Climate Change and Health Seminar: "Climate, Energy, & Inequity: From Exposures to Epidemiology"

Transcript

• 00:07<v ->Welcome to this special seminar</v>
• 00:11being sponsored by the Yale Center
• 00:13on Climate Change in Health.
• 00:15And it's a pleasure to welcome Daniel Carrión
• 00:23who is currently a postdoctoral Fellow
• 00:26in Environmental Medicine and Public Health
• 00:28at the Icahn School of Medicine at Mount Sinai.
• 00:33He received his PhD from
• 00:35Columbia Mailman School of Public Health
• 00:39from the Department of Environmental Health Sciences
• 00:43and it was in their Climate and Health Program,
• 00:45which is a really great program that our own Chi Chen
• 00:52has been closely associated with in the past.
• 00:56And so we're really looking forward to Daniel's presentation
• 01:00on Climate, Energy and Inequity:
• 01:03from Exposures to Epidemiology.
• 01:05So, Daniel, welcome.
• 01:08<v ->Thank you so much.</v>
• 01:10So I'm excited to speak to you all today
• 01:13and just by way of a little bit more introduction,
• 01:17I completed my BA at Ithaca College in 2008,
• 01:22and if you remember 2008,
• 01:25that was right when the global recession happened,
• 01:28so a great time to graduate from college.
• 01:31So I had two part-time jobs, one where I was working
• 01:34actually for the Health Department in Tompkins County
• 01:36in New York state, and the other one where I was working
• 01:40for the Solid Waste Division where I was doing composting
• 01:42and recycling education and outreach.
• 01:45I then ended up leaving
• 01:47and going to Hudson River Healthcare,
• 01:48which is a network of federally qualified health centers
• 01:53across New York state, about 25 at the time,
• 01:57where I was helping manage outreach
• 02:02and programming for folks with HIV, folks who were homeless,
• 02:06folks in public housing, and migrant farm workers.
• 02:09I was concurrently doing my masters in public health
• 02:12and environmental health sciences
• 02:13at New York Medical College.
• 02:15And then after completing my MPH
• 02:18ended up leaving to go to Columbia university
• 02:21where I started a pipeline program called
• 02:24the Summer Public Health Scholars program,
• 02:26a CDC funded program to increase the diversity
• 02:30of the public health workforce,
• 02:32specifically around health equity.
• 02:35I then started my PhD as Rob mentioned
• 02:38in the department of environmental health sciences
• 02:42and the climate and health program
• 02:44and completed that in 2019.
• 02:47And then now at the Icahn School of Medicine as a post-doc.
• 02:51And so I'm excited to tell you about the work that I've done
• 02:56in the most recent part of this journey,
• 02:59which I characterize being at this nexus of climate energy
• 03:03and health inequity.
• 03:05So we all know that energy lies at the source of our climate
• 03:09crisis, societal decisions on where we derive energy,
• 03:13how much we need and what we use it for
• 03:19are all leading to increasing global temperatures
• 03:23that we have been observing and we'll continue to see.
• 03:28But we've run a dynamic tension here, right?
• 03:31Because energy is fundamental to public health.
• 03:35It's fundamental for folks to stay healthy,
• 03:38from the energy that we use to cook with,
• 03:41to the energy that we use in the winter to stay warm,
• 03:44to the energy that we use in the summer to stay cool,
• 03:49we need energy.
• 03:51And so I've been fortunate to work in all three
• 03:53of these spaces, thinking about this,
• 03:56these energy tensions in public health,
• 03:59but for the scope of this talk,
• 04:00I'm going to only tell you about two of them,
• 04:03which is about my work in household energy and air
• 04:05pollution related to cooking,
• 04:07and then more recently temperature epidemiology
• 04:10from summertime temperatures.
• 04:14So quickly about my dissertation work and household energy
• 04:18and air pollution in low and middle income countries.
• 04:22As background, 3 billion people around the world
• 04:28experience energy poverty,
• 04:30which is characterized by cooking and or heating with wood,
• 04:34dung, charcoal, or other biomass fuels.
• 04:40And although the proportion is decreasing overall
• 04:45of the population that relies on these fuels
• 04:49because of population growth,
• 04:51the absolute counts are actually increasing and the highest
• 04:55increases are actually in Sub-Saharan Africa.
• 04:59And so this is the stove that you would see in many parts
• 05:03of Sub-Saharan Africa, it's called the three stone fire,
• 05:08which you might guess because there are three stones
• 05:11that prop up a pot and underneath biomass is combusted.
• 05:18We're concerned about this because the combustion of that
• 05:21biomass leads to a mixture of compounds collectively
• 05:26referred to as household air pollution.
• 05:29And so that comprises CO2 particulate matter,
• 05:34carbon monoxide,
• 05:35polycyclic aromatic hydrocarbons amongst others,
• 05:40and both the deforestation associated with biomass
• 05:43harvesting depending on country and the combustion
• 05:47are projected to actually contribute to climate change.
• 05:52And we also know that exposure to household air pollution
• 05:56is associated premature deaths each year,
• 06:00millions of premature deaths each year,
• 06:03the largest proportion from lower respiratory infections.
• 06:07And you might know that lower respiratory infections
• 06:10are actually the leading killer of children
• 06:13under five in lower and middle income countries.
• 06:16And so it's widely agreed that the solution here
• 06:19is to scale up cleaner cooking alternatives
• 06:23like liquified petroleum, gas, electric, and induction.
• 06:30And in Ghana, as in many other countries,
• 06:33LPG represents the cheapest and most accessible options
• 06:37of the three that I just mentioned because the other two
• 06:42electric and induction requires stable and extensive
• 06:46electricity grids that don't exist
• 06:48in many parts of the world.
• 06:50But if you're unfamiliar with this literature,
• 06:52I would understand if some folks in the audience
• 06:55are confused at how using a fossil fuel can actually help us
• 06:59fight climate change.
• 07:04The atmospheric science behind this is complicated
• 07:06and outside the scope of my talk today,
• 07:09but rest assured that the international panel on climate
• 07:12change indicates that activities consistent
• 07:16with the greenhouse gas emission reductions needed
• 07:19for a warming of 1.5 degrees Celsius
• 07:24world includes transitions to clean cookstoves
• 07:29that are gas based or electric based.
• 07:33And unfortunately, atmosphere projections
• 07:37that are Ghana-specific are actually unavailable
• 07:40at the moment, but one done in Cameroon
• 07:45undergoing a similar LPG transition
• 07:49shows that there are projected net cooling benefits
• 07:54of switching to LPG rather than continued use
• 07:58of biomass fuels.
• 08:00And so this then represents in many parts of the world
• 08:04climate mitigation opportunity
• 08:07with potential health co-benefits.
• 08:12And so my thesis works set out to try and provide evidence
• 08:15to support clean cooking efforts.
• 08:18The relationship between energy,
• 08:20poverty and disease can be described as a pathway
• 08:23from poverty to energy poverty,
• 08:27which then causes household air pollution,
• 08:30and then the exposure to that household air pollution
• 08:33leads to a whole host of diseases.
• 08:36And there are particularly three parts of this pathway
• 08:39that we can try to interrupt in this relationship
• 08:44between poverty and disease in this context.
• 08:47So we can focus on making the clean available,
• 08:51which is a moniker from the late Kirk Smith,
• 08:55essentially saying identifying interventions
• 08:58to increase the uptake of clean cookstoves
• 09:00like induction or LPG.
• 09:04We could interrupt this part of the pathway,
• 09:06which is to make the available clean by identifying ways
• 09:11to reduce exposures from biomass combustion,
• 09:15such as improved cookstoves that have interventions
• 09:19like increasing ventilation,
• 09:21thereby increasing the efficiency of combustion.
• 09:26And then finally, we can do health research
• 09:30to understand biological pathways
• 09:32for improved treatments or interventions.
• 09:38My work was particularly focused on these two parts
• 09:41of the pathway, and I'll quickly sum up my dissertation
• 09:46in one slide, which is the first paper
• 09:51in my dissertation was where I
• 09:54created a new framework to try and understand why recipients
• 09:58of new cookstoves often end up
• 10:01stopping using those cookstoves
• 10:04and we refer to this as stove use discontinuance.
• 10:09Acknowledging that a lot of people who receive
• 10:11new cookstoves end up stopping their use in the longer term,
• 10:17we ended up then trying to design an intervention
• 10:21to support a government effort.
• 10:24So the government actually freely distributes LPG stoves
• 10:29in rural areas in Ghana.
• 10:31And so we designed and implemented an intervention
• 10:36to try and increase the long-term use of those stoves.
• 10:41The findings suggest that more fundamental policy changes
• 10:44are actually needed just rather than a simple intervention.
• 10:49And finally understanding biological pathways
• 10:54from data from a cohort study,
• 10:56we used banked nasal swabs from infants
• 11:00of the age of one or less
• 11:03and found that household air pollution is associated
• 11:06with increased presence of bacterial and not viral microbes.
• 11:11And this is important because there's other literature
• 11:14that otherwise indicates that household air pollution may be
• 11:17contributing to bacterial forms of pneumonia
• 11:21and not viral forms of pneumonia
• 11:23and so this is trying to understand that ideological pathway
• 11:27a little bit better.
• 11:31So with that very brief overview of my thesis work,
• 11:35I wanted to spend more time on my current portfolio,
• 11:39which is focused on ambient temperature,
• 11:41temperature epidemiology, and energy insecurity.
• 11:47And the motivation here is simple.
• 11:51We're living it right now.
• 11:52Climate change means that there's an increased frequency
• 11:56and intensity of extreme heat events
• 11:59and hotter average summers.
• 12:01And we know that those higher temperatures are associated
• 12:05with a whole host of health outcomes from cardiovascular
• 12:08to respiratory, to renal,
• 12:11to even violence and other non-health outcomes,
• 12:15but still very health relevant like educational performance.
• 12:20And there's also work that shows that increased ambient
• 12:24temperatures are associated with perinatal outcomes
• 12:28like pre-term birth.
• 12:30And there's an important opportunity here
• 12:32because temperature epi has been largely focused
• 12:35on older adult populations
• 12:37and so there's an opportunity to grow the literature
• 12:41thinking about pediatric populations.
• 12:47So I first want to tell you about a study
• 12:49that we're wrapping up right now,
• 12:51thinking about the case process over design as a way
• 12:55of studying the relationship between ambient temperature
• 12:58and preterm birth.
• 13:04And the motivation here
• 13:06I think is also simple for a public health crowd.
• 13:10That preterm birth is a major health outcome
• 13:16that's associated with high infant mortality.
• 13:19It's also one of the most pronounced and persistent
• 13:23racial disparities that we know of,
• 13:26and it not only represents poor health
• 13:29potentially in the immediacy of birth,
• 13:33but also potentially a trajectory of poor health
• 13:37in the longterm.
• 13:39Many of the health outcomes are also health disparities
• 13:43for communities of color.
• 13:45And there's a growing literature on the relationship
• 13:48between ambient temperature and preterm birth.
• 13:51One of the initial studies identifying this association
• 13:54was actually from Bosu at all in 2010,
• 13:58a study based in California using
• 14:00the case crossover study design.
• 14:04So if you're unfamiliar with the case crossover study
• 14:07design, a quick introduction.
• 14:09It's a case-only study design that compares the case time
• 14:15to control times when the event did not happen.
• 14:20And it's been widely used in air pollution epidemiology
• 14:23and is increasingly used in temperature epidemiology.
• 14:28It's a temporal comparison,
• 14:32meaning that it's comparing the same person to themselves
• 14:35at different time points.
• 14:37And so a real perk there is that then it's not vulnerable
• 14:41to person level forms of confounding.
• 14:47However, proper control selection is then pivotal for proper
• 14:53inference because you want to make sure that you
• 14:55are controlling for potential temporal confounders
• 14:59and other temporal forms of bias.
• 15:02And a key assumption of this design is that there are no
• 15:07trends in the risk of the outcome over time.
• 15:11And it was actually pointed out in a commentary
• 15:14from that original Bosu paper that I mentioned
• 15:18that preterm birth actually violates this assumption.
• 15:22And this should be pretty intuitive to folks in the audience
• 15:25because the risk of birth changes
• 15:30pretty secularly over gestation.
• 15:33And so this is something that we need to think about
• 15:35if we're using this study design
• 15:38for ambient environmental exposures.
• 15:45However six other studies have employed this study design
• 15:49for preterm birth since 2010,
• 15:52specifically for ambient temperature that we're aware of.
• 15:56And I'm sure that number is much higher
• 15:58if we also consider air pollution.
• 16:06So that this was a great opportunity for a simulation study.
• 16:10So for those who are unfamiliar,
• 16:12a simulation study are essentially computational experiments
• 16:15where we can test the behavior of our epidemiological
• 16:19studies under controlled circumstances.
• 16:23So first what we do is we create a dataset and then we embed
• 16:28a known association in that dataset.
• 16:32We then test our epidemiological analysis'
• 16:35ability to recover that association.
• 16:38Then we try to repeat,
• 16:40or we repeat this a thousand times to represent
• 16:43some of the stochasticity of the underlying distribution.
• 16:47And then we could see if different strategies
• 16:49or specifications of models
• 16:51can actually improve our inference.
• 16:56More specific, what data did I use to do this?
• 16:59Well, LaGuardia Airport has temperature data
• 17:03readily available for download online.
• 17:06So we downloaded LaGuardia temperature data
• 17:09as our exposure data.
• 17:12And then for our health data, we actually downloaded CDC
• 17:16wonder data to create estimates of daily preterm births
• 17:22by gestational age from 20 to 36 weeks.
• 17:28And just as a quick definitional thing,
• 17:30preterm birth is generally a birth that take place
• 17:34before 37 weeks.
• 17:37We got these data for 2007 and 2018 from,
• 17:42and then we created data sets with a range
• 17:46of simulated effects ranging from 0.9 to 1.25.
• 17:51I don't think anyone thinks that temperature
• 17:53is protective of preterm birth,
• 17:56but we wanted to see how malleable these models
• 18:01were to different underlying assumptions.
• 18:06And then we do these case crossovers to see how our model
• 18:10does at recovering the simulated effects.
• 18:14We ended up doing this using a time stratified control
• 18:18selection for three different time periods.
• 18:21So we did it for a two week time stratified,
• 18:24a 28 day time stratified, and a month time stratified.
• 18:29And we limit our case crossover to warm month analyses,
• 18:33which is consistent with other studies in this literature.
• 18:38And again, we do this a thousand times to kind of represent
• 18:41some of that stochasticity of the underlying distrobution.
• 18:46So these are the input data that we use.
• 18:50So up here are, is the temperature data
• 18:55from LaGuardia Airport
• 18:58and down here are the estimated number of births
• 19:01on a given day that we used from the CDC wonder database.
• 19:06And then this orange region
• 19:08is the warm month time period that we used.
• 19:15So the main result that I'm showing you here
• 19:17is for absolute bias.
• 19:20And so absolute bias is simply the difference between
• 19:23the simulated relative risk with the coefficient that we get
• 19:27from the case crossover in the log scale.
• 19:31And I'm showing you first a relative risk of one,
• 19:34meaning that there's no association between temperature
• 19:38and preterm birth.
• 19:40And you could see that using all three of these study
• 19:43designs, we actually get relatively unbiased results
• 19:48with the medians hovering around zero.
• 19:54If we look across the entire range of our embedded effects,
• 19:58we see relatively consistent results where all three
• 20:02of these case control selection designs actually yield
• 20:07relatively unbiased results, with our two-week stratified,
• 20:12yielding the noisiest results characterized here
• 20:16by a wider intercore tile range.
• 20:21And then when we looked at coverage,
• 20:23so coverage would be the coverage
• 20:26of the 95% confidence intervals.
• 20:29What percentage of the time does the confidence interval
• 20:33actually include the true embedded effect?
• 20:36And you would hope for a model that that would be
• 20:40consistently 95% of the time.
• 20:41And indeed we see that these models are relatively stable
• 20:45with approximately 95% at all of these risks embedded.
• 20:55So this is really important work because this shores up
• 21:00the evidence that we have
• 21:02for the case crossover study design
• 21:05and ambient exposures and preterm birth,
• 21:10which I think is really important.
• 21:12We ended up doing 24,000 simulations and corresponding
• 21:15case crossovers, finding that the models
• 21:18are relatively unbiased.
• 21:20And we're excited about wrapping up this project
• 21:24because we've tried to enhance reproducibility
• 21:27of our findings and results by using the targets package
• 21:31in R, which then means that other folks
• 21:37can go and rerun these analyses and can actually swap out
• 21:42different years or regions and their analysis,
• 21:45which aids an extensibility of this analysis.
• 21:50And now we're actually using the case crossover analysis
• 21:56to think about a national level analysis
• 21:59that we're doing actually in Mexico
• 22:02and hopefully future studies in the U.S. as well.
• 22:08But much the same way that we're thinking about
• 22:11epidemiological methods, we're also thinking about improving
• 22:15our exposure methods.
• 22:17And so here, I want to tell you about a project
• 22:19that we just published on,
• 22:21thinking about a one kilometer hourly air temperature model
• 22:24across the Northeastern United States
• 22:27from Maine to Virginia
• 22:29and this is fusing ground data
• 22:33with satellite remote sensing data.
• 22:37And the inspiration for me here is that there is a small,
• 22:43but growing literature on temperature disparities,
• 22:47that temperature is perhaps unevenly experienced
• 22:52based on race, ethnicity, income,
• 22:56and other forms of potential vulnerability.
• 23:00And so one limitation, however,
• 23:04with some of these past studies is that they either use land
• 23:08surface temperature, which is remotely sensed
• 23:10with satellites and related to air temperature,
• 23:14but not exactly air temperature,
• 23:16or they use forms of land cover,
• 23:21and land use that are associated with temperature,
• 23:24but again, not empirical measures of temperature
• 23:28and so an opportunity then to try and grow this literature,
• 23:32thinking about these potential temperature disparities.
• 23:39So the goal here is to create this one kilometer
• 23:42hourly air temperature model
• 23:44to be able to produce predictions
• 23:46between the time period of 2003 to 2019.
• 23:51So we ended up using national oceanic,
• 23:55atmospheric and atmospheric administration data
• 24:00for ground stations throughout this region
• 24:03as our ground truths for air temperature.
• 24:05And so that's what's depicted in red
• 24:08across our study region.
• 24:10These are the locations of all of the ground sensors
• 24:13that we used in our model.
• 24:16We then collected 34 predictors that we thought
• 24:21would help us characterize the spatial and temporal patterns
• 24:24of cooling and heating throughout the day.
• 24:29And the goal here is to be able to create consistent
• 24:32and reliable predictions of air temperature across
• 24:36this region, even in places
• 24:38that we don't have ground observations.
• 24:46So we tested five different statistical approaches
• 24:50to actually create these predictions
• 24:55and show their differences in performance in our paper.
• 24:59For the sake of time,
• 25:01I'm just going to tell you the punchline,
• 25:03which is that we ended up using the XG boost model
• 25:07for our final predictions.
• 25:09So the XG boost model is a powerful machine learning model
• 25:14that we used and had to adapt to create
• 25:19a spatial temporal predictions.
• 25:23And what we ended up doing was actually comparing
• 25:27our XG boost model to the NLDAS-2 model.
• 25:32So NLDAS-2, if you're unfamiliar is a NASA product
• 25:37that also gives hourly predictions
• 25:39and it's what the CDC uses for their heat and health
• 25:43tracking system, as well as some of their research.
• 25:48And so we thought that this was an important model
• 25:51to benchmark again.
• 25:56So these are the predictions from our XG boost model,
• 25:59from the hottest midnight of our data set, July 22nd, 2011.
• 26:05And so you can see across this Northeast region
• 26:09from Virginia to Maine,
• 26:11that we reconstruct a great deal of spatial heterogeneity.
• 26:17Again, this is for one hour,
• 26:19the highest midnight of our time period.
• 26:23And when we zoom in to a sub region,
• 26:29this, in this case being New York City,
• 26:34we see that we reconstruct a great deal of spatial
• 26:37heterogeneity from the urban heat island effect.
• 26:42And I should have mentioned earlier,
• 26:45I mentioned that NLDAS-2 is hourly,
• 26:49but it's actually at a much coarser spatial resolution.
• 26:53So these larger grid cells overlaid our predictions
• 26:58are actually the NLDAS-2 grid cells.
• 27:02And it's important to note here that in this one,
• 27:06NLDAS-2 grid cell, you have most of Manhattan,
• 27:10a big chunk of the Bronx and a little bit of Queens
• 27:14that would get one prediction for all of that region,
• 27:18with the NLDAS-2 predictions,
• 27:21but we can reconstruct a great deal of heterogeneity
• 27:25within that region.
• 27:29And we think that that then is related
• 27:32to the performance of these models.
• 27:35So these are the root mean squared errors from just 2019
• 27:40from our XG boost model versus the NLDAS-2 model.
• 27:46So RMSE is a measure of predictive accuracy and the goal
• 27:51is to have lower RMSEs.
• 27:54And so we show that our model
• 27:56has a low RMSE of 1.4 Celsius,
• 28:01whereas the NLDAS-2 model has a RMSE of 2.4 Celsius.
• 28:09When we look across the entire region across all years,
• 28:14we see that the XG boost predictions have one third
• 28:17of the mean squared error of the NLDAS-2 predictions.
• 28:26But given the small literature on temperature disparities,
• 28:29we were curious to see if our model was also associated
• 28:33with a measure of social vulnerability.
• 28:37And so what we decided to do was actually conduct a limited
• 28:42application to look at the relationship between our model
• 28:46and the NLDAS-2 model with social vulnerability.
• 28:52So what we did was we used the CDCs social vulnerability
• 28:56index, which are a composite of 15 census variables,
• 29:02including socioeconomic status, housing, transportation,
• 29:07language isolation, amongst other characteristics.
• 29:12And these are variables that the CDC uses to identify
• 29:16communities that may need support before,
• 29:19during or after a disaster.
• 29:23The results from the social vulnerability index
• 29:26are proportional.
• 29:28It produces measures from zero to one.
• 29:31And so we decided to use mixed models
• 29:37to associate our XG boost model and the NLDAS model
• 29:43with social vulnerability to see how they were associated
• 29:47with social vulnerability at the census tract level.
• 29:51We wanted this to be a limited application
• 29:54so we only did it for one hour of one day from that hottest
• 29:58midnight that I showed you earlier.
• 30:04And here are the results.
• 30:07So, as I mentioned earlier,
• 30:09the CDC social vulnerability index is a proportional measure
• 30:12from zero to one.
• 30:14And so for a unit increase of the CDC SVI,
• 30:20we see that the NLDAS-2 model shows an increase
• 30:24of temperature of 0.18 Celsius.
• 30:28However, when we look at the XG boost model,
• 30:30we see that our model has a stronger relationship
• 30:35with an increase in temperature,
• 30:37average temperature of 0.71 Celsius.
• 30:44And just to ground that in some places that you might know,
• 30:49so if we look at New York City,
• 30:52two boroughs of New York City, Manhattan and the Bronx,
• 30:57and then we look at two counties in upstate New York,
• 31:02you would see that the NLDAS-2 model has a very,
• 31:07very shallow gradient of temperature and social
• 31:11vulnerability across these temperature predictions.
• 31:16However, with our XG boost model,
• 31:18because we reconstruct much more spatial heterogeneity,
• 31:23we see much more of a strong relationship
• 31:28with the social vulnerability index.
• 31:32So with the caveat that this is one hour of one day,
• 31:36what this implies to us is that there's potentially exposure
• 31:40misclassification in coarser models.
• 31:44And that that exposure misclassification may be differential
• 31:48by neighborhood vulnerability.
• 31:53So as a takeaway here,
• 31:56we've created highly accurate air temperature predictions
• 32:00that we think are right for application
• 32:03to social science, exposure science,
• 32:07and epidemiological studies.
• 32:11But wait, there's more,
• 32:13I think that this is a great segue
• 32:16because I'm currently expanding on these questions
• 32:19with work that I'm doing at the moment.
• 32:23And so right now, I want to quickly tell you about work
• 32:27that I have underway to try and explore these exposure
• 32:31disparities further and point to its potential importance
• 32:36for epidemiological methods.
• 32:39And so this is about thinking about residential segregation,
• 32:43air temperature, and circulatory mortality.
• 32:49So for the first part of the analysis,
• 32:51I'll be looking at exposure disparities,
• 32:54similar to the methods that I just showed you,
• 32:57but with some key differences.
• 33:00So unlike the last analysis,
• 33:03this time I actually want to look at the differences
• 33:07and the predictions by race.
• 33:09We know that we have suggestions from past literature
• 33:13that there are differences in exposure by race
• 33:16and ethnicity and so we want to look at this
• 33:20by race and ethnicity as well
• 33:25now that we have air temperature predictions.
• 33:28And so what we decided it had to do was we decided to
• 33:32aggregate our models to the census tract level
• 33:35like we did before and then we wanted to see what
• 33:40the differences were potentially in an experienced summer.
• 33:46And so what I did was I wanted to compare are the summertime
• 33:50aggregates so I borrowed from the energy literature
• 33:54and computed cooling degree days.
• 33:58So if you're unfamiliar with cooling degree days,
• 34:01generally speaking, what it is is measures
• 34:04of how much hotter a day is than a threshold value.
• 34:08Generally in the U.S., the threshold value that's used
• 34:12is 65 degrees Fahrenheit, or 18.3 degrees Celsius.
• 34:18So, as an example, if today is 67,
• 34:22which I wish that it were, but if it were 67 outside today,
• 34:26that would give us two cooling degree days.
• 34:29And then you repeat that for every other day,
• 34:31and then add up all of those cooling degree days
• 34:35for the summertime values.
• 34:39For now I'm only conducting a comparison
• 34:42of exposure experiences by black and white people,
• 34:46but in the future, I want to consider more racial groups
• 34:51to try and characterize these exposure disparities better.
• 34:57And you can imagine that if we see differences by race,
• 35:02someone could make an argument that it might be
• 35:06because different people live
• 35:09in different parts of the region.
• 35:11So for example, saying that more white folks live
• 35:15in the Northern most parts of the region like Maine
• 35:20and more black folks live in the Southern most part
• 35:22of the region like Virginia.
• 35:25And so we wanted to then make this within county comparison
• 35:31within geographic compact geographies,
• 35:35to look at exposure disparities within these
• 35:40more relevant administrative units.
• 35:43And so to address that,
• 35:45we then took a similar approach of comparing tracks
• 35:49within counties with our predictor variable,
• 35:53being the proportion of the census tract
• 35:58that was comprised of black folks,
• 36:00and then using random intercepts and slopes by county
• 36:05to then get county level comparisons.
• 36:11On the epidemiological side of things,
• 36:13you can imagine that getting health data that covers
• 36:16the entirety of this region is pretty difficult
• 36:20so we use it as an opportunity to get creative.
• 36:23We, again, access to CDC wonder data
• 36:27and although I'm interested in child health,
• 36:30CDC wonder data has some major limitations
• 36:34if we're thinking about a rarer health outcome
• 36:37like preterm birth.
• 36:40Data are provided are at very coarse geographies.
• 36:44In this case, data are only provided at the county level,
• 36:48and they're also only provided for course time spans.
• 36:52And then data that are counts that are below 10
• 36:58are suppressed for privacy concerns.
• 37:02So, because CVD mortality is a much more common event,
• 37:07we decided to conduct this analysis with CVD mortality.
• 37:12There are still however,
• 37:14a fair amount of suppressions of data
• 37:18and so to deal with that,
• 37:19we ended up using a left censored Poisson regression
• 37:24since there would be left censoring for lower counts.
• 37:29And really one of the things that I'm getting at here is
• 37:32around this question of exposure misclassification.
• 37:37So for example, in many environmental epidemiology studies,
• 37:40there's oftentimes an analysis that looks at effect
• 37:43modification by race, often finding higher effect estimates
• 37:47based on race and ethnicity.
• 37:49And while there are sometimes reasons to think that this
• 37:52might be the case, depending on exposure and context,
• 37:56I am often left wondering if it's potentially a consequence
• 38:01of underlying exposure disparities that our exposure models
• 38:05are not picking up.
• 38:08And so with that inspiration,
• 38:10I ended up doing four different regressions,
• 38:15two regressions for white folks using both exposure models
• 38:20and two regressions for black folks using both regression
• 38:23models or prediction models, I should say.
• 38:27And since this ended up being at the county level,
• 38:29I tried to preserve some of the exposure differences
• 38:33by computing weighted by track level racial composition,
• 38:39aggregated up to the county level.
• 38:47So these are preliminary results just for the year 2019.
• 38:53So this plot is simply looking at the distributions by race
• 38:57across the 13 states including DC.
• 39:02And what we see here is that actually both models
• 39:05appear to reconstruct a temperature disparity
• 39:09between whites and blacks.
• 39:12However, our XG boost model has a much more smoothed out
• 39:18distribution for black folks.
• 39:24And when we actually look at the median values experienced,
• 39:28we see that they're about the same for white folks,
• 39:32but between these two prediction models.
• 39:34But in fact, we have higher exposures for black folks
• 39:38with our XG boost model.
• 39:41But this is just looking across the entire region,
• 39:44this isn't actually of the results from our analysis
• 39:47and so from that linear mixed effect model
• 39:50that I mentioned earlier,
• 39:52we look to see at how these were related to the proportion
• 39:56of black people living inside of a census tract
• 40:00and we found that a zero to one increase for the proportion
• 40:06of folks was associated with 25 higher cooling degree days
• 40:11for the NLDS to model.
• 40:14But for the XG boost model,
• 40:16we reconstruct approximately 68 cooling degree days.
• 40:23And so we think that this is potentially important
• 40:27for reconstructing some of these potential
• 40:30exposure disparities and on the epidemiological
• 40:34side of things, when we do a stratified model
• 40:38for white folks, we see a modest but significant effect
• 40:42of approximately 1.04.
• 40:45But when we look at those as effect estimates
• 40:48for black folks, we see much higher effect estimates
• 40:53for both models.
• 40:54However, this is for the NLDAS-2 model with about 1.24
• 41:00as the effect estimate.
• 41:04It was mentioned in the slide
• 41:05but I should've said it before,
• 41:07these are scaled per 92 cooling degree days
• 41:11or one cooling degree day average increase
• 41:15across our time span.
• 41:18And so for the XG boost model,
• 41:21we see that we get a much lower,
• 41:25but still higher than for whites effect estimate of 1.14.
• 41:32So what this means to me,
• 41:34or implies to me that there is potentially exposure
• 41:39misclassification that can appear
• 41:44in epi models as greater susceptibility.
• 41:49And so I think that there is an opportunity here to think
• 41:52further about these models and what they can lend us
• 41:56for health disparities types of research.
• 42:02So some next steps here is that I have data for more years
• 42:07than just 2019, so I'm going to include more years
• 42:10in this analysis.
• 42:12We also know that there are exposure disparities
• 42:16for other forms of environmental contaminants
• 42:20like ozone or PM2.5.
• 42:23And so I want to potentially control for these
• 42:25as spatial temporal confounders,
• 42:29potentially contributing to these relationships.
• 42:33And then I want to include explicit measures of segregation.
• 42:38So, as I mentioned, I showed the proportion of black folks,
• 42:43but there's a whole host of literature that actually shows
• 42:46different measures of segregation like the dissimilarities
• 42:50index or the index of concentration at the extremes.
• 42:54And I would like to use these
• 42:56as potential predictors in these models.
• 43:02And then finally, I want to analyze these disparities
• 43:06in relation to energy data
• 43:09because I'm interested in studying some quantitative
• 43:15research between energy burden and energy insecurity,
• 43:20which leads me to some of my
• 43:21future directions and opportunities.
• 43:26So if you're unfamiliar with energy insecurity,
• 43:29this is a relatively new framework that my colleague
• 43:33Diana Hernandez at Columbia has used and described
• 43:40as a framework that outlines the interplay
• 43:42between energy needs, financial constraints,
• 43:46and behavioral adaptations.
• 43:48So I think a lot of us are familiar with this concept
• 43:54in what's referred to as the heat or eat dilemma.
• 43:58So the heat or eat dilemma describes the kind of precarious
• 44:01situation that historically poor families have been put in
• 44:06of during the winter time,
• 44:09do they keep themselves warm or do they forgo some staples,
• 44:14like a healthy meal, or perhaps they get their heating
• 44:21from some sort of precarious thing
• 44:23like opening their oven and putting a fan next to their oven
• 44:27to keep their home warm, right?
• 44:30We've heard the stories if not done it yourselves,
• 44:35but I think in a warming climate,
• 44:37we need to start having a conversation on analogous,
• 44:42what I'm coining the heat stroke or go broke dilemma.
• 44:46What does it mean to think about that
• 44:49there are folks who potentially have ACs in their homes,
• 44:53but can't afford to run those ACs.
• 44:58How do we think about that
• 45:00they may be foregoing other important staples of their lives
• 45:05on the other side of things to cool their homes.
• 45:09And so I think that there's a real opportunity
• 45:12for climate epidemiology and climate and health research
• 45:16to engage with some of this.
• 45:21And finally, I'm also interested in continuing to integrate
• 45:25the social and environmental determinants of health.
• 45:29So I didn't attend the society for epidemiologic research
• 45:33conference this year, but I saw on Twitter
• 45:35that one of the big takeaways was a quote from Jay Kaufman,
• 45:39who said that all epidemiology is social epidemiology.
• 45:44And I think that that lends a real opportunity for us
• 45:48to think about borrowing from the social epidemiology
• 45:54literature and also lending our tools
• 45:57to the social epidemiology literature.
• 46:00So we recently just published a paper
• 46:04in Nature Communications
• 46:06where we actually used environmental exposure
• 46:10mixtures methods that were designed
• 46:12for the environmental health sciences,
• 46:15and actually implied it to thinking about neighborhood
• 46:18disadvantage to try and understand some of the infection
• 46:22disparities that we're seeing in New York city for COVID-19.
• 46:28And so I think that there's an opportunity here to continue
• 46:32to, you know, trade and learn lessons
• 46:35across the different areas of public health.
• 46:41I'm also conducting a large natality analysis that I
• 46:45mentioned earlier in Mexico and soon hopefully accessing
• 46:51data for also New York state.
• 46:56And we're trying to apply mixtures methods in this context
• 47:00as well thinking about perinatal and climate epidemiology.
• 47:05I also want to continue to expand
• 47:07my own environmental justice lens.
• 47:10I think a lot of focus in environmental health
• 47:12has been on distributive justice,
• 47:16but what does it mean to also think about different forms
• 47:19of environmental justice,
• 47:20like procedural justice or restorative justice
• 47:24in these contexts?
• 47:26And then finally, I'm hoping to get more engaged
• 47:29in community and policy engaged research to try and find
• 47:33climate energy and health leverage points that we can use
• 47:37to create a more health equitable
• 47:40and climate equitable future.
• 47:44So of course this research relies on a ton of folks to help
• 47:49make this possible, so thank you to all of those folks,
• 47:52as well as the funding that has made this all possible.
• 47:58And with that, I will open up for questions.
• 48:05<v ->So, yeah, thank you, Daniel, for a very well-presented</v>
• 48:09and interesting talk.
• 48:13I could start with a question.
• 48:15Well, maybe other people are thinking about theirs,
• 48:18so you spoke a lot about temperature exposure disparities
• 48:29and then introduced how energy,
• 48:32so you have the temperature exposure disparities,
• 48:36and then on top of that,
• 48:37you have the people with the highest temperature exposure
• 48:42having less of an ability to deal with that high temperature
• 48:46exposure and that part you didn't address as much,
• 48:51you know, understand that you can only do so much,
• 48:54but I'm wondering, you know,
• 48:56have you thought about ways to measure that,
• 49:01let's call it energy insecurity in epidemiologic studies
• 49:06in order to make that next step?
• 49:10<v ->Yeah, absolutely.</v>
• 49:11So I'm interested in this in two different ways.
• 49:15So I think that we could do work to actually collect data
• 49:21from folks to try and get a better sense,
• 49:24a better quantitative sense of people's energy insecurity.
• 49:31So Diana has developed actually an energy insecurity
• 49:37screening tool and so it would be great to try
• 49:42and get that screening tool out there
• 49:44as part of larger studies so that we can understand
• 49:48the kind of geographic distribution
• 49:51of this energy insecurity and trying to overlay that
• 49:55potentially with what we know about temperature.
• 49:59So that's on one end.
• 50:00On the other end, I think the lower hanging fruit
• 50:07is actually to access energy data.
• 50:10And so this is something that we're working on right now
• 50:14actually is to use energy data and pair that with
• 50:19our temperature predictions to see if we could see
• 50:23differences in the dose response relationship
• 50:28between neighborhood temperature
• 50:31and energy utilization by neighborhood.
• 50:34And if we see differences in the slopes
• 50:37between those neighborhoods,
• 50:39then that would imply to me that potentially
• 50:43those are differences in your response
• 50:46to the temperature and your ability to keep yourself cool.
• 50:51Of course, that needs to be adjusted
• 50:52for many, many different things,
• 50:55but that is where I'm thinking as a lower hanging fruit
• 51:00using administrative data at the moment.
• 51:04<v ->Great, other questions, comments?</v>
• 51:10<v ->I have a question or a comment and observation,</v>
• 51:13first of all, this is an amazing presentation.
• 51:16It's brilliant work, and it could not be more timely.
• 51:19And I'm going to go to your last point, talking about,
• 51:23you know, the application of your work and of this research
• 51:26within the current policy development work
• 51:29at the federal level right now.
• 51:31And I think that you're diving in and focusing in
• 51:34on that exposure data and how
• 51:36we're not getting an accurate indication of what
• 51:39the risk are is vitally important
• 51:42and there are a couple of proceedings right now, you know,
• 51:44with the executive order 13895,
• 51:48with executive order 14009.
• 51:50There's an OMB, a docket open until July six.
• 51:54There's another FEMA docket open until July 21st,
• 51:58is how are you, whether you are planning
• 52:02or whether you could consider
• 52:05taking your research and getting it into these
• 52:07and other dockets because that is setting
• 52:10the administrative record where we can start changing how
• 52:14the federal government is thinking about this.
• 52:17So I don't know what your thoughts are in trying to move
• 52:21in those spaces.
• 52:23<v ->Yeah, no, absolutely.</v>
• 52:25And I would definitely look to others who are closer
• 52:30to the policy landscape to help me figure out
• 52:34what the leverage points are.
• 52:36The most proximal leverage point that I'm aware of
• 52:41is actually what environmental justice folks
• 52:44are talking about right now.
• 52:46Folks that We Act are talking about that the low income home
• 52:51energy assistance program has been historically used for
• 52:56helping to keep folks warm during the winter,
• 53:01but has been lesser so used to help keep folks cool
• 53:06during the summer.
• 53:07And so we already have a policy instrument in place
• 53:12to identify the people who need the help,
• 53:15but we don't have the dollars allocated to the right part,
• 53:20potentially the right part of the exposure distribution.
• 53:25And so I think that that is the most proximal policy
• 53:30instrument that I'm aware of that could help move the needle
• 53:34towards improving public health.
• 53:38<v ->That's fantastic.</v>
• 53:40You know I would also throw out taking that
• 53:42as that illustration applying the national environmental
• 53:44policy act and the resurgence and undoing
• 53:47what the Trump administration did to that law
• 53:49because I think there's some opportunities for programmatic
• 53:51environmental impact statement reviews
• 53:54and it would be great to get your data, you know,
• 53:57forming the basis of some of those types of actions.
• 54:00So thank you.
• 54:01<v ->Yeah, thank you.</v>
• 54:04<v ->Other questions or comments?</v>
• 54:13<v ->Maybe just a small technical question.</v>
• 54:16We know that using CDC wonder data
• 54:18for especially the birth outcome,
• 54:22this issue is you mentioned briefly that the temporary
• 54:26resolution is not good enough.
• 54:28They don't accurate give you the exact date.
• 54:32So I'm wondering how do you deal with
• 54:34in your time cross data with that?
• 54:39<v ->Oh yeah, for sure.</v>
• 54:39So we ended up doing a lot of interpolation estimates.
• 54:47So for example CDC wonder can give you how many births
• 54:53there on it are in a day of the week,
• 54:55in a typical day of the week.
• 54:57And it'll give you how many births there were in a month.
• 55:00And so we ended up then doing a lot of averaging.
• 55:05Knowing Tuesdays, let's say are where, you know,
• 55:0930% of the births are happening,
• 55:1120% are happening on Wednesdays, let's say.
• 55:15Using that relationship, again with the longer month
• 55:21time span to then do a lot of smoothing and averaging
• 55:25to get an estimate of how many births there were.
• 55:28I don't think for this study
• 55:30we need an actual accurate number
• 55:33of births because at the end of the day,
• 55:38you're creating your truth with the simulation methods.
• 55:44But it's just a way of making sure that we have good
• 55:48representation of the different age groupings
• 55:52of different preterm births.
• 55:55Are there more 20 week olds perhaps being born in February
• 56:01rather than in June, right?
• 56:04Trying to preserve some of those distributions
• 56:07of the different weeks of gestation
• 56:12was where we spent a lot of our attention.
• 56:16<v ->Thanks yeah, that's makes a lot of sense.</v>
• 56:19And I'm more thinking of like a new addition
• 56:22to your similar study in the future, your future work,
• 56:25if you want to extend to the whole U.S.
• 56:27that might be something to be carefully dealt with.
• 56:33<v ->Yeah, absolutely.</v>
• 56:36<v ->So I, there's a question in the chat.</v>
• 56:38I think this'll be the last question.
• 56:40It's from Taiwo Bello,
• 56:42Please, how convinced are you about these studies
• 56:47considering that Africa has the hottest temperature
• 56:50and majority had no cooling systems in place
• 56:54and what are the limitations of your research findings?
• 56:57Thank you.
• 57:00<v ->Yeah, absolutely.</v>
• 57:01So I think the temperature epidemiology
• 57:05generally shows that there is such a thing
• 57:08as acclimatization, that there are differences
• 57:11in people's response to different temperatures
• 57:14in different parts of the world based on what
• 57:17they're historically exposed to.
• 57:20And so to some degree,
• 57:24people are climatized to the places that they live in.
• 57:28Another factor that needs to be considered as well is that
• 57:34humidity is also very different in different parts
• 57:37of the world.
• 57:38So in Western Africa, for example,
• 57:42at least the places that I've done research,
• 57:44humidity is not as high
• 57:47as it is in the Caribbean, let's say,
• 57:52or in other parts of the world, right?
• 57:54And so humidity plays a big part in our ability
• 57:57to thermo regulate in our ability to dissipate heat.
• 58:01And so I think that that's an important part of this
• 58:05relationship that a lot of temperature epidemiology
• 58:08kind of grapples with to do this.
• 58:12And I think the last thing I should mention is I think
• 58:18that we don't have sufficient evidence in many parts
• 58:21of the world to necessarily say that that heat
• 58:26is not an issue in Africa.
• 58:29There are studies that show that heat is an issue in Africa,
• 58:33even though the dose response relationships
• 58:36may be different, but nonetheless people
• 58:39are impacted by heat in Sub-Saharan Africa as well
• 58:45and I think it's actually a call for more research
• 58:49in the region to understand
• 58:52what those relationships look like.
• 58:57<v ->Okay, so thank you very much, Daniel.</v>
• 59:00You gave a very interesting talk and congratulations
• 59:04on doing such great work.
• 59:06<v ->Thank you so much.</v>
• 59:08<v ->Okay, take care, everyone.</v>