Climate Change and Health Seminar: "Heat health effects: research to inform policy."

Transcript

• 00:00- Good morning, everyone.
• 00:03Noon.
• 00:04Welcome to the Yale Center on Climate Change
• 00:06and Health seminar.
• 00:08I'm your host today, Dr. Kai Chan,
• 00:10assistant professor at the Yale school of public health.
• 00:14During the presentation if you have any questions
• 00:17you can use the chat box
• 00:19and we will try to address them as the speaker finishes.
• 00:24As a reminder, today's seminar will be recorded.
• 00:27So, it is my great pleasure today to introduce our speaker
• 00:33professor Greg Wellenius from Boston university
• 00:37school of public health.
• 00:38So Greg is actually the 2019,
• 00:41recipient of the ISEE Tony McMichael award.
• 00:46So it is very exciting to have Greg here today because,
• 00:50everyone knows Tony McMichael was the pioneer
• 00:54that developed the connection
• 00:56between epidemiology and the global countries.
• 00:59So with that legacy,
• 01:01I would like to take it over to Greg and very much,
• 01:05looking forward to your talk.
• 01:08- Wonderful.
• 01:09Thank you, Kai.
• 01:10Thanks so much for the invitation to speak here.
• 01:12And I only wish we could meet in person,
• 01:14under better circumstances.
• 01:16I was telling Kai before
• 01:19a few minutes earlier that one of the great pleasures
• 01:22of giving seminars in places is visiting with the people
• 01:26in small groups.
• 01:28So hopefully we'll have the opportunity to do that,
• 01:31again shortly.
• 01:33So let me share my screen.
• 01:39Okay.
• 01:40So you should be able to see my slides,
• 01:42Kai, give me the thumbs up or somebody can see my screen.
• 01:48Okay, great.
• 01:49So we'll just go ahead and get started.
• 01:52So yeah, so feel free to stop me along the way.
• 01:55I will rely on Kai to flag me down
• 01:57if you wanna put questions in the chat window
• 02:00and then I can stop,
• 02:02I don't mind being interrupted
• 02:03and that way we can make it more interactive that's fine.
• 02:06I should mention that,
• 02:07I am currently a visiting scientist
• 02:11working with Google and, this
• 02:17nothing I say here should be interpreted
• 02:19as being the official position of Google.
• 02:22All right.
• 02:23So with that I will get started.
• 02:26So I wanted to talk today about the effects
• 02:29of heat on health, which is,
• 02:32very well described in the scientific literature
• 02:35and connect that to
• 02:37why we have sort of this disconnect between,
• 02:41what we know about heat and the fact that
• 02:44people continue to die of a heat related illness.
• 02:49So the problem, as I see it is that excess heat
• 02:51is a widely recognized threat to public health.
• 02:54It's often cited based on CDC statistics
• 02:57that in the U.S more people die
• 02:59of extreme heat each year
• 03:00than of any other meteorologic event.
• 03:03So despite all this knowledge,
• 03:06that we have about the risks of
• 03:09days of extreme and perhaps moderate heat,
• 03:12there seems to have been remarkably little progress
• 03:15towards preventing heat related illness and death.
• 03:17So we still see that heat waves
• 03:19are a major source of morbidity and mortality
• 03:22across the world.
• 03:23And so this got us thinking that
• 03:26this suggests a lack of translation
• 03:27of the abundance scientific knowledge about risks
• 03:30into public health action.
• 03:32And so just to highlight the point
• 03:35for those that may not be as familiar.
• 03:37So a Seminole study by Antonio Gasperini and colleagues,
• 03:41London school of hygiene, tropical medicine,
• 03:44published several years ago
• 03:45and have since published extensively,
• 03:47globally on the impacts of heat on health.
• 03:51And just to zoom in on a couple of locations,
• 03:55you could see that there's this,
• 03:58U shaped relationship between,
• 04:01daily maximum temperature,
• 04:02is typically used and the relative risk of
• 04:04some adverse outcome in this case mortality.
• 04:07And you can see that there is a temperature,
• 04:11what we'll call the temperature of minimum mortality,
• 04:12or the optimal temperature at which the fewest
• 04:15number of people die.
• 04:18And then as temperatures get warmer than that,
• 04:21you see a sharp increase, in,
• 04:24the relative risk of mortality and the shape of this curve,
• 04:28varies from location to location,
• 04:30but the pattern has been shown throughout the world,
• 04:35by Gasperini and colleagues, as well as
• 04:39other groups in specific locations.
• 04:41So this is pretty universal
• 04:42and pretty well understood at this point.
• 04:45In the U.S we additionally know,
• 04:48about the effects on morbidity.
• 04:51So as measured by hospital admissions.
• 04:53So this is some terrific work done by Jennifer Bob
• 04:56working with Francesca Dominici at Harvard and team.
• 05:00And, so this was in the Medicare population
• 05:03looking at millions of hospital admissions
• 05:08for a number of different causes and showing
• 05:11both the relative risk and the risk difference of,
• 05:14hospital admissions for different causes that you can see.
• 05:20Increased relative risk of fluid
• 05:22and electrolyte disorders, renal conditions,
• 05:23urinary tract infections, heat stroke,
• 05:27and other external causes.
• 05:29And, with the risk difference shown there as well.
• 05:34So, interestingly although heatstroke
• 05:37has the biggest relative risk
• 05:38because it's relatively uncommon as a diagnosis,
• 05:41the risk differences is smaller than for some other causes.
• 05:45So terrific work.
• 05:47So this is just a sampling.
• 05:48There's a huge literature now on this,
• 05:50and very large studies demonstrating
• 05:52that extreme heat is associated with higher rates of death
• 05:55and hospitalization all across the world.
• 05:58Moderate heat is associated with higher rates of death, and,
• 06:02building amounts of evidence suggesting also
• 06:05with hospitalization.
• 06:07And we know that the vulnerability of these effects
• 06:10varies by personal housing
• 06:12and neighborhood characteristics.
• 06:14Further we know that the U.S has already warmed
• 06:17more than a degree and is projected
• 06:20to warm further through the end of the century
• 06:21in substantially with that,
• 06:24regional substantial regional variation and how much,
• 06:27further warming we expect to see.
• 06:31So how do we translate this into action
• 06:35that actually saves lives and reduces the health impact?
• 06:39So local public health and emergency
• 06:41preparedness officials
• 06:43need to know something a little bit different.
• 06:44They need to know what are the health risks
• 06:46associated with a given climate hazard in my location,
• 06:49what local actions can I take
• 06:52to protect the public health
• 06:53and do these actions actually work?
• 06:55So I'm gonna walk you through some of the research
• 06:58that we've done in this domain.
• 07:02And I'll start with what are the health risks
• 07:03associated with a given climate hazard
• 07:05in a particular location?
• 07:07So I started this work when I was in Rhode Island,
• 07:11actually Julia Gold at the time
• 07:14at the Rhode Island department of health,
• 07:15came to me and said,
• 07:17we really wanna know how many people
• 07:20are dying of heat and Rhode Island and how many ed visits,
• 07:23we have in Rhode Island.
• 07:25We need to know how to prioritize this.
• 07:27And I said, well, there's lots of literature
• 07:28it's a big problem you should just be worried about it.
• 07:30And she said, no, can you give me a number?
• 07:32And so I said, okay sure
• 07:34let's try to give a number.
• 07:36And then it turned out that New Hampshire and Maine
• 07:39were also in interested in the same question.
• 07:42Public health officials in those States
• 07:45were interested in the same question.
• 07:47And because this was done at small,
• 07:49relatively smaller populations,
• 07:53we all had the challenge
• 07:54of having sufficient statistical power,
• 07:57to examine the associations
• 07:59between heat and either mortality or ed visits,
• 08:02in our own communities.
• 08:04So we partnered with between Rhode Island,
• 08:09New Hampshire and Maine to pull data,
• 08:12do the analysis in each of the community shown here
• 08:15and then pull the results to have enough statistical power.
• 08:19And we also engage with the regional offices
• 08:21of the national weather service, in order,
• 08:25they were interested to reconsider the
• 08:30threshold criteria at which the,
• 08:33heat advisories or heat warnings were issued
• 08:35based on local evidence.
• 08:37So we were trying to provide local actionable evidence,
• 08:41and in particular in communities outside of
• 08:43the large cities of the area that would otherwise,
• 08:46dominate the signal.
• 08:49And so we found what you'd expect is that the,
• 08:52here we were interested in heat index,
• 08:54'cause we were doing this in partnership
• 08:57with the national weather service and heat index is
• 08:58this combination of temperature and humidity that,
• 09:01they often use for issuing heat warnings
• 09:03and heat advisories.
• 09:05And we found approximately what we expected,
• 09:08that there was a monotonic relationship
• 09:10between increasing maximum daily heat index
• 09:13and relative risk of emergency department admissions
• 09:16that you see there on the left
• 09:18and deaths there as you see there on the right.
• 09:21And, these were about of the expected magnitude.
• 09:24And you can see that even pooling across these 15 locations,
• 09:27the confidence intervals around our estimates of,
• 09:30for mortality relative to some mortality
• 09:33were somewhat imprecise.
• 09:35So, the, I think the key part of this is,
• 09:40to translate sort of relative risks
• 09:42and smooth curves, which are available,
• 09:47with standard software now,
• 09:49thanks in large part to work by Gasperini and colleagues,
• 09:54is to translate that into real numbers.
• 09:57So, okay.
• 09:59So a curve is all good but how does that translate to
• 10:03number of excess ed visits or excess deaths
• 10:07attributable to days of different heat indices?
• 10:11So we created this table where the bottom row here
• 10:14shows you on all the days of 100 degrees
• 10:18with a heat index of 100 degrees or higher,
• 10:20how many excess deaths,
• 10:21excess CD visits were there on the same day, or,
• 10:25incorporating the lag effects up to seven days.
• 10:29And so, across these 15 new England towns,
• 10:31there were 39 additional ed visits
• 10:34on all days over 100 degrees and 232.
• 10:39If you incorporate the lag structure,
• 10:42the fact that the next day
• 10:44and the next day might also have some excess ed visits
• 10:46and about four to eight excess deaths
• 10:50for the days above 100 during this time period.
• 10:52And, obviously there's more days that are at,
• 10:55or above 95 degrees.
• 10:57And so then, those numbers are bigger and, at,
• 11:01or above 95 degrees,
• 11:02there's close to 200 to 700 depending on,
• 11:06how far out in the delay you want to incorporate,
• 11:11excess ed visits.
• 11:14So we took this information to the national weather service,
• 11:17to the regional office for the national weather service
• 11:19and said, look, we think that at temperatures below that,
• 11:22at which you currently issue heat advisories.
• 11:25So during this time heat advisories were
• 11:27issued by the national weather service for days
• 11:30with a heat index forecast to be above 100 degrees.
• 11:33We said, look at days as low as 95 or 90,
• 11:37we still see excess ed visits.
• 11:40And you can see that in the curves too, that,
• 11:42it's relatively monotonic so there's no reason
• 11:44to pick just 100 degrees as the threshold.
• 11:46It could be even at 95 degrees, you could,
• 11:50presumably warn or prevent
• 11:53some excess morbidity and mortality.
• 11:56And the national weather service said, okay
• 11:59that's great.
• 12:00And, so let me
• 12:05I'm gonna skip ahead to the national weather service.
• 12:08Okay, sorry.
• 12:10So before I get to the national weather service story,
• 12:13so 'cause I think that's really important, but then,
• 12:15so I want to shout out to Kate Weinberger,
• 12:18who was a postdoc in my group at the time.
• 12:20And what she said is, okay, this is great for New England,
• 12:21but how many people die of
• 12:24deaths attributable to heat across the country?
• 12:28And so using data that we had a mortality through 2006,
• 12:32she estimated that there were 5,000 or more excess deaths
• 12:39per year across the U.S attributable to heat.
• 12:42This number is really important because
• 12:43it's about an order of magnitude
• 12:45higher than what the CDC estimates
• 12:51report for heat related deaths that are those
• 12:54that are coded as being due to heat.
• 12:57And so when we think of sort of the,
• 12:59public health burden of disease of heat related illness,
• 13:03the CDC estimates, are important,
• 13:06but we think a likely an underestimate
• 13:09of the true excess mortality due to heat.
• 13:12The other important point here is
• 13:15that if we separate out the extreme heat days
• 13:18versus the moderate heat days,
• 13:20so we defined extreme heat
• 13:22as those days above the 95th percentile
• 13:24for a particular location.
• 13:26And these 297 counties across the U.S.
• 13:30The burden of disease is actually bigger for,
• 13:34deaths due to moderate heat.
• 13:35And that's been reported previously,
• 13:38across the world and in the U.S but it,
• 13:41this puts concrete numbers on that that
• 13:44moderate heat accounts for a substantial burden of disease.
• 13:49And the other key point from this study is that, the risk,
• 13:54or the excess mortality is not distributed uniformly
• 13:58across the U.S and there's parts of the country,
• 14:01that seem much more vulnerable to,
• 14:05heat-related mortality than others.
• 14:08Again, emphasizing the importance of local knowledge
• 14:10and local action to prevent these.
• 14:14Okay, so let's turn to local actions,
• 14:17that can be taken to protect the public's health
• 14:20and evaluating if these actions actually work.
• 14:24So in the U.S the national weather service issues,
• 14:27heat, advisories, and excess heat warnings
• 14:30when the heat index is forecast to be high.
• 14:33Now, and this is for most places,
• 14:35there's a handful of places
• 14:38that use the other criteria besides heat index.
• 14:40But these warnings that are issued,
• 14:42provide information that the public can take,
• 14:44of actions that the public can take to protect their health.
• 14:48And in some places the warnings may also trigger
• 14:52activation of local heat response plans,
• 14:55that may involve things like opening cooling centers, or,
• 14:59reaching out to particularly vulnerable communities
• 15:02in addition to targeted messaging,
• 15:05and the optimal thresholds for issuing
• 15:07these heat advisories or heat warnings,
• 15:11remain largely unknown or unstudied,
• 15:15refer to heat advisories and warnings together
• 15:18as heat alerts.
• 15:19So based on the work we did in that New England study,
• 15:24working with the national weather service regional office,
• 15:27they decided to partition the Northeast, which was,
• 15:31had one criteria for issuing heat advisories
• 15:35prior to this work starting in summer 2017,
• 15:37they changed it so that the,
• 15:41new way in New England was treated separately
• 15:43from the rest of the Northeast,
• 15:45acknowledging that the vulnerability
• 15:47to a heat related illness might be different in New England,
• 15:53not just based on our study,
• 15:56there's other studies that have shown that as well.
• 15:58So this felt like a major public health victory.
• 16:00So following this starting of the summer of 2017,
• 16:04the national weather service in the region,
• 16:07issued heat advisories when the heat index
• 16:12was forecast to be greater than 95 degrees.
• 16:17And there was some confusion as to whether
• 16:18that should be for one day or for two days,
• 16:20it was initially for two days.
• 16:21And, then they subsequently revised the criteria,
• 16:25to be consistent across the New England region.
• 16:29So essentially changing the heat advisory threshold
• 16:32from 100 degrees heat index to 95 degrees heat index.
• 16:36So this felt like, to me,
• 16:37a major public health victory, this was, one study,
• 16:41one paper that, and a series of conversations
• 16:45that ended up changing the criteria,
• 16:48at which heat advisories are issued for,
• 16:51a region with a substantial population.
• 16:53So that felt very impactful,
• 16:55but it leads to the question of okay,
• 16:57so we're issuing more heat advisories now
• 17:00than we were before,
• 17:02because we've changed the threshold.
• 17:03Does that actually save anybody's life?
• 17:06So, we weren't the first or the only ones
• 17:10to be having this type of conversation.
• 17:12We followed in that research some very nice work,
• 17:17from New York city,
• 17:18where they also informed local policy
• 17:21through evaluation of data in New York city.
• 17:24And so the question we were asking is,
• 17:28what is the optimal threshold for issuing heat alerts,
• 17:32heat warnings, and heat advisories.
• 17:35But these conversations assume that issuing
• 17:38heat advisories and warnings actually
• 17:40reduces heat-related morbidity and mortality.
• 17:44And there's been relatively few studies on that question.
• 17:48What, again, there's a handful of studies,
• 17:51but one that I particularly like is this study from,
• 17:56Tarik Benmarhina while he was still at McGill and looking,
• 18:01taking a very creative approach to looking
• 18:03at the effectiveness of the heat action plan that including
• 18:06included a new heat early warning system on,
• 18:11heat related mortality in Montreal.
• 18:14And, that team reported that the,
• 18:20that having this heat action plan implemented in Montreal,
• 18:25reduced mortality during hot days
• 18:27by about two and a half deaths per day,
• 18:30and with particularly larger effects amongst the elderly.
• 18:34So we wanted that's exactly the question
• 18:37we wanted to ask is the issuing of heat warnings,
• 18:39heat early warning system.
• 18:41How much does that benefit the population?
• 18:44So we built this study on the advantage
• 18:49that heat warnings are issued by people,
• 18:52and they're issued on forecasts.
• 18:54They're not completely algorithmic.
• 18:56They are issued by specialists
• 18:58at the national weather service
• 19:00that are focused on heat warnings.
• 19:02And, they,
• 19:05there's a collection of days where we forecast
• 19:08that there will be a high degree of heat.
• 19:13And then it turns out to be a little bit less,
• 19:15and then there's other days where we forecast,
• 19:17lower heat levels.
• 19:22And it turns out to be a little bit higher.
• 19:23So the forecast can be wrong even just a little bit.
• 19:26And because they're issued by people,
• 19:28there's some discretion in how much they think
• 19:31people need to know about the upcoming heat.
• 19:34So for instance, we were told that on the 4th of July,
• 19:37you might issue a heat alert at a slightly lower,
• 19:40forecast heat index, then on another day,
• 19:43because so many people are gonna be outside.
• 19:45So many people are going to be exposed that maybe,
• 19:48we can have the flexibility to change that threshold.
• 19:51And that was entirely built into the system.
• 19:54So there should be these days with a similar heat index,
• 19:57right around sort of the warning threshold,
• 20:01some of which have a heat warning some of which do not.
• 20:05And so that's the
• 20:09paradigm we were taking advantage of.
• 20:11And at the time we had data on heat warnings from 20 cities
• 20:17that issue heat warnings regularly.
• 20:20And, we matched us to the mortality data we had
• 20:22from the CDC.
• 20:25So the overlap between these two data sets is 2001 to 2006.
• 20:31And, again, comparing days of similar heat index,
• 20:37with versus without a heat alert,
• 20:40this is the relative risk of mortality,
• 20:43associated with having a heat alert.
• 20:46And so if he'd warnings or heat advisories were,
• 20:49protective of the population,
• 20:51you would expect to see a decreased,
• 20:54relative risk or a decrease in the rate of mortality
• 20:59on days with a heat alert compared to without.
• 21:01So interestingly, we did not see that
• 21:03across these 20 cities,
• 21:05overall there was a null association.
• 21:08And the one place where we did see an association was,
• 21:13Philadelphia with a reduction of about 4%
• 21:16in mortality of about 4% on days
• 21:17with a heat warning versus without.
• 21:19So this could be for a couple of reasons.
• 21:23One Philadelphia, we know has been very proactive about,
• 21:27having a robust heat early warning system
• 21:29and taking action on days expected to have high mortality.
• 21:35It could also be that this was 20 estimates,
• 21:39and that one out of 20 was,
• 21:41in the direction that we expected.
• 21:44So clearly needs a followup study,
• 21:48but then we played the thought experiment of
• 21:52so heat alerts were effective
• 21:55at reducing mortality in Philadelphia.
• 21:57And the number of deaths we estimated,
• 21:59that were averted in Philadelphia
• 22:03each time they issued a heat alert,
• 22:05was about four and a half or five lives per time.
• 22:09And so if you extrapolate that to the,
• 22:13typical year in Philadelphia during this time,
• 22:16that meant that the heat early warning system
• 22:18saved about 45 lives per year.
• 22:21Again, lots of assumptions of causality,
• 22:23but it gives us a starting point that if the,
• 22:29if heat warnings could be as effective
• 22:32as they were observed to be in Philadelphia
• 22:35during this time then a city like New York,
• 22:38or Dallas or Phoenix,
• 22:41could potentially save avert quite a few lives per year,
• 22:47depending on the effectiveness of the heat warning
• 22:49and how often the heat alerts are issued per year.
• 22:53So this provides,
• 22:55a rough for back of the envelope calculation as to
• 22:58how many lives could potentially be averted each year,
• 23:03across the country if heat warnings, reduced,
• 23:09mortality by the same magnitude as we saw in Philadelphia.
• 23:15Okay.
• 23:16And, again,
• 23:17I want to emphasize that we're not the only ones
• 23:19that have considered this question.
• 23:20This is some great work by Kristie Ebi
• 23:2515 years earlier, showing that in Philadelphia, exactly.
• 23:31The heat warning system, she estimated,
• 23:33each time that a heat warning
• 23:36was activated at saved two and a half lives per day.
• 23:40So, in the same ballpark of the estimates,
• 23:44we were seeing but in a very different time period.
• 23:47Okay, so there's lots of limitations to this study.
• 23:50One of them is that the data we were using
• 23:52at the time was old, was mortality data through 2006.
• 23:56So, Kate Weinberger has since been updating this,
• 24:02sorta with more recent mortality data from,
• 24:06nine Northeastern cities
• 24:07where we found the data readily available
• 24:10in collaboration with Joel Schwartz and team.
• 24:12And, there, we, she found, that perhaps,
• 24:173% mortality benefit on heat warning days versus,
• 24:21days with versus without heat warnings.
• 24:24So maybe it's just that in 2006 and earlier,
• 24:28when most places did not yet have a heat action plan, then,
• 24:32we don't see very much of a benefit,
• 24:34but in more recent times where,
• 24:35many more communities do have heat action plans,
• 24:38tied to those heat alerts that we see,
• 24:43perhaps some signals so we're following that up
• 24:46in a broader population.
• 24:47And then the other question is of course,
• 24:50is that mortality is not the only outcome of interest that,
• 24:53we also want to prevent illness,
• 24:56as reflected through hospitalizations.
• 24:59And, here we saw in 97 counties
• 25:03in 2007 to 2012,
• 25:06using Medicare hospital admission data.
• 25:10We found no reduction
• 25:13in the risk of emergency hospitalization
• 25:16during this time point.
• 25:18So again, to works in progress that,
• 25:22we're following up on a larger scale
• 25:23and with more recent data.
• 25:27Okay,
• 25:29so our national weather service heat warnings effective,
• 25:32they may reduce the risk of death in some cities,
• 25:35but we don't yet see evidence of
• 25:37widespread health benefits.
• 25:40And if that's true and again it needs to be confirmed,
• 25:43but that would represent a missed opportunity
• 25:47to prevent heat-related morbidity and mortality.
• 25:51There's lots of limitations to the analysis I've shown here,
• 25:54and we're working to actively to address these limitations.
• 25:59So I just wanna emphasize the,
• 26:02that we're at the beginning of the road here not the end.
• 26:05Okay, so I wanna turn to talking about,
• 26:09how susceptibility to heat related illness
• 26:13might vary by age groups.
• 26:15And, so in one of the first studies we did in Rhode Island,
• 26:20we looked at emergency department visits,
• 26:23to the to Rhode Island over several years now,
• 26:27there's only a million people in Rhode Island.
• 26:29So again, there's an issue about statistical power.
• 26:35But the interesting thing is that, of course,
• 26:37we all think of the elderly as really vulnerable.
• 26:39And what we saw is that for heat related ed visits,
• 26:44in fact, the relative risk was a lot higher,
• 26:48so this is excess relative risk.
• 26:50So these are percents.
• 26:51So this would be an odds ratio of 1.6, approximately.
• 26:56So that the relative risk was actually higher in
• 26:59that study for population of adults of non elderly adults,
• 27:0318 to 64 and with significant for kids also
• 27:07or children and adolescents 18 and under,
• 27:11so what to follow that up.
• 27:15More recently we partnered with Ari Bernstein, the Harvard,
• 27:20center for climate health and the global environment,
• 27:25and using data from on ed visits from a network
• 27:31of standalone U.S children's hospitals.
• 27:34These are 47 hospitals and the recent Tara
• 27:37with a total of three point million ed visits,
• 27:39amongst children and adolescents.
• 27:42And you can see the location of the hospital here
• 27:44as well as the relative size and contribution.
• 27:48And so a little bit hard to see here,
• 27:52but so what we see is that the overall relationship between,
• 27:57maximum daily temperature and the relative risk
• 28:01of ed visits for all causes in
• 28:03this population is a 1.17 or about a 17% increase.
• 28:07And for heat related illness it's about
• 28:10a relative risk of 1.83.
• 28:12And again, you see it's interesting for all cause ed visits,
• 28:18there's not a lot of heterogeneity by age,
• 28:21but there does seem for heat related illness
• 28:22specifically seem to be somewhat of a stronger effect
• 28:25amongst the older adolescents.
• 28:28So that was really interesting.
• 28:32And then we wanted to sort
• 28:34of move beyond heat related illness
• 28:36to look at a number of potential causes.
• 28:39And this is a little bit hard to see.
• 28:41So I just wanna zoom in a little bit.
• 28:42So to the, we considered a number
• 28:45of different categories of disease,
• 28:47some of them that we sort of had prior hypotheses for,
• 28:51and some that seemed like we should just check.
• 28:53And these are adjusted for multiple comparisons
• 28:56in this sort of more agnostic analysis.
• 28:59And you can see that heat related illness of course
• 29:02comes up with a very high relative risk,
• 29:04but there's other interesting
• 29:06and much less explored associations
• 29:09between different causes of ed visits
• 29:11in children and adolescents and temperature.
• 29:14So, more to be done there,
• 29:17but we're quite excited by these results.
• 29:21I'll make the point as in the paper
• 29:24I showed you at the beginning by Jennifer Bob
• 29:26and colleagues that not all the,
• 29:30those conditions with the highest relative risk
• 29:32don't always have the biggest sort of numeric impact.
• 29:36So heat related illness here,
• 29:39you see the attributable fraction.
• 29:41So of the heat related illness
• 29:42a substantial proportion are due to heat.
• 29:45And, but heat related illnesses
• 29:49and in frequent or uncommon diagnosis.
• 29:53And so the out of 100,000 ed visits,
• 29:56it contributes a relatively small proportion.
• 29:59Whereas for injury and poisonings are very,
• 30:01very common diagnosis amongst kids, as,
• 30:04so even though the attributable fraction
• 30:08is smaller for them the attributable number
• 30:10per 100,00 ed visits total
• 30:13is much bigger because it's much common.
• 30:17Okay, so I wanna share with you some,
• 30:20very exciting work that Darren Son in my group is,
• 30:24leading and working on.
• 30:25So this is now turning to 18 to 64 year old individuals.
• 30:30And this is amongst an insured population,
• 30:32working with data from the Optum labs.
• 30:39And obviously here you have the number of sorry,
• 30:44the average summer maximum temperature.
• 30:46And then this just shows you sort of the distribution
• 30:48of where we have information on in this population.
• 30:52So it tends to follow,
• 30:54the distribution of population
• 30:57focused on obviously more urban locations.
• 31:01But, this particular data set has a more info
• 31:04tends to have more information in the Southeast
• 31:06and in the Southwest.
• 31:09And, you can see here is
• 31:13that overall there's a relative risk of ed visits,
• 31:17amongst these non elderly adults an odds ratio of 1.1,
• 31:24let's say about a 9% increase in risk
• 31:26and for heat related illness
• 31:28it's a relative risk of about 1.9.
• 31:32And again, you see some variation in,
• 31:36the relative risk by age,
• 31:38some heterogeneity by age that we'll explore
• 31:40a little bit further to see.
• 31:43It's interesting though that sort of repeatedly
• 31:45we're seeing that although elderly are known to be,
• 31:49and there's good evidence
• 31:50that they are a susceptible subgroup,
• 31:53that's by no means the only part of the age distribution,
• 31:56where we have sensitivities and in there's,
• 31:58we know of from other studies, outdoor workers,
• 32:01children that spend a lot of time outside,
• 32:04perhaps children's spending time
• 32:05in non-air conditioned schools,
• 32:07can also be quite a bit at risk.
• 32:12Okay.
• 32:13So turning back to the, the bigger, framework.
• 32:16So on a global and national scale,
• 32:18we think that we understand
• 32:20the adverse health impacts of heat.
• 32:22But there's been this lack of translation
• 32:24of abundance scientific knowledge on the risks
• 32:27and to public health action in terms of prevention.
• 32:30And so, again,
• 32:32this means that there's insufficient evidence
• 32:35to guide the public health response
• 32:36to present day or future heat.
• 32:39If we were designing, optimal response to heat,
• 32:44Jeremy Hess and Kristie Ebi have written nicely about this,
• 32:48you'd define dangerously hot weather,
• 32:50you'd forecast it well,
• 32:52you'd identify who's at greatest risk of these effects.
• 32:55You'd intervene to reduce those health impacts,
• 32:58and you'd evaluate the effectiveness of those interventions.
• 33:01And you do this on a continuous cycle.
• 33:03You'd do this repeatedly to continue to optimize.
• 33:07So, our broader research agenda
• 33:11follows mirrors these image.
• 33:14So, the vision that we have is that
• 33:17we could provide the evidence needed for any community
• 33:19in the U.S to mitigate the adverse health impacts
• 33:22of extreme heat.
• 33:23And I'd probably amend that now to say
• 33:26both extreme and moderate heat,
• 33:28although we recognize
• 33:29that they require different strategies,
• 33:30the same strategies won't be effective for both,
• 33:33thinking about moderate and extreme heat.
• 33:36The concrete sort of next steps in that is
• 33:39to identify optimal health based and location
• 33:41specific metrics for issuing heat alerts.
• 33:44We wanna follow up our work on the benefits of
• 33:49heat alert's heat warnings and heat advisories,
• 33:53because I think there's
• 33:55they're probably effective in some circumstances
• 33:58in some places and in some populations.
• 33:59And if we knew where they are effective
• 34:02and under what conditions,
• 34:03then we can presumably provide information
• 34:06that helps other communities replicate that effectiveness.
• 34:09I think there's a lot of potential benefit,
• 34:11to investigating that further.
• 34:15And you, one of the shortcomings in this line of research
• 34:20is that we don't actually have
• 34:21a centralized database of which,
• 34:24what local health departments are,
• 34:26what actions local health departments are taking
• 34:29in response and preparation for,
• 34:31and in response to days of extreme heat.
• 34:33And so one of our goals is to try to,
• 34:36catalog that we're working with Jeremy has and Nicole era,
• 34:40at university of Washington.
• 34:43And then if we can identify again,
• 34:45the key elements of these interventions and
• 34:48where they're most effective,
• 34:50then we can share this information back
• 34:51with local health departments and say,
• 34:53"hey, if you have limited resources and you,
• 34:55"here's what has worked in other settings
• 34:58"that are similar to your settings
• 35:00"in terms of whatever characteristics,
• 35:02"we wanna have about the community.
• 35:06Okay, so I wanna acknowledge also that,
• 35:09heat doesn't happen alone.
• 35:11This is some great work done by Keith Spangler,
• 35:14who is currently a post-doc in working in my group.
• 35:17And this was part of his doctoral dissertation at Brown.
• 35:19And what you see here is different hazards across different,
• 35:26across New England, sorry.
• 35:27So, this is a probability of one or more days
• 35:32with the heat index above 95 degrees.
• 35:35And so you could see the distribution of that.
• 35:37So there's parts of New England that are more prone
• 35:40to getting really hot days.
• 35:42The distribution of getting an inch or more of rainfall
• 35:46is quite different.
• 35:48And similarly, the distribution of the
• 35:51risk of high ozone days is again different.
• 35:54And we don't have high PM 2.5 levels in New England.
• 35:58But, if you were to look at where they are highest,
• 36:02you can see the distribution again is quite different.
• 36:05And so if you integrate those into the percent of days with,
• 36:10one or more hazards during this time period,
• 36:13you see that there's an interesting distribution where,
• 36:17parts of the Connecticut river Valley
• 36:19and Southern Connecticut are particularly,
• 36:23high risk of being exposed to one or more hazards.
• 36:28Interestingly, if you connect this with the
• 36:31social vulnerability index,
• 36:33this is the CDC social vulnerability index
• 36:35that is also not homogeneously distributed.
• 36:38And interestingly, those high vulnerability locations,
• 36:47also tend to have a higher probability
• 36:51of having more than one hazard.
• 36:55This is primarily driven by the distribution of,
• 36:58the hazard of excess heat,
• 37:00and somewhat by the excess ozone.
• 37:02So really interesting to think about
• 37:06how the hazards overlap with each other
• 37:10and with social vulnerability
• 37:13and Keith created a climate risk index,
• 37:16based on this which looks different
• 37:19depending on the spatial scale that you look at.
• 37:22So again, if you combine the hazards
• 37:24and the social vulnerability, again,
• 37:25the Connecticut river Valley at Southern Connecticut,
• 37:28coastal Connecticut show up as places of particularly,
• 37:34potential pretty high impact.
• 37:36And if you were to look instead at the,
• 37:39Boston metropolitan area here,
• 37:41you can see that on a very fine spatial scale.
• 37:44There's tremendous heterogeneity as well in this.
• 37:49Okay, so to close.
• 37:51So in order to adapt to current and future climate hazards,
• 37:55local officials need to know what's the current health risk
• 37:57associated with a given hazard,
• 37:59what local actions can be taken
• 38:01to protect the public health.
• 38:03Do these actions actually reduce the risk of the hazard?
• 38:07How has the risk likely to change into the future?
• 38:10I didn't go into that today,
• 38:11but obviously we have very good projections of future,
• 38:16temperature changes under different concentration pathways,
• 38:21so we can predict into the future
• 38:25under different potential alternative realities.
• 38:27And we can do this in a repetitive way
• 38:29to continue to optimize.
• 38:32And so this just Zooming way out,
• 38:34highlights the needs and challenges
• 38:36of translating scientific research
• 38:38into public health benefits.
• 38:40So, this none of this would be possible
• 38:45without a fantastic team local team in my group,
• 38:51as well as, fantastic collaborators.
• 38:53Kate Weinberger was a former post-doctoral fellow
• 38:56that worked with me and is now
• 38:58at the university of British Columbia.
• 38:59We have a terrific team at Boston university and formerly,
• 39:06people were still connected with at Brown
• 39:08and then fantastic collaborators at Harvard,
• 39:10university of Michigan,
• 39:11university of Washington and Mount Sinai.
• 39:16And of course we all need funding,
• 39:17and I'm very grateful to the funding from NHS
• 39:20and Wellcome trust.
• 39:21So I will stop there and a welcome your questions.
• 39:29- Great, thanks, Greg, for the very, insightful presentation
• 39:33and also sharing with us your latest research.
• 39:38Before we go to the question from the attendees,
• 39:41we actually, have already pre collected questions
• 39:45from the our students who attend the
• 39:49Climate Change and Health seminar.
• 39:51I'm happy to see actually doing your presentation.
• 39:55A lot of questions has been answered.
• 39:57So just, pick some of the questions remaining.
• 40:01One the heat topic that the students are wondering is
• 40:04about the effectiveness of the heat index system.
• 40:08So they're wondering,
• 40:10like why there's no standard index
• 40:14in different places, and why there can be some, action of,
• 40:21why there can be some other matrix
• 40:23that can be considered like the wet bulb temperature,
• 40:29which may shows, more spatial rate disperse,
• 40:34varied effect rather than that or temperature.
• 40:39- Yeah, it's a great question.
• 40:41So the national weather service sets up, actually
• 40:45the national level of the national weather service
• 40:47makes recommendations of criteria that could be used,
• 40:52to issue heat alerts and then encourages regional offices
• 40:56and even local offices to come up
• 40:58with their own criteria that,
• 41:01are most appropriate for the populations that they serve.
• 41:05And so there isn't exact, it's not,
• 41:08a top-down sort of you must use this,
• 41:11here's a standardized threshold, which,
• 41:12some countries have taken that approach.
• 41:14This is a much more decentralized approach.
• 41:17So many, many, locations do use the heat index.
• 41:21And for approximately, Northern location
• 41:26sort of Northern half of the country
• 41:29uses a heat index of 105 as a threshold for
• 41:32issuing heat warnings and,
• 41:37a threshold of 100 degrees heat index
• 41:40for issuing, heat advisories,
• 41:42and then the Southern half of the country, approximately,
• 41:45each of those is five degrees set at five degrees higher,
• 41:49but there's a number of locations,
• 41:50they use their own system, including,
• 41:53Philadelphia is notable for using
• 41:59a predictive model of sort of
• 42:01how many people are at risk from this heat.
• 42:04New York city has done some terrific work on,
• 42:08changing the threshold.
• 42:10So there a number of examples around the country where,
• 42:14local health departments have worked with the community
• 42:17to identify what's the most appropriate metric
• 42:22and threshold for issuing heat alerts.
• 42:26But the challenge with that approach is that,
• 42:29it's not a systematic investigation
• 42:30of what would be work the best.
• 42:33So one of our goals is to think of,
• 42:37well, let's look everywhere in the country
• 42:39and see what either by region or by community
• 42:42or by climate zones,
• 42:44what would be the optimal metric for predicting,
• 42:48which are the most dangerous days of extreme heat,
• 42:52keeping in mind that it's in nobody's interest to issue,
• 42:58a very high number of heat alerts each year.
• 43:01So you really wanna focus each summer on like,
• 43:03what are going to be the worst days,
• 43:05how do we identify those
• 43:06and sort of using a health based perspective
• 43:09rather than a weather based perspective?
• 43:10So it's not necessarily the hottest days, but rather,
• 43:13we know from the work of others that, the,
• 43:18vulnerability to heat varies by location,
• 43:22by population and by time of year,
• 43:25as well as it's been shifting over the years.
• 43:27And so taking all that into consideration,
• 43:29can we sort of have a health based metric
• 43:31for issuing heat alerts heat warnings,
• 43:36and heat advisory's.
• 43:36Wet bulb globe temperature is a really interesting one.
• 43:40There's,
• 43:43I think that it's potentially very interesting,
• 43:46and I know that in some occupational settings,
• 43:49a wet bulb globe temperature is used as the guiding metric.
• 43:55It has not been to my knowledge been widely used,
• 43:58in sort of population level, heat warning work.
• 44:04But I think it'd be really interesting
• 44:05to look at that as well.
• 44:08- Great, thanks.
• 44:09Another kind of very detailed technical question
• 44:14is one students is wondering,
• 44:16the previous paper,
• 44:21where you choose the control days,
• 44:25because if you have a very higher threshold,
• 44:28then it's likely that you don't have enough control days.
• 44:34- That's a great question.
• 44:35So this refers I believe to Kate's study
• 44:39of looking at the effectiveness of heat warnings.
• 44:43And so what we did is we compare days,
• 44:46of the similar heat index
• 44:48and with or without a heat warning.
• 44:50And you're right, that for very hot days,
• 44:54like if a day is 110 degrees, heat index,
• 44:55that there's not going to be any days
• 44:58in that same location of 110 degrees,
• 45:00that didn't have a heat warning.
• 45:03So, by so we had to limit ourselves to those days in which,
• 45:09we sometimes saw a heat warning but not always.
• 45:13And if, a 90 degree day,
• 45:16nobody's issuing heat alerts and on 110 degree day,
• 45:19everybody's issuing heat alerts.
• 45:20And so we had to focus on the middle.
• 45:22So one of the limitations of this work is that
• 45:25it is there's no counterfactual,
• 45:28there's no information about the counterfactual of like,
• 45:31what would have happened had we not issued a heat alert
• 45:33on a very, very hot day?
• 45:35There's just, there's no data is conditional on location.
• 45:38So that is one of the challenges.
• 45:40So we should, our results are generalizable
• 45:42to those days on which you might,
• 45:45or sometimes issue heat alerts.
• 45:47And not outside of that relatively narrow band
• 45:51of temperatures.
• 45:54- Thanks.
• 45:55I think we do have a question from the audience,
• 46:00one of the first, so,
• 46:02the question from Stephan Lessen is asking
• 46:06about one third of the Medicaid population
• 46:10has no access to the internet.
• 46:12So how, the heat alerts commonly distributed within cities.
• 46:17- Yeah, that's a really great question.
• 46:19And again, it varies a little bit by location.
• 46:22The several or many of the national weather service,
• 46:26local offices are actually on social media now, and you,
• 46:29you could follow them on Twitter, there's, also,
• 46:34you can sign up for their email newsletters,
• 46:37that'll warn you of particular, threats,
• 46:41and you're right that those channels,
• 46:44while they might reach some segments of the population,
• 46:47they, probably are focused
• 46:50on those segments of the population
• 46:52that are particularly engaged
• 46:53and maybe not particularly at risk,
• 46:56for heat specifically.
• 46:58So, traditionally this was all through TV and radio,
• 47:03where you would say, national weather service has
• 47:06issued a heat alert for the next two days, or for,
• 47:09this region for tomorrow and advises you to,
• 47:13drink lots of water avoid exposing yourself to
• 47:16your kids to high heat, et cetera.
• 47:19So I think they use a combination of traditional
• 47:24and digital media, channels,
• 47:28but I think it raises a good question of,
• 47:31are we reaching the most vulnerable populations,
• 47:33with these alerts?
• 47:35And even if we inform people that there's a risk
• 47:38that doesn't necessarily mean that people are able,
• 47:41to protect themselves from that risk.
• 47:44So for instance
• 47:45when we think of the most vulnerable populations,
• 47:48you're amongst them sort of perhaps outdoor workers,
• 47:52so outdoor workers, there are guidelines,
• 47:56in temperatures above which outdoor workers shouldn't work,
• 48:00but your roofers and landscapers and construction workers,
• 48:03they're not getting paid if they're not doing the work.
• 48:05So sort of the opportunity for not just
• 48:10reaching and informing people,
• 48:12but actually giving them options
• 48:13of how to protect themselves,
• 48:15is I think a really hard challenge.
• 48:18You see this also with agricultural workers
• 48:20and other settings.
• 48:21So I think that there's we have to move from a model
• 48:25where we're just trying to reach people,
• 48:27to give them information to discovering, understanding,
• 48:32and addressing the hurdles
• 48:35to actually protecting themselves,
• 48:37or helping them protect themselves,
• 48:40rather than sort of just an information deficit model.
• 48:44- Yeah thanks.
• 48:45I think, kind of follow up on these detailed questions
• 48:50one of the students is asking like,
• 48:53behind this (indistinct) system exactly.
• 48:57Kind of mixture of all multiple different intervention
• 49:01matters such as you said, some including TV,
• 49:05some including other informing approaches.
• 49:09So, kind of further question is how to,
• 49:14evaluate the cost and effectiveness
• 49:17of different approaches when people, when
• 49:20the public health officials want to inform,
• 49:25want to intervene.
• 49:27- Yeah, I think it's a really interesting question.
• 49:29And so there's two questions.
• 49:30There is sort of what,
• 49:32how do you evaluate the effectiveness
• 49:34of these different channels?
• 49:37And I think the broader question is,
• 49:39can we move away from thinking that
• 49:43a channel of communication or a series
• 49:47works on the population as a whole?
• 49:48So, for example, if we,
• 49:50if you wanna try to reach and protect outdoor workers,
• 49:54there's probably channels of communication
• 49:56and engagement that are different
• 49:58than if you're concerned about seniors
• 50:01in institutional facilities,
• 50:03or if you're thinking about kids in school
• 50:05based environments or summer camp environments.
• 50:08So I think we probably in our communication strategies
• 50:11and engagement strategies need to move away
• 50:13from thinking that if only we use channel X,
• 50:17we'll reach more people,
• 50:18it's not about reaching more people,
• 50:20it's about reaching specific segments of the population
• 50:23that in specific ways that are amenable to their needs
• 50:29and the resources available to them.
• 50:32So I think working with school nurses is a great way
• 50:34to reach kids in school.
• 50:36I think working with organized kids activities
• 50:40is a great way to, reach again,
• 50:43vulnerable children and adolescents.
• 50:46But those strategies aren't gonna work in other settings.
• 50:49So I think it has to be much more targeted
• 50:51than we're doing now.
• 50:54- Thanks, yes, those words are insightful.
• 50:58I do have another question from the audience,
• 51:01from Alexi, is asking,
• 51:04is there evidence of political inference,
• 51:07determining the implementation of the warning system?
• 51:12- It's a great question.
• 51:13I actually don't know enough to,
• 51:16so I haven't seen political influence in that, but,
• 51:19I haven't worked with,
• 51:22too many national weather service offices directly.
• 51:27So I think there's probably others involved
• 51:32that can answer that more.
• 51:34One of the interesting linkages is that sort of the
• 51:38whether these heat alerts trigger local action
• 51:44varies across locations.
• 51:46So in New York city,
• 51:47I understand that every time
• 51:49the national weather service issues a heat warning,
• 51:52that triggers a certain number of activities.
• 51:54Like there's no intermediate decision,
• 51:56whereas in the city of Boston I understand that
• 51:59it's when the mayor declares a heat emergency,
• 52:02which is informed by the national weather service forecast
• 52:04and heat warnings,
• 52:05but it's not automatically triggered by.
• 52:07So I think there's some differences in,
• 52:10or quite a bit of differences actually around the country
• 52:13as to whether the national weather service heat alerts
• 52:17automatically trigger action,
• 52:19or are they informational,
• 52:21but the action is triggered by some other mechanism.
• 52:23And that's one of the things that we need
• 52:26to get a better handle on across the country is
• 52:28this the right trigger for local heat action plans to,
• 52:32and heat responds plans to be activated.
• 52:35And, I don't have a preconceived notion
• 52:38as to what the right answer there is.
• 52:40Maybe this is the optimal trigger
• 52:42or maybe something that it's appropriate
• 52:44to have an intermediate step of somebody else sort
• 52:46of making a judgment call for that local population.
• 52:50So I think that's an exciting area of research.
• 52:53- Thanks.
• 52:54We do have another question from, Rob Tuber.
• 52:57He's asking,
• 52:58have you ever looked into the effectiveness
• 53:00of cooling centers?
• 53:03- I love cooling centers
• 53:04because they seem like such a great idea.
• 53:06Oh, people are know dying or or being hurt by heat
• 53:11let's provide them a cool place to go.
• 53:13And the anecdotal evidence is that,
• 53:17you open cooling centers and very few people go.
• 53:19And so again, understanding the hurdles of that.
• 53:22And I think, again,
• 53:25I've worked somewhat with people in New York city
• 53:28and I understand that they provide
• 53:30transportation assistance for vulnerable populations,
• 53:35because I think one of the hurdles they found was that,
• 53:40not everybody can get themselves to a cooling center,
• 53:43so you opened a cooling center and that assumes that
• 53:44somebody can go.
• 53:46Okay, so there's cultural barriers to or
• 53:53barriers in terms of like, well,
• 53:55what am I going to do there?
• 53:57Is this a place where I'm actually welcome?
• 53:58How do I get there?
• 53:59Can I actually afford, like,
• 54:01if I work, again,
• 54:03can I take the time to go do that?
• 54:05Or if I have, medication needs will I be able to,
• 54:11treat my medical condition while I'm there?
• 54:13So I think that cooling centers are really
• 54:15intuitively attractive option.
• 54:17And I think with so much of what we do in response to heat,
• 54:21there is not a body of evidence as to what works.
• 54:23And I think that's really where we need to
• 54:26sort of move the field is starting to think
• 54:28about what works in what settings and for whom,
• 54:30so that we can really provide evidence-based guidance
• 54:33for developing solutions.
• 54:37- Thanks very well said.
• 54:39We do need a lot of these evidence-based research
• 54:41on these policy actions.
• 54:43I do have another follow-up question from the students,
• 54:48is that actually within your next steps?
• 54:50So the students is kind of wondering
• 54:54how do you actually verify the causal assumption
• 54:59in evaluating the heater systems?
• 55:03- Yeah, that's great.
• 55:04So, the best we can do is use the data,
• 55:12this isn't a randomized, these aren't randomized studies.
• 55:14So the best we can do is,
• 55:17use observational data to the best of our ability.
• 55:20So, can we ever prove that we understand
• 55:23the causal effect of heat alerts?
• 55:24No, but I think we can do,
• 55:27more detailed, more insightful analysis
• 55:32of the existing observational data.
• 55:34And I think this idea of there are a range of days.
• 55:39So going back to the heat warnings,
• 55:41there's these days where we say,
• 55:42we're always going to issue a heat warning,
• 55:44'cause it's just so hot that we just take it for granted
• 55:46that it's dangerous and we need to do something,
• 55:49so we're going to do it.
• 55:50And then there's this other bucket,
• 55:52a days on the other end where like, it's just,
• 55:55issuing key warnings is just not likely to be effective,
• 55:57but there's this middle range where you're like,
• 55:59should I issue a heat warning?
• 56:01Yes or no.
• 56:02And so what we're doing is providing information
• 56:05on that part, the spectrum, and where we say,
• 56:09should we issue somewhat more heat alerts
• 56:11because we can do it right around this threshold,
• 56:14would that save lives?
• 56:16And, that's it's not the entire picture.
• 56:20It would be so interesting to know
• 56:22on these very hot days when we issue heat warnings,
• 56:25do they actually prevent deaths?
• 56:29And the problem is as we said before,
• 56:31that there's no data on the counterfactual,
• 56:33like what would have happened
• 56:35had you not issued a heat alert?
• 56:37So, there's probably other creative ways to do it,
• 56:40but we haven't figured that out yet.
• 56:41So this is really about at the margin,
• 56:44would you do better issuing say 10%
• 56:46more heat alerts each year,
• 56:49or 15% more heat alerts each year?
• 56:50'Cause you don't wanna issue them if they're not,
• 56:54there's risks of warning,
• 56:56fatigue of people not taking it seriously.
• 56:58Because there are too often and there's some costs
• 57:01associated with each time you issue it,
• 57:03if it triggers actions.
• 57:05So it's again, it's like, no, should we issue a few more?
• 57:08And in that question, we,
• 57:10so far our evidence suggests
• 57:12that there's not widespread benefit of them, but,
• 57:17sort of with the asterisk that more work is needed on that.
• 57:22- Okay, thanks, yeah.
• 57:24I think we have the final comment or question
• 57:28from Donna Spellman.
• 57:31I've been struggling to see how implementation science
• 57:34might promote environmental health.
• 57:38This project is a perfect example of the connection.
• 57:40Thanks.
• 57:41- Thanks Donna.
• 57:43I think that's a great point.
• 57:44And I think that there I have not seen a large amount
• 57:47on implementation science,
• 57:48specifically oriented towards solutions
• 57:53in environmental health.
• 57:56We're really great at describing problems
• 57:58and less good at figuring out and implementing solutions
• 58:03and then evaluating their effectiveness.
• 58:05So I think that this is right for that
• 58:07because we know there's a risk there.
• 58:08We just don't actually know exactly what to do about it.
• 58:11And there are lots of good ideas,
• 58:12but we need to move from good ideas to,
• 58:15good evidence supporting specific ideas.
• 58:20- Great.
• 58:21I think with that we will conclude, this seminar
• 58:25and thank you Greg, for this wonderful presentation
• 58:27on the science-based actions.
• 58:30And, this seminar will be recorded
• 58:34and will be posted later.
• 58:37So thank you all for coming and thanks again Greg.
• 58:40- Wonderful thanks for the opportunity, bye bye.
• 58:42- Bye.