02lowest prevalence group in the
14:03population. It's about a quarter
14:04of a percent.
14:07The next group is the
14:08moderate prevalence group, and so
14:09this is drug misdemeanor convictions
14:11and domestic violence restraining
14:13orders.
14:14And these people make up
14:15about one percent of a
14:16population. And then on the
14:19last one, we have the
14:20high prevalence group, which is
14:22comprised of all felony convictions
14:24and misdemeanor,
14:26convictions. And so this is
14:27the largest group. It's about
14:28two point five percent. And
14:29so we're comparing disqualifications in
14:31these different groups, which is
14:33what is often the discussion
14:34around firearm violence and how
14:36to prevent it versus a
14:37very population based approach, which
14:38is increasing prices.
14:40So just basically increasing prices
14:41on the firearms and ammunition
14:43and what can we do
14:44with that? Before you go.
14:46Yes, please. I can answer.
14:48Disqualifications?
14:49Disqualifications. They cannot purchase guns.
14:52So
14:53Exclude they're not allowed to
14:54buy a gun. Correct.
14:56Correct. However, we also do
14:58model whether they can get
14:59a gun illegally. So if
15:00you have a friend
15:01who has a firearm
15:03in a year and you
15:04are a perpetrator, you can
15:05get the firearm through that
15:06connection.
15:07So all those connections are
15:09modeled still too.
15:11And so the way you
15:12actually prove that you understand
15:13the model is by validating
15:15it by what happened in
15:16reality, and the goal is
15:17to have them match up
15:18and they do. So we
15:19did look at illegally purchased
15:20firearms and how that is
15:21affected.
15:22I don't show that here,
15:23but how that is affected
15:25by prices going up and
15:26things like that. So we
15:27really do try to model
15:28the whole system.
15:30It's process.
15:32So, so the first thing
15:33to kinda take from this
15:34is that the baseline firearm
15:35high on-site rate in New
15:36York was four per hundred
15:38thousand persons. So the goal
15:39of the first step was
15:40just to decrease it by
15:41five percent. What could we
15:42do?
15:43So first is removing the
15:45low prevalence group, which included
15:46that psychiatric hospitalizations. You remove
15:48every single firearm from that
15:49low prevalence group, you can
15:50only reduce it by two
15:51percent.
15:53That's the most you could
15:54do if you wanna do
15:55effects firearm homicides.
15:58In the moderate group, if
15:59you removed it from twenty
16:00five percent, you could get
16:01to that five percent level.
16:04In the high prevalence group,
16:05it's even less twelve percent.
16:07And then if we increase
16:08price, you'd increase price by
16:10just eighteen percent and you'd
16:11hit that same five percent.
16:12So having a lot of
16:13people at that lower risk
16:15is going to have a
16:16bigger impact when you're affecting
16:18all of them versus the
16:19few people at high risk.
16:20If you combine all three
16:22of them, you would get
16:22to a twelve percent reduction
16:24at this.
16:26And then just to show
16:28how
16:30how the the next stage
16:31of it kinda goes, you
16:32can look at different levels
16:33of this. And this is
16:34the the
16:35interesting part about these models
16:36is once you build them,
16:37you can look at them
16:38a lot of different ways.
16:39So if we wanna see
16:39a bigger example, what we
16:41basically start to see is
16:42that price becomes the only
16:43thing that's influential.
16:45You know, even if we
16:46remove a hundred percent of
16:47the firearms again from that
16:49moderate group,
16:50we're reaching sixteen percent. So
16:51we can't even hit that
16:52twenty five. And a hundred
16:53percent disqualification would never happen
16:54by the way, just to
16:55be clear. Like, this is
16:56a hypothetical world. This isn't
16:58reality. We would never be
16:59able to take all of
17:00them away. So even if
17:01you did take every gun
17:02away from this group or
17:03firearm, it still would not
17:05have the desired effect of
17:06anything above sixteen percent.
17:09The high prevalence group, you
17:11could get a little bit
17:11closer, but you are gonna
17:12top out pretty soon after
17:14that. And you again, you
17:15see that price is really
17:16the only thing.
17:18And unfortunately, despite this kind
17:19of evidence, the contrary that
17:21mental illness is not driving,
17:23these instances,
17:24we hear the same debate
17:25that's really continuing after every
17:27single mass shooting. How about
17:28the folk need to focus
17:29on mental illness?
17:31And we definitely need better
17:32access to mental illness. I
17:33would or treatment. I would
17:34never say otherwise. But the
17:36research shows that focusing interventions
17:38on this group or any
17:39group is going to be
17:40insufficient,
17:42in this kind of situation
17:44where a lot of the
17:44cases are coming from those
17:46at lower risk. And so
17:47it's a need to think
17:48about the whole population
17:50and their distribution.
17:53So this example really demonstrates,
17:55again, the difference in how
17:56we think about the high
17:57risk prevention strategy versus the
17:59population based strategy when we're
18:00when we're thinking about policies.
18:02And there's a lot of
18:03examples of this. The high
18:05risk prevention strategy being promoted
18:07over that population based approach.
18:10And
18:11this is particularly true when
18:12we looked at stigmatized outcomes.
18:14And then I'm gonna so
18:15I'm gonna pivot here to
18:16focus on the next stigmatized
18:17outcome, which is more of
18:18the the other topic of
18:19this stuff, which is looking
18:20at, drug use, addiction, overdose.
18:23And so if we look
18:24specifically at overdose,
18:27high risk,
18:28prevention strategies are often prioritized.
18:30But
18:32as we talk about that,
18:33I wanted to introduce the
18:34next topic I said I
18:35was going to, which is,
18:38the the two common
18:40in both policy making and
18:42in research in particular.
18:44We have a tendency to
18:45focus on those easily measured
18:47and to easy to measure
18:49metrics instead of those meaningful,
18:50harder to measure items.
18:53And so this is the
18:54basis of the McNamara fallacy.
18:56So the gentleman who gets
18:58the honor of having this
18:59named after him is Robert
19:00McNamara. He was the US
19:01secretary of defense during the
19:02Vietnam War.
19:04And there's a lot of
19:04different reasons this has been
19:06attributed to him. I think
19:07the one that I see
19:08most often
19:09is that during the war,
19:10he became highly focused on
19:12the metrics of deaths, and
19:14he thought that you could
19:14win a war of attrition.
19:16So if you simply counted
19:17how many people you killed
19:18versus how many people on
19:20your side died, eventually, you
19:22would have the winner.
19:24And that never happens. And
19:25the reason that never happens
19:27is because focusing only on
19:29killing another population
19:30is going to destroy
19:32any
19:33goodwill or any other feelings
19:35that could exist. Any of
19:35the rural population that is
19:37affected by this and the
19:38people you're supposedly trying to
19:39help,
19:40is not
19:41they're not coming to your
19:42side, essentially. So you're missing
19:44that harder to measure qualitative
19:45factor, which is the attitudes
19:47on the ground and how
19:47people felt.
19:49And so this is often
19:50stated in, three parts. So
19:52the fallacy basically says that
19:54you measure what's easy to
19:55measure, you disregard that which
19:57can't easily be measured,
19:58and then you assume that
20:00whatever can't be measured is
20:01unimportant and you can even
20:02go step further. It doesn't
20:03even exist.
20:05And it basically says that
20:06focusing on metrics leads to
20:08a very narrow view and
20:09ignores that complexity and the
20:11crucial
20:11intangible factors
20:13that are gonna result in
20:14poor long term strategies.
20:16And I'd argue that almost
20:17all of the policy mishaps
20:19that have happened during the
20:20overdose crisis
20:23are fell victim to this
20:24fallacy,
20:25and we'll kind of walk
20:26through that here. One of
20:27the easiest ways to see
20:28that is really the focus
20:30of the
20:31of the crisis. So the
20:32metrics just were so easy
20:34in this case. You had,
20:35you know, opioid prescriptions. If
20:37you looked at them, dispensed
20:38between ninety nine and twenty
20:39thirteen. And we've all kind
20:40of seen these figures before.
20:41This isn't anything new.
20:42And when you overlay it,
20:43you just get such a
20:44perfect picture. And so I
20:46think this became the focus
20:48of the easiest to measure
20:49metric,
20:49which was supply. How do
20:51I affect supply?
20:53And you really see that
20:54in the policies that came
20:55into effect. The first policies
20:57sorry. This slide isn't the
20:58easiest to see, but this
21:00report came out in twenty
21:01eleven from the White House,
21:04Office National Drug Control Policy,
21:06and they put forward four
21:07different policies.
21:09First is education,
21:12educating patient providers on the
21:13risk of opioids.
21:15The second was advancing prescription
21:16drug monitoring programs.
21:18The third was, increasing access
21:20to proper disposal of unused
21:21medications. And third and finally,
21:25increasing enforcement for illegal
21:27prescriptions.
21:29But at the time, there
21:30really was no evidence to
21:31support these claims. And not
21:33only that, the evidence that
21:34did exist was looking at
21:36prescription opioid supply.
21:38And so
21:39one of the the
21:41papers that we did was
21:42question this and ask,
21:44do we care about prescription
21:45opioid supply or do we
21:46care about deaths? Do we
21:47care about actual some measure
21:49of outcomes?
21:50And this isn't necessarily a
21:51really hard to measure metric,
21:53but I think it still
21:54illustrates,
21:55that this was one of
21:56the key metrics that were
21:57just ignored,
21:58in a lot of the
21:59early policies.
22:00And so at the time
22:01this article came out, there
22:02were seventeen papers looking at
22:04prescription drug monitoring programs and
22:06death. There was really low
22:07grade evidence, which means there
22:08was conflicting results. It means
22:11that there was risk to
22:12bias in a lot of
22:13them,
22:13and they still
22:15had just moderate,
22:16evidence that it reduced prescription
22:18opioids
22:19deaths. But the real concern
22:20that came out of it
22:21was that it was shown
22:22to increase heroin related deaths
22:24in a much more
22:25rigorous fashion.
22:27And this was the first
22:28one of the first papers
22:29that really brought light, I
22:30think, to the unintended consequences
22:31of these policies at a
22:32at a large level.
22:35And then it became a
22:36regular occurrence. And I think
22:37this paper was a brilliant
22:39paper that was done by,
22:41Pitt and colleagues.
22:42It's a systems dynamic model,
22:44which is kinda like an
22:45agent based model. There's a
22:46lot of thinking about different
22:47creating a whole society and
22:49then trying these different interventions.
22:52And if we looked at
22:53this article
22:55and four of these outcomes
22:56here, these were really focused
22:58on prescribing.
22:59Again,
23:00we saw that four of
23:01the outcomes, which was chronic
23:02pain reducing chronic pain prescribing,
23:05which we thought as a
23:06tapers, drug rescheduling, prescription drug
23:08monitoring program, and then drug
23:10reformat reformulation
23:11such as,
23:13abuse deterrent, OxyContin,
23:15that all these actually
23:17reduced,
23:19prescription opioid deaths, but they
23:20were completely offset by heroin
23:21deaths. It was a complete
23:22lack of understanding about the
23:24complexity of what was occurring.
23:25And there's
23:26now, you know, set almost
23:28ten years of papers maybe
23:29or maybe not that much
23:30that have all, you know,
23:32demonstrated the same thing. The
23:33reformulation was extremely harmful. And
23:35it's a lack of understanding
23:36the whole system, the complex
23:38network of the system, how
23:39they all work together and
23:40understanding the unintended effects of
23:41these.
23:43And so in an attempt
23:44to kind of start to
23:45challenge this and push back
23:47against it,
23:48one of the some of
23:49the work that we did
23:50was try to quantify some
23:51of the harder measure stuff.
23:53Yes.
23:54Yes.
23:55I think this is super
23:56important and not not surprising,
23:58but But I feel like
23:58sometimes the time horizon
24:00is wrong because you can
24:01imagine a world where the
24:02short term
24:04people moving from prescription abuse
24:06to sort of Yes. Ethanol
24:08sort of sees sort of
24:09offsets the the ending benefit
24:10of the debt. And maybe
24:12the long term
24:13sort of fewer entrance into,
24:14like, the risk pool if
24:16there are fewer prescription opioids
24:17as sort of as, like,
24:18a gateway. Hundred percent.
24:20Have any of these studies
24:21tried to, like I mean,
24:21clearly, that isn't maybe enough
24:23time to do that. Is
24:24there a way or has
24:25there any modeling of, like,
24:27potentially a long term benefit
24:28even if there's no Yes.
24:30So, yes, there has been.
24:31So this was the first
24:32paper that was done by
24:33this group. They published a
24:35paper, I think, two years
24:36later, in twenty twenty that
24:38did different time horizons.
24:40This was a five year
24:40time horizon. They also did
24:42a ten year time horizon.
24:43And when you hit the
24:44ten years, you start to
24:45see exactly what you're saying,
24:46where prescription drug monitoring programs
24:48even started to reduce deaths
24:49at that point. You just
24:51had to survive that ten
24:52year,
24:53harmful effects period to get
24:55to the benefit one. And
24:56and actually, I think a
24:58lot
24:59of the discussion around that
25:00what around all of this
25:02is that any of these
25:03programs, I think, could have
25:04been helpful, and that's something
25:05we can definitely talk about
25:06is that I think any
25:07of them could have been
25:08helpful if they were done
25:09differently, and it wasn't just
25:10so hyper focused on one
25:12aspect of it instead of
25:13thinking about the individual and
25:15the and understanding people and
25:16having them involved in it.
25:18Right?
25:19And so one of one
25:20of the things that we
25:21tried to quantify and look
25:22at was the other side
25:23of,
25:24what we started hearing a
25:25lot about, which was socioeconomic
25:27situations and how that could
25:28play into it. You know?
25:31And so when we looked
25:32at this systematic review,
25:34I enjoy systematic reviews. If
25:36you can't tell, I I,
25:37I like to do one
25:37every couple years. So it's,
25:39you know, these are topics
25:40you're interested in, I'm I'm
25:41available.
25:43But, one of the things
25:43we looked at was socioeconomic
25:45determinants of overdose deaths and
25:46really to understand what was
25:47the literature there, because that's
25:48a much harder to measure
25:49thing. It's much harder to
25:50measure.
25:52One of the so in
25:53this study, we found
25:54twenty seven studies had done
25:56this.
25:57And so that's a much
25:58more than they had looked
25:59at PDMPs in death. So
26:00this is a much more,
26:02study topic.
26:03And the results were universal
26:05across the board. Socioeconomic
26:07situations affected county level overdose
26:10rates,
26:11on every study. It didn't
26:12matter what what measure you
26:14looked at. It wasn't it
26:14didn't matter if you're looking
26:16at income inequality or if
26:17you were looking at poverty
26:18level. They all had the
26:19same effect.
26:21It was one of the
26:21most consistent findings I think
26:23I've ever found.
26:26And yet, I I have
26:27never heard a policy maker
26:28say, let's increase universal basic
26:30income to affect the overdose
26:31crisis or job training programs
26:33or anything to that extent.
26:34It's something we just don't
26:35hear as much.
26:37But one of the interesting
26:38things that we did is
26:39take this a step further.
26:42And so we then combine
26:44the two. So what's the
26:45role of socioeconomic situations and
26:46prescription opioids?
26:48And this was a really
26:48fascinating paper for me, because
26:50it didn't produce the effects
26:52I thought it would, but
26:53then it made sense.
26:55So basically, what we found
26:56in this paper
26:58was that
26:59in highly deprivized,
27:02environments, counties that had the
27:04most economic inequality, that had
27:07the highest federal poverty rates,
27:10prescription opioid supply had no
27:12role. It was completely unassociated.
27:14It was a pretty strong
27:15finding. It was it was
27:16pretty consistent across
27:18those locations as well.
27:20In in in places where
27:21there was much
27:23lower,
27:24poverty and much less income
27:26inequality, all of a sudden
27:27prescription opioid supply was very
27:28important.
27:29So, again, it wasn't what
27:31I think we I expected
27:33to find, but it actually
27:33started a story could start
27:35to come together,
27:37in understanding how these two
27:39work together and that highly
27:40deprivised area places like prescription
27:42opioid supply was just one
27:43more thing, you know, that
27:44was already affecting them.
27:46Whereas in in places that
27:48were
27:49doing better overall, this became
27:51something much more impactful. And
27:53so now if you're looking
27:53at all the policies that
27:54were focused on reducing supply,
27:56even if they all worked,
27:57you might have only been
27:58affecting one segment of the
27:59population because you didn't understand
28:01the problem.
28:02And so I think that's
28:03a a really important piece
28:05of this, and we really
28:06need more literature to understand
28:07this and actually capture these
28:08harder to measure
28:10metrics in a better way
28:11because I don't even think
28:11this is perfect. And I'm
28:13gonna continue to pull out
28:15my own research and my
28:16own shortcomings in this, and
28:17that's gonna kind of be
28:18the the next part of
28:19this talk is
28:21to understand
28:22how we continue to do
28:23this in pharmacoepi studies,
28:25and where the McNamara fallacy,
28:27I think, continues to live.
28:29And so in this past
28:30year, I received the r
28:31zero zero to look at
28:32buprenorphine treatment outcomes,
28:34in real world data. And
28:36so we are looking at
28:37VA EHR data. We're looking
28:39at what happened during COVID
28:41nineteen.
28:42And,
28:43you know, did telehealth increasing
28:44telehealth use affect people that
28:46were initiating buprenorphine? Did it
28:47affect the long term outcomes
28:49of people that had been
28:49on it a long time?
28:51And when we started putting
28:52this grant together, I looked
28:53for all the outcomes that
28:54I could find, in these
28:56kind of studies.
28:57And I found four that
28:59were most commonly used. So
29:01with almost
29:02no exceptions,
29:04and my study is not
29:05an exception, the primary outcome
29:06is always a hundred and
29:07eighty day retention in care.
29:09This is the metric that
29:10is used most often. The
29:11problem is is this is
29:12not a health metric, this
29:14is a process metric.
29:16We don't inherently care that
29:18someone's in care. If we
29:20care that they are stable,
29:21maybe we can make that
29:22statement, but that's not what
29:23that is necessarily measuring. And
29:25so that's the first one
29:26we see. And then a
29:27couple
29:28metrics that I was able
29:29also find is a list
29:30of drug use and toxicology
29:32data,
29:33opioid use specific hospitalizations,
29:35fatal overdoses.
29:38And there's so there's many
29:39reasons, I think, that a
29:41patient might seek out care,
29:43and we know these reasons.
29:44And some of them are
29:45on here. There's definitely a
29:47a desire to stay alive.
29:49That is one reason that
29:50drive that brings people into
29:52treatment.
29:52There's a desire to stop
29:54using
29:55drugs.
29:57And so there is a
29:58piece of that that can
29:59be found maybe in the
30:00toxicology data.
30:01But it's incomplete. And I
30:02think that that can be
30:04seen a little bit more
30:05when we actually look ask
30:06questions about what patients want.
30:10And so I found a
30:10couple of systematic reviews that
30:12were focused more on, patient
30:14goals.
30:15And when we look at
30:16these, we see again, there
30:17is some overlap of the
30:19the challenge that,
30:21that I became aware of
30:22is that
30:23even the things we are
30:24measuring were not clear
30:26in how we should measure
30:27them. And I think the
30:28first example of that is
30:29really good, the treatment related
30:30goals.
30:32When you looked at studies,
30:33there's one study in particular
30:34that asked patients about their
30:35goals.
30:36And about seventy percent said
30:37remain in treatment,
30:39while two thirds said to
30:40get off of buprenorphine.
30:42So the majority of them
30:43actually their one of their
30:44main goals was getting off
30:45of them. And now, obviously,
30:46these are thinking maybe more
30:47of a longer term period
30:48like a year or I
30:50don't know the exact timeline
30:51of what that would be.
30:54But
30:55they're using retention and goal
30:57in care might in and
30:58of itself not even be
31:00a patient centered goal,
31:02in that sense. There's, again,
31:04substance use related goals of
31:05avoiding withdrawal. Those could be
31:06seen in some of this.
31:08But the bottom one is
31:09completely absent,
31:10I think from almost any
31:11study that's using,
31:12administrative claims data.
31:15How to measure living a
31:16normal life, stability, reduce criminal
31:18activity, improved housing, employment, improved
31:20social and familial relationships. These
31:22are the things that drive
31:23people into treatment that that
31:24they wanna get back the
31:25part of themselves they wanna
31:26regain
31:27from entering recovery,
31:29and we don't measure it
31:30in our pharmaco studies at
31:32all. And I think that
31:33a lot of that is
31:35driven by them being difficult
31:36to measure metrics, And I
31:38think we continue to kinda
31:39do this. And so
31:40part of,
31:42part of this talk is
31:44to to to bring my
31:45own awareness to it, but
31:46also to to begin to
31:48ask questions about how we
31:48can do better with this
31:49and how continue to think
31:50about it. And I don't
31:51think that this is unique
31:53to to looking at,
31:54these outcomes. I don't think
31:55it's unique to addiction, even
31:57though I I put that
31:58in the main title slide.
31:59I think these are the
32:00same problems that are coming
32:01up in a lot of
32:01our research,
32:03where we tend to look
32:04most at the high risk
32:06individuals
32:07and,
32:08and and miss the population,
32:09and we continue to
32:11to look at what's easiest
32:12to measure, what's available, and
32:13and kind of perpetuate that.
32:16And so
32:18I think, as I said,
32:19again, I think this is
32:19something that's very common in
32:21addiction science.
32:23And as I reviewed,
32:24there isn't just really one
32:26reason for this.
32:27It's usually the confluence of
32:28factors. I think it's all
32:29of those things. I think
32:30that,
32:31usually it involves not interacting
32:32with the population enough, not
32:34understanding the individuals that are
32:36part of that population and
32:37what's going on in their
32:38lives is a big piece
32:39of it. I think the
32:41the idea of looking at
32:42sick individuals and focusing on
32:44that high risk group and
32:45othering them and saying they're
32:46not part of our population,
32:48is another piece of it.
32:49And then again, I think
32:50the metrics.
32:51And I think one of
32:51the things that came up
32:52when I was putting this
32:53all together is that we
32:54have just done this again.
32:56I don't know how many
32:56of you guys are familiar
32:57with kratom.
32:58It's a substance that we've
32:59been talking about a lot
33:00more.
33:01It's an it acts on
33:03opioids,
33:03receptors,
33:04same way opioids does. It's
33:06available,
33:07at a lot of, like,
33:08vape shops and stuff like
33:09that. You can become very
33:11dependent on them. And two
33:12weeks ago, Connecticut
33:14just rescheduled it and just
33:16dropped them from the shelves.
33:18But I don't I haven't
33:19seen any discussion of what
33:21to do when people are
33:22dependent on it. Maybe providers
33:23have received something
33:25that has said what to
33:26do, that there's evidence that
33:27buprenorphine can work for individuals
33:28that are,
33:29dependent on it. I have
33:31not seen that discussion occurring.
33:33I haven't the discussion recurring
33:34of where to get help
33:35or anything else, and I
33:36think it's the same kind
33:37of thing where you fail
33:38to understand the complexity of
33:40it. We kind of look
33:40at one aspect, which is
33:41supply over and over again,
33:43and kind of pulling out
33:44supply without thinking of the
33:45individual and what they will
33:47do next.
33:49I sent a text before
33:50this to try to talk
33:51with some research to see
33:53if we can learn something
33:54if it's not too late
33:55about what those individuals are
33:56doing. I think it's less
33:58likely they'll go to an
33:58illicit supply, like, with, with,
34:01like, OxyContin or something else,
34:03but,
34:03I think that could still
34:04happen. Just Yeah. Yeah. I
34:07like that. This is it
34:08is real, like, even before
34:09Kratom was rescheduled.
34:11Clinically, we see we see
34:12this people who, like, suddenly
34:13stop Kratom, and they don't
34:14know why they feel like,
34:15there's a real mystery because
34:16they just don't know why
34:17they feel so horrible. Yeah.
34:19So we are we are
34:20using buprenorphine. I can think
34:21of, like, a handful of
34:21patients. But you're right. Like,
34:22I don't know. It's just
34:24so unknown. Like, someone's like,
34:25I can't get anymore, and
34:26then they just feel horrible,
34:27and they just don't know
34:28why. Yeah.
34:30It's a confounding
34:31a confounding thing to to
34:32deal with. So it does
34:33happen. I I haven't thought
34:34of people trying to surveil
34:36Connecticut and sort of if
34:37there'll be an uptick. If
34:38people suddenly have a withdrawal
34:39symptoms, they're not knowing why
34:40and sort of trying to
34:41get into treatment. Yeah.
34:43Yeah. No. We'll see. It
34:44would have been best to
34:45start a study,
34:46you know, two months ago.
34:48But, you know, since that
34:49didn't happen as far as
34:50I know, it's still something
34:51that's worth doing because other
34:52states are gonna continue to
34:53do this too. So learning
34:54from these kind of situations
34:55is exactly the kind of
34:56things I like to do.
34:58So, for those who are
35:00interested,
35:02this this text,
35:03system science and population health
35:05is a great book. It's
35:06edited by Abdul El Sayed
35:07and Sandro,
35:08Gala.
35:10The the topic of this
35:11talk, wrong answers, I wrote
35:13a chapter on it. It's
35:14not specific to addiction policy.
35:15There's another one that I
35:16I authored on, social determinants
35:18of health and how system
35:19science can help with that.
35:20But it's a really great
35:21text. And for those who
35:22are not familiar, the first
35:24editor on there, Abdul El
35:25Sayed, actually stepped away from
35:26academia, and he's now running
35:28for US Senate of Michigan
35:30to change policy directly.
35:32So it'll be interesting to
35:33see what an epidemiologist
35:34comes up with there.
35:36And so I I appreciate
35:38this. My my goal is
35:39to kind of start discussions
35:40and and continue to think
35:41about how this affects,
35:43my work and hopefully others
35:44work.
35:46So I can't tell you
35:47how much of a privilege
35:48it is to be here
35:49and to be able to
35:49give this talk and to
35:51be part of this community.
35:53It's been very nice. And
35:54so I appreciate the opportunity
35:56to, to talk with you
35:56about my research and kind
35:58of my path here today.
35:59So happy to continue that
36:07discussion.
36:08Yes. David, thank you. Really
36:10nice talk. A lot of
36:11provocative
36:12themes and ideas,
36:14which I think this forum
36:15is really well suited for.
36:16So I appreciate you,
36:18taking the time.
36:19I wanna focus on your
36:21last topic that I I
36:22would call, you know, how
36:24do we get better patient
36:25reported outcomes
36:28to, you know, things that
36:29matter to patients in their
36:31lives
36:32and use data to
36:35drive,
36:37decision making and interventions towards
36:39things that actually affect patients.
36:41So, you know, in the
36:43in the clinical trial world,
36:46PCORI and even the NIH
36:49to a certain degree have
36:49been really pushing us to
36:51find better patient use better
36:53patient reported outcomes.
36:56But trials only go so
36:58far. Right? It takes a
36:59long time to develop a
37:01trial. We've got a certain
37:03select population of people who
37:04enter clinical trials, and it
37:06takes a long time to
37:07generate that kind of evidence.
37:08So
37:10what I and others have
37:11been thinking about is, you
37:12know, how can we get
37:14actual PROs into clinical practice
37:16so we're measuring
37:18things in day to day
37:19practice
37:20that actually matter to patients
37:22so that we can look
37:23at real time data and
37:25have that affect clinical outcomes.
37:26So
37:27all pointing to
37:29the VA is doing this
37:31in in the pain world,
37:33integrating
37:34a pain measure order set
37:37so that, you know, we
37:39can collect real time data
37:41and do secondary analysis of
37:43these data to better inform
37:44code of practice.
37:45And I just wanted to
37:47tell you that so that
37:48maybe you'd be interested in
37:49joining us to That that's
37:51fair. Help develop that measures
37:53and help examine the findings
37:55that we can
37:56create from this work. Yeah.
37:58No. I definitely am. I
38:00I I think it's so
38:01important.
38:02I I've been involved with
38:04some of the NIDA CTN
38:05studies. Right? And so, Ned
38:07Nunez,
38:08who I I collaborate with
38:10some,
38:10he we we worked on
38:12one of his, the Xspot
38:14study, which is looking at
38:15extended release, buprenorphine and naltrexone
38:18comparison. But they had such
38:19a nice question in there
38:20that the each patient reported
38:22what their goal was. What
38:23was your goal in entering
38:24treatment? And then you could
38:25look at, did that goal
38:26achieve? And we can't do
38:27that in these pharmacoepi studies
38:29if we don't have those
38:30kind of metrics,
38:32and we just don't have
38:33them. So, unfortunately, I don't
38:35no solution that I have
38:36yet except for things like
38:37this where the VA system
38:38can care about it and
38:39then enter it into the
38:40system.
38:41And we have Question one
38:43of the pain measure order
38:44status. What is your goal?
38:45Yes. Such a simple question.
38:48Yeah. And sometimes those questions
38:49are super important. I one
38:50of one of the papers
38:52I did a while ago,
38:53and it was it was
38:54one of those really fun
38:55papers in some sense. It
38:56was
38:57I shouldn't use that word.
38:58It was an interesting paper,
38:59but it was looking at,
39:00post deployment,
39:02responses that people had on
39:03on mental health and well-being.
39:05And so we collected all
39:06these measures on how people
39:07could be, and their post
39:09deployment and their their mental
39:10health status and their physical
39:11health status. And we asked
39:13one question that said, how
39:14is your post deployment
39:15transition going? And that was
39:17more predictive than anything else
39:18about all the other things
39:20and about how they thought
39:21it was. It was lower
39:22suicide attempt risk, all these
39:23other things. It was just
39:24a simple question. So sometimes
39:26that simple question can be
39:27added. It just needs to
39:28be added. And it's very
39:29difficult to change an entire
39:31system,
39:32to to to do that.
39:33But it's it's great when
39:34systems like the VA are
39:35willing to start.
39:39Yes.
39:40Great talk.
39:41I I would back just
39:43a little bit on your
39:44population
39:45figure because it all gets
39:47down to cost benefit analysis.
39:49Right? And in some cases,
39:51identifying
39:52this group is the most
39:53cost effective thing to do.
39:54Right? In other cases, not
39:56depending on the penetrance,
39:58depending on the cost of
39:59the intervention. Right? There's a
40:00lot of other Yeah. Go
40:01into that calculation.
40:03So I think it's a
40:04little bit dangerous to say
40:05it's always better. No. Yeah.
40:07You know, there are circumstances
40:08where it's better, and it
40:09sounds very much like this
40:10is one.
40:11But there are other circumstances
40:13where that would not be
40:14the case. And in fact,
40:15I would contend that we've
40:16gotten into a lot of
40:17overtreatment
40:18in this country
40:19precisely with that kind of
40:20logic. So
40:22I think it has to
40:23be a little more balanced
40:24than that. I appreciate that,
40:26and I
40:27take it honestly and say,
40:28yes. I can I will
40:30change how I present it
40:31because I don't feel that
40:31way? I'm not saying that
40:32I don't think the high
40:33risk approach is
40:34wrong and not useful,
40:36and I think that both
40:37have their place in society.
40:38I think the challenge that
40:39we have is too often
40:41biases
40:42is what draws to the
40:43high risk approach because what
40:45the high risk
40:46approach requires from everybody else
40:48is nothing, and that is
40:50much more tenable. And so
40:51especially when you're talking about
40:52a population or outcome that
40:53is,
40:55stigmatized, we're going to favor
40:57that one over the one
40:58that makes me change what
40:59I'm doing if I'm not
41:00part of that just to
41:01help them. And we saw
41:02that during COVID. Right?
41:04Yes.
41:05With alcohol, for example. Yes.
41:07I don't disagree with No.
41:08It's how I presented it.
41:09So I I hear that.
41:10Thank you.
41:12Yes.
41:13Online had a question.
41:16You wanna jump on, or
41:17I can read it?
41:20I don't know how. Hey,
41:22David. Can you hear me?
41:23Yes.
41:24Great talk.
41:26Appreciate,
41:29the narrative over time.
41:32You mentioned that one of
41:33the common outcomes that,
41:36studies were using you identified
41:38for buprenorphine was retention and
41:40treatment at a hundred and
41:41eighty days.
41:43And that sounds fairly straightforward.
41:45I was just
41:47my observation is that
41:49different teams operationalize
41:51even that, quote, unquote, standard
41:54metric differently.
41:55And as you pointed out,
41:56some will allow, you know,
41:58seven days of missed med,
41:59then some will
42:01expand it to thirty days.
42:02So I was just wondering
42:03if you could kinda talk
42:04on that
42:05variability.
42:07Yes. Yeah. Even even those
42:08measures are not completely agreed
42:09upon.
42:11I mean, I think the
42:12thirty day gap is the
42:13most commonly used one, but
42:15that does bring up the
42:16fact that by thirty days,
42:18every single person that's on
42:19buprenorphine of a reasonable dose
42:22would be deep into withdrawals.
42:24You know,
42:25after one day, two days,
42:26they would be into withdrawals.
42:27It's a longer active medication,
42:28I believe, but not not
42:30enough to do longer periods.
42:31So, yes. I I think
42:32it's interesting that that's the
42:34gap that's often used when
42:35we have to assume that
42:36people are using something else
42:38during that time probably.
42:39Yeah. Historically,
42:40I think that gap derives
42:42from payment for opioid treatment
42:44programs. Yes.
42:45So it's not clinically derived.
42:48Yes. No. A lot of
42:49I mean, that's that was
42:50the point I was kinda
42:51trying to make where that's
42:52the process outcome.
42:53And I think that's part
42:54of the process. It's really
42:55more about health services research
42:57that has dictated that.
42:58I think that thirty day
43:00gap
43:01I can't think of the
43:01organization it comes from. The
43:03n
43:04n n any anyway, it's
43:06a very common one. I
43:07either way, it is a
43:08process gap. It is based
43:09on payment. It's not based
43:10on, factual
43:12piece. So
43:13I think that that is
43:14an area we can improve
43:16as well as even the
43:16ones we already collect. How
43:18do we collect them in
43:19a more meaningful way that's
43:20thinking about the patient's
43:22responses,
43:23and and where they're at
43:24versus
43:25just how things are paid,
43:27I guess.
43:30I I have a,
43:32another question. So I this
43:34last sort of cuts up
43:35sort of when you went
43:35through that list of what
43:36your patients report as as
43:38their goals and sort of
43:39that bottom sort of being,
43:40like, employment or avoiding criminal
43:42justice,
43:43contacts, sort of reconnecting with
43:45family.
43:46I I'd love your thoughts
43:47on sort of like there
43:48are it's challenging,
43:50but there are ways to
43:51get at administrative data sets
43:53to get at some of
43:55and, like, I've spent a
43:56lot of time trying to
43:57get access to that data,
43:59through, like, you know, using
44:01IRS records or using employment
44:03records or using some or
44:04sort of criminal justice records.
44:06It's a challenge. But is
44:08that
44:09doing that type of work
44:10where you're linking sort of
44:11treatment to sort of other
44:12datasets,
44:13does that actually achieve what
44:15you would have in mind?
44:16Because, like, is it still,
44:17like, not
44:18accurate on the patient centered
44:19goals or sort of, like,
44:20still it's like me saying
44:22that optimal goal is
44:24seventy five percent employment or
44:25whatever whatever make up the
44:26number. Is that type of
44:28work sort of meet some
44:29of the needs, or is
44:30it still insufficient
44:31to do that?
44:33Yes. That makes sense.
44:35I think
44:37I don't have a clear
44:38answer to it. I think
44:39that that's more of, an
44:41area I'd like to continue
44:42to dive into, and I'm
44:43happy to talk more about
44:44it as well. I think
44:45that
44:47anything that you look at
44:48besides what is just this
44:50is what we do and
44:51what we've done is an
44:52improvement, because it starts to
44:53understand the complexity
44:55of the patient experience.
44:57And when we continue to
44:58just use the same metrics
44:59because that's what's been used
45:00before, I think we lose
45:02that.
45:03And again, I'm not unique
45:05to it. I I I
45:06am guilty of this as
45:07well sometimes where we tend
45:08to just go, this is
45:08what what's been measured. Let's
45:10stick with it. So I
45:11think anytime you try to
45:12advance that, it's good. But
45:13I think that that is
45:14empirically something that we could
45:16look at. And I think
45:17that you would do studies
45:18where you ask people if
45:20how their treatments are going,
45:21and you also pull in
45:22this data so you understand
45:24if people are getting jobs,
45:25are they feeling like the
45:26recovery is going well? I
45:27mean, it's plausible that getting
45:28employment could actually,
45:30you know, be negative to
45:31recovery, especially if it happens
45:33too soon or something else.
45:34I don't know. So I
45:35don't think it's as easy
45:36to say this is what
45:37we need to measure because
45:38that's gonna solve it. I
45:39think it's
45:40we need to do better
45:42with how we're thinking about
45:44capturing success,
45:45of these policies and these
45:47changes.
45:48Yeah. Along those lines, I
45:50would say we need to
45:50do better with patient reported
45:52outcomes. Yes. It's just a
45:53huge individual variability
45:55in those those values so
45:57that it can be
45:59fairly misleading.
46:00I years ago, I did
46:01a study where we were
46:02looking at quality of life,
46:03reported self reported quality of
46:05life, health report,
46:07in people with HIV.
46:09And the group that had
46:10the quote worst quality of
46:12life were white men who
46:13were relatively healthy.
46:16And the group that had
46:17the best quality of life
46:19were older black men,
46:20which were who were quite
46:22sick.
46:23It's all relative to your
46:24environment
46:25in terms of how you
46:26report those factors. So
46:28I I
46:29well, I think it's really
46:30important to ask people how
46:32they're doing and the kinds
46:33of questions that you're raising.
46:34There are all kinds of
46:36measurement problems
46:37just trying to use those
46:38as outcomes that we need
46:40to figure out how to
46:40solve. I mean, I'm not
46:41saying abandon them, but I'm
46:43saying
46:44be dubious of the current
46:45ones that we use because
46:46they they have a lot
46:47of problems too. Yeah. No.
46:49Everything's
46:49very imperfect here. And I
46:51think in that example, you
46:52give perfectly, like, hopelessness is
46:54one of the number one
46:54predictors of suicide. Right? And
46:56so even if that person's
46:57in a
46:58a good social environment, everything
47:00looks good, but they are
47:01feeling hopeless, you know, they
47:03might be at the highest
47:03risk for an adverse event,
47:05whereas, you know, the other
47:06individual who's at a worse
47:07circumstance. So I I think
47:08it's very challenging, and I
47:10do appreciate the things like
47:11z codes exist, and maybe
47:12that's a piece of it.
47:13I know clinical notes exist.
47:15I don't know. I don't
47:16know. I mean, I think
47:17that that's why I'm presenting
47:18this. This is my this
47:19is where my head is
47:19most days now, is understanding
47:21how to measure these things
47:22better.
47:23And so I I was
47:24hoping to bring them up
47:24so other people would that
47:25are interested and it can
47:26continue this discussion.
47:28I think if you ask
47:29the same person
47:31Yeah. What's been in the
47:32series and they become their
47:33own control Yes. Is a
47:35more useful metric. But when
47:37you try to do it
47:37on a population level,
47:39you get into all kinds
47:40of problems. Yeah. I think
47:41that makes sense.
47:44Thank you for your talk.
47:47I'm just looking at the
47:48first author on the book
47:49that you're describing and thinking
47:50about. Are there communities or
47:52states
47:53in the United States where
47:55where simple, you know, interpretations
47:57have not created
47:58where where they're less inclined
48:00where they're where they're less
48:01reactive. Right? In theory, Connecticut
48:03in theory is is a
48:04pretty
48:05felt to be reasonable, generous
48:07state in terms of thinking
48:08about Medicaid.
48:10But, are there other places
48:11where where they've been more
48:14careful with data in terms
48:15of implementing both
48:18laws or guidelines,
48:21that you can share with
48:22us?
48:24I'm unfortunately not the right
48:25person to ask about that.
48:26I love the question, and
48:28I think it's something that
48:29I'd be very interested in
48:30knowing. I don't think I
48:31have enough
48:33firsthand experience with the policy
48:35making side of it. I
48:36mean, I've definitely I saw
48:38it.
48:39I worked for the US
48:40army for a bit in
48:41the public health command, and
48:42so we would get called
48:43out to do outbreak investigations
48:45of mental health issues by
48:47generals.
48:48And so you saw the
48:49different scope of generals where
48:51some of them would hear
48:51the data and just go,
48:52this is what I'm doing.
48:53I don't really care what
48:54you say. You'd see other
48:55people that want to have
48:56discussion with you. So that
48:58exists on a continuum, and
48:59I think it I'm sure
49:00it exists on a continuum
49:01elsewhere. I don't have examples
49:03of it. The exemplars. Right?
49:04So where where are people
49:05doing it? It doesn't have
49:06to be a cookie cutter
49:07at one size, but it's
49:08all it's gonna be very
49:09good. Yeah. Words.
49:11We know them.
49:12I am too.
49:13I wanna think of it.
49:17Yeah.
49:18Comment or the question comment?
49:20It's very hard to get
49:21people to fill out.
49:25One of the problems with
49:26the metric.
49:28The how what you're asking
49:29them to do,
49:31what they do. In the
49:32cancer field, roughly, I don't
49:33know, seven or eight years
49:34ago, a huge study came
49:35out, randomized.
49:37People were getting chemo.
49:39Half of them were randomized
49:40to patient
49:42electronic, and the
49:44alert would go off if
49:45they're having symptoms and the
49:46other half is usual care.
49:49People randomized to the PRO
49:52group ended up having
49:54actually better survival
49:55because the, the bell would
49:57go off if they're nauseous,
49:58and the nurse would call
49:59them and say, here you're
50:00nauseous. Let's give you this.
50:01We'll keep you out of
50:02the ER. They could stay
50:03on their treatment longer. Mhmm.
50:05If we were able to
50:06finish the course of therapy.
50:07The people came back from
50:08the big ASCO annual meeting.
50:10So this is a plenary
50:11presentation, and the person did
50:13a great job presenting
50:14to you. Basically, putting up
50:15their PRO, have the intervention,
50:18comparing it to all the
50:19big chemo drugs. Like, this
50:20is just as good as
50:22Beviz is and that. Then
50:24checkpoint,
50:25it sounds a lot cheaper.
50:26So everybody came back across
50:27the country. We've gotta do
50:29this. The of course, this
50:30is we gotta start measuring
50:31PROs and our time.
50:33Five years later, seven years
50:34later, almost nobody's doing. It's
50:36just real big so a
50:37big challenge
50:38is to figure out not
50:40only what to measure, when,
50:42where, and how to use
50:42it, but,
50:44how to make sure that
50:46we're
50:47eventually,
50:49getting clinics
50:50getting this embedded into the
50:51clinical
50:52workflow. Mhmm. Be one of
50:54the
50:55main people I've found helpful
50:56is if patients can see
50:58it helpful for them
51:00in some way. So I'm
51:00just thinking
51:02about how to improve
51:03it here. It's what we
51:04figure out what we're trying
51:05to do. Yeah. No. I
51:07I find that that's a
51:08interesting area that I haven't
51:10done as much in. I
51:11with prescription drug monitoring programs,
51:12we did a little bit
51:13in how the clinical workflow
51:14and understanding how that, like,
51:16enters into it.
51:18It's, that's a whole other
51:20area of study that I
51:20think I find interesting, but
51:22I am not as familiar
51:22with. So I hope I
51:23can learn more about that.
51:25Question was, just as far
51:27as, interventions, just thinking about
51:29this PRO Yeah.
51:30People's goals and
51:35what we want to.
51:36Are there inter studies ongoing
51:38or,
51:40interventions that are being evaluated
51:42that are basically
51:44multi prompt or adjuncts to
51:49traditional pharmaceutical
51:51approach
51:52that would be
51:53that have been shown to
51:54be effective in some way
51:56as far as, you know,
51:58having getting jobs, take avoiding
52:00conservation and things like that.
52:02I mean,
52:04yes and no. I I
52:05think that there are clinical
52:06trials that are collecting those,
52:07but that's where it's really
52:08at is the clinical trials.
52:09I haven't seen it outside
52:10of that. So again, this
52:12those CTN studies that are
52:13being done through the NIH
52:15of the clinical trials network,
52:16like, they definitely have those
52:17outcomes and they have published
52:18papers that have looked at
52:19those kind of things for
52:21buprenorphine and naltrexone, these other
52:22drugs.
52:24Haven't seen it outside of
52:25a clinical trial.
52:29But it's a discussion. I
52:30mean, editorials are everywhere now
52:31on this. So let's let's
52:33you know, this this came
52:35to my attention a few
52:35years back, and now it's
52:36like, I I feel like
52:38it's a flood, you know.
52:39I I think the scientific
52:40consensus kinda moves, and I'm
52:41not unique to it. So
52:43I think it's discussions that
52:43are happening. It's just nobody
52:45knows how to do it.
52:47Well, I have one more
52:47question on the Zoom. Is
52:49there okay.
52:51Good. One. Three question.
52:53Julia, do you wanna jump
52:55on?
52:57Hi. Sure.
52:59Thank you so much for
53:00sharing this fascinating work. So
53:01I'm thinking about the global
53:02perspective,
53:03making it even more complex.
53:05And,
53:06of course,
53:08we want to be able
53:09to compare what happens
53:10in different contexts
53:12and learn from that.
53:14But then this,
53:15question that you are,
53:18grappling with and showing us
53:20becomes even more complicated because
53:22validating
53:23even just one instrument for
53:24different countries takes a lot
53:26of effort and a lot
53:27of work.
53:28And then,
53:29if you validate it for
53:31the language and then, for
53:32example, like, if it's the
53:34language in England versus the
53:35language in Ireland, this is
53:37also,
53:38an extra validation process. So
53:41is it, you know, is
53:42the juice worth the squeeze?
53:44Do you think that
53:45this is effort wisely spent?
53:48What would be your recommendation
53:50given what you've been learning?
53:52Yes.
53:53That's a a great point.
53:59Is it worth it? I
54:01mean,
54:02I think it is, but
54:03I I think I also
54:04understand that
54:06it's going to have translational
54:08problems.
54:09And so then it needs
54:10to be studied again and
54:11again in different contexts. So
54:14I think that the one
54:15of the questions that will
54:16have to come up is
54:17what is the consequence of
54:18using one measure over another?
54:20And I don't think we
54:21have that yet.
54:22And so maybe
54:24there have been studies that
54:25looked at hundred and eighty
54:26days and found it to
54:27be associated I'm I'm just
54:29using this example. I found
54:31it to be associated with
54:33reductions and, overdose deaths, and
54:35that's probably the main outcome
54:37they probably looked at. But
54:39it's not a perfect relationship.
54:41And so like many
54:43things, there's a lot
54:44left that's not understood. And
54:46so, like, you know, if
54:47it reduces if it's a
54:49two percent reduction in overdoses,
54:51that could be scientifically significant,
54:53and could be something that
54:53we continue to use for
54:54that reason.
54:56But is it meaningful? And
54:57I I think that's a
54:58discussion that we have to
54:59continue to have. I I
55:00don't have an answer for
55:01it.
55:04I I I hope that
55:05next time I present, I
55:06will have more thoughts as
55:07I kinda dive into this
55:08research more empirically and understand
55:10that. But I I think
55:10that's a great point of
55:12understanding how much are we
55:13gaining from these different measures.
55:15It's one thing to to
55:17encourage researchers and policymakers to
55:19think about the whole
55:21individual instead of just a
55:22piece of it. It's another
55:23to say what that costs,
55:25and that's not something I
55:26do as much. So I
55:28appreciate
55:29being reminded of my own
55:31limitations,
55:33in that piece. So, yeah,
55:34I think that's an interesting
55:35point to continue to think
55:36about.
55:38Well, it's a great session.
55:39Thank you. Thank you.