Data Journalism: Reporting Where Climate and Health Meet

[00:00:18]

SHEL WINKLEY: Hi everybody, come on in. We are going to give it just a couple of minutes. Let some of our folks kind of wander into the Zoom room, and then we’ll get started here in just maybe about 30 seconds. Thanks for coming today. It is one minute past the hour, so we’ll get started. First of all, welcome, everybody, and thank you for joining us for today’s webinar covering where health and climate meet. Over the next 60 minutes, the next hour will help you better understand how and where to find climate and health data, define some of the key terminology, guide you to where you can find the right experts for your stories and storytelling and really dive into how to tell familiar stories in a way that centers around the communities most affected by climate change and its health impacts.

As a side note, today is World Health Day. April 7th marks the founding of the World Health Organization 78 years ago in 1948, focusing on a specific critical global health theme. This year’s theme from WHO is ‘Together for Health Stand with Science,’ emphasizing scientific collaboration to protect human, animal, and environmental health. I do want to take a second to introduce the folks that have made this webinar possible today. We’ll start with Climate Central. If you’re not familiar. We are a nonprofit, non-advocacy organization dedicated to researching and reporting about our changing climate.

At Climate Central, we develop and distribute, broadcast, and publish-ready climate content and data for your storytelling and communication. We’re co-hosting today with our good friends from SciLine, a free service for journalists and scientists based at the American Association for the Advancement of Science, the world’s largest multidisciplinary scientific society. SciLine has the singular mission of enhancing the amount and quality of scientific evidence in news stories. My name is Shel Winkley. I’m your pilot today. I’m a senior engagement specialist and meteorologist here at Climate Central. The recording, or the briefing rather, is being recorded, and a recording will be emailed to everyone after the call as well.

Today’s webinar is focused on something that we’ve heard from many journalists and storytellers that have told us they want to learn more about, which is how to find and use climate data, but specifically how to connect it to their storytelling around the impacts of health. There are great free tools available that allow you to answer very specific questions about heat, precipitation, air quality, and long-term trends locally to bring it down to the cities that you’re reporting and telling these stories for. We’re here to help you either get started on that storytelling or to dive deeper into the data. We’re also going to look at a few stories that have covered the climate, health, and equity intersection in hopes that we can help to spark story ideas that are important in your community. So let me introduce the folks who are going to be doing most of the work here for us today. During today’s seminar, you’ll hear from three experts. The first of which is Kaitlyn Trudeau, who is the applied climate scientist here at Climate Central. We have Tori Espensen, training director at SciLine, and one of my favorite people to just read stories from, and learn from, Melba Newsome, an award-winning independent journalist. After the presentation, we’ll open up to questions for the audience. At the bottom of your Zoom screen, you will see a Q&A feature where you can go ahead and you can drop those questions in. So without further ado, I want to hand it over to Kaitlyn, who will walk us through the data.

[00:04:01]

KAITLYN TRUDEAU: Thank you so much. As Shel says, my name is Kaitlyn Trudeau. I am the applied climate scientist here at Climate Central, and I have been here for a little over, almost eight years now, and my main job has been working with the data. A lot of times, people ask about certain topics, but really, what I love about Climate Central’s data is that it’s so expansive. It can go across all kinds of topics. But health is a particularly great one. So I think it’s just important to start off by saying that every climate story is actually a health story. We are inextricably linked to our environment. We cannot not be impacted by the world around us.

As the world around us changes, our health is also affected. And people ask, “How is that true? Like, how is every climate story a health story?” And when you think about kind of in this three-part process, you think about these climate drivers, so you can think of something like a wildfire. And then you think about pathways, how does that affect your body, smoke inhalation, things like that. And then outcomes, respiratory disease, things like that. So it’s kind of this three-step process where it brings you from climate, and these impacts and these hazards that we’re having, to our health and actual outcomes that we experience. There are so many data sources that I could feel like get into, but I’m trying not to overwhelm everyone in fifteen minutes. So just going to go over some of my favorite sources for looking at data that you can bring into health stories. The first one is actually the one that we talked about last time, which is the Applied Climate Information System.

This is an excellent system by NOAA and the regional climate Centers, and it’s local data, which is so hugely important for these kinds of stories. It is not just temperature data. It’s not just precipitation data, but there’s so much context within that data. You can look at how long the growing season is. You can look at specific precipitation on certain days when there’s hurricanes. How much actual rain fell in this location? What does that mean for mold? Things like that.  Another one is the local climatological dataset by NOAA, as well. This is just basically a huge collection of weather stations, but what’s super special about it is that it has a lot of hourly data.

Now, the Applied Climate Information System also has some hourly data, but the local climatological dataset is really one of the best sources for local hourly data. It also includes humidity, which is really, really important for our health. So if you’re looking for humidity data in particular or if you want to look specifically at hourly data, I’d say that was a really great resource. They also have a map that you can look at. You can download the data in CSV. It’s a really great resource.

Another one is Google’s Climate Engine. I don’t know if many people have heard of this, but it’s essentially an interface to look at all different types of gridded climate data. It mainly has gridded data. It has, I think, maybe several dozen climate data sets that you can look at. You can download maps. You can extract data from a singular point. You can aggregate the data over a county or over any kind of polygon you want to draw.

It’s really great for calculating heat index. So that’s what we use. When we talk about heat index, we pull humidity and temperature data from gridMET, which is one of the datasets in Google’s Climate Engine. So if you’re trying to do humidity, heat index type stuff, it’s also really great for our mosquito days analysis. When you look at mosquito days, we’re talking about days where the temperature and the humidity are just within the sweet spot that allows mosquitoes to flourish. And we also use Climate Engine for that data as well. And not to be super self-promoting, but we also have our million-dollar disasters dataset that is now Climate Central.

This is not just great just to see the major disasters, the costly disasters, but also the impacts, what that means for the economy, what that means for our health, what that means for infrastructure, things like that. So that’s another really great resource. And then also our Climate Shift Index. That’s our daily temperature attribution tool. And this summer, we will be releasing our humid heat daily attribution tool, which will be really exciting. So stay tuned for that.

And then just some other really helpful resources, the EPA AirNow. If you’re looking for air quality, you want to know about wildfire smoke, or you want to know about ground-level ozone, things like that, the EPA AirNow dataset actually has a lot of really great interactive resources where you can download days above a certain concentration level. You can look by county. You can also look at a map over time. You can also just look at smoke plumes.

If you want to see how wildfire smoke travels across the country or comes down from Canada, that’s another really great resource, as well as NOAA’s Hazard Mapping System. CDC is obviously a health resource, but there’s a lot of great heat-related data, emergency room data as well, that we can tie into that. And then the Lancet Health Countdown is a really excellent resource for all things health and climate. It’s got some really great data in there that you can download directly from the website. You can read the report. And then also the U.S. Census data, which may not seem immediately like a health data set, but there’s a lot of things in there that you can pull out, not just the demographics, but also like air conditioning, for example, how many homes actually have air conditioning units, things like that. You can also look at vulnerable communities. Where do we see these concentrations of people who are more affected by these kinds of health impacts than others? I want to give an example about one of the things to be careful for. This is also because I have been really involved in our humid heat attribution tool, so it’s how I have it on the mind.

But I wanted to give an example of where you might want to be careful when you’re talking about something like humid heat, for example. So not all heat is equal. There’s so many different types of heat humidity indices. They all answer a different question, essentially, and they tell you something different. Neither one is perfect, but it’s really important to know the difference between them. So heat index is basically a feels-like temperature. It basically is a measure of comfortability. But it’s also really important to know that it assumes you’re standing in the shade and wearing very light clothing. So it’s really an under count. Wet-bulb temperature is basically a measure of how effectively you can cool your body, which is really important through sweating, right? So when the air gets too humid around you, it limits your ability to actually sweat, which means you’re having heat trapped in your body, which can cause all kinds of organ failure, which is really not great. With wet-bulb temperature, there’s actually an upper limit, which is called the survivability limit. To which above that, our bodies can no longer function. We used to think it was about 35 degrees Celsius, but it’s actually a lot lower than that.

There’s not one single temperature, but we’re learning that there is this limit to which we really need to pay more attention to because our bodies can no longer function in that sense. So wet pool temperature is really great for that kind of a story where you’re talking about how effectively our bodies can cool ourselves as the climate continues to change. One thing I always think about is prescription drugs. There are a lot of people who take prescription drugs that limit their ability to sweat, and what does that mean in a world that’s becoming increasingly hot and more humid. Wet-bulb globe temperature, so many people get these two confused, and I’ve seen it across all types of really credible websites, where people have really gotten confused.

Wet-bulb temperature is basically a measure of heat and humidity. Wet bulb globe temperature is just a more detailed and more comprehensive measure of heat stress. In addition to heat and humidity, it also takes into account things like solar radiation and wind speed, things like that. And web-globe temperature is actually developed by the military. It’s used a lot in that kind of context, but it’s also used to measure set outdoor safety standards, and it’s used a lot in sports as well. I wanted to give a little note about attribution because a lot of times, people get really nervous about climate attribution. We’re not sure about the science, but it’s actually the very same science that has been used to attribute things about our health.

For example, we know that smoking increases your chances of getting cancer, right? Smoking a cigarette doesn’t mean you will get cancer, but it increases the chances that you will get cancer or other health-related diseases. And this is the same kind of thing that we’re talking about. These are the same concepts that we’re using when we talk about human-caused climate change, and attributing our actions to things like really hot days.

So again, this is our climate shift index map. You can look anywhere in the world on any given day, and you can see how much more or less likely temperatures are due to human-caused climate change. This is a great source to bring into your data. It’s one metric. For example, if you’re living in Grand Junction, Colorado, right now, you are currently experiencing average temperatures that have been made at least five times more likely due to human-caused climate change. So that’s something that you can bring in. You can talk about the impacts of heat on your body, and talk about the fact that we are making this kind of heat much more likely, and what does that mean moving forward? What can we do about it?

And just some other, more general notes of caution, data like this is not available everywhere, and it’s most often in countries that are richer. So there’s a huge undercount of people who are more vulnerable in places like Africa; we just do not have that kind of data. So it’s important to note that when we’re talking about global data, local data, anything like that, just knowing that the data is not available everywhere. So there are going to be biases in that.

There’s also a lag in just the data reporting, but also the impacts. There are sometimes where you will be impacted by something, but it won’t show up in your health much later. And there’s still a lot of uncertainty. It’s important to not be overly confident or trying to say, “You know, this for sure is causing this.” So obviously, things like this is made more likely than it would have been otherwise without climate change. And the undercounting and the lack of data is a story itself. It’s something that I think a lot of people are trying to think about, how we can address that moving forward to really help the people who will need it most, the people who will be impacted most, which are usually the people who are living in areas with no data.

Another thing I’ll say, we’ve had climate matters. These are some of just some great health-related ones we’ve had in recent years. So the air quality, mental health is also one that’s really great, and I think is still under-reported, talking about the effects of anxiety, especially for young kids today, and just the kind of grief and concern that they feel moving forward into the world that we are making for them. Also, things like mosquito days, things like ticks. But it’s also really important to be careful should also note that there are other things that are impacting these trends. Ticks, for example, we know that the range is impacted by temperature, precipitation, things like that, but it’s also impacted by how many deer are around.

If the deer group is increasing, that could also be why the ticks are increasing, because we’re just seeing more of them. Things like that are just really important to keep in mind. I think that’s all I have on my end. Now we are going to Tori, I believe.

[00:15:38]

TORI EPSENSEN: Yes, we are. Thank you so much, and I will share my screen. Reporting on this intersection between climate and health may feel really daunting because not only are you going to find yourself getting into datasets and reading scientific studies and reports, but you also have to deal with the jargon of two different, although definitely interrelated, scientific fields.

But I promise that that does not have to be intimidating because you don’t have to get into those weeds all by yourself. You really only need to be able to do three things when you’re writing a climate and health story. You need to be able to find a story. You need to be able to find expert sources and ask them the right questions, and you need to be able to explain what those experts said and why it matters.

For that first step, finding stories, Kaitlyn’s already gotten us started here, because one of the ways to find stories is to look at data and see if there’s something interesting and important. So Kaitlyn has given us a bunch of different datasets you could potentially look at, a bunch of things to be considering. Let’s say you’ve done all that, and now you need to determine whether there’s a story here. Before we dive in, I want to emphasize that you remember, as Kaitlyn just said, that climate and health go beyond just weather events and diseases. You don’t want to be overlooking mental health stories, community health stories, cumulative impacts, more chronic climate-related stressors, infrastructure, engineering stories.

There’s a whole realm of stories in this climate and health space that go beyond weather events and disease. With that, though, here are some of the terms that you’re going to find in health-related datasets, and understanding them can help you determine whether there’s actually a story in all of these numbers. Of course, there are a million different terms you’ll come across. These are just some of the ones that you might see most frequently, that we wanted to give an overview of.

The first is the difference between morbidity and mortality. Morbidity refers to the number of people who are affected by a health condition. It could refer to how many people show symptoms of a disease or how many people have been diagnosed. Mortality refers to the number of deaths from that condition. So morbidity is just having that condition, and mortality is dying from it.

But to make things more confusing, morbidity data is typically reported as incidence or prevalence, and these are not the same thing. The incidence of a condition is the number of new cases of a condition over a specific period of time. So let’s say the number of new cases in a specific year.

The prevalence of a condition is the total number of people who are affected at that specific time. So let’s say that you’re interested in reporting on how increased air pollution impacts asthma. In 2021, the prevalence of people worldwide with asthma was about 260 million. In that same year, the incidence of people with asthma was about 38 million. So 38 million people displayed symptoms for the first time, and 216 million people displayed symptoms at all. The mortality of asthma in 2021 was 436,000. So that’s how many people died. Keeping all of these different numbers straight can help you identify stories in data. In our hypothetical dataset about asthma, maybe you see that the prevalence rate is going up over time, but the mortality rate is going down. That could indicate that there’s a story about cases of asthma being more common but less severe, or it could be a story about improving access to asthma treatment.

Maybe incidence of asthma is going up, but prevalence is going down, which would suggest that maybe more people are growing out of asthma as they get older and no longer meet the criteria. Any of those could be an interesting story. Of course, you also want to make sure that you’re using the right words and explaining to your audience the meaning of those words that you’re referring to in your story. Datasets can also include information about the impact of health conditions. When we’re thinking about the impact of conditions, one of the most comprehensive datasets is the Global Burden of Disease project out of the Institute for Health Metrics and Evaluation. Ignore my fudged animation here. Great news. This particular dataset is not a federal dataset, and the study is funded by philanthropy. So these datasets are still really easily accessible, and they’re continually being updated with new data.

One of the ways of measuring the impact of health conditions is calculating the years of life lost. This number reflects the total years of life lost due to premature death related to a condition. So if the life expectancy for a man in the United States is 76 years, but he dies of heat-related kidney disease at the age of 60, that’s 16 years of life lost.

You can get this number for a whole population by adding that gap up for everyone with heat-related kidney disease. A similar concept that measures the impact of a health condition is years lived with disability. This counts how many years someone is in less-than-ideal health due to some specific condition, and it multiplies that by how severe or limiting that disease or condition is. Living with bone cancer for three years would be more years lived with disability than living with allergies for three years. Once again, you can add this metric up across everyone with a specific condition in a given population. If you add together the years of life lost and the years lived with disability, you get Disability Adjusted Life Years or DALYs. Returning to the question of asthma, the global burden of asthma in 2021 was 675,000 DALYs. You can find these metrics for different countries, regions, and demographic groups. You can also find these metrics not just for specific diseases but for risk factors. For example, you might be interested in whether DALYs of pollution-related illnesses are increasing.

These metrics and many others that you might run into on both the climate and health parts of this equation are estimates. It is simply not feasible to go through the medical records of every single person who has ever died of heat-related kidney disease in every country, see how old they were when they died, and add them up. Even if you could do that, that would only tell you how many years of life had already been lost and not how many years will be or could be lost. Instead, scientists have to extrapolate from data that they do have. This means that there is always going to be some wiggle room, and things may change or shake out differently. This does not necessarily mean that the models and estimates were wrong. When we predict an estimate, we determine a range the outcome is most likely in. For example, if we run this model 100 times, 95 of those times, we’re going to get an answer between this number and this number. Or if we pick a random sample of data 100 times, 95 of those times the average is between this number and this number. Importantly, that means that we go into it knowing that there is a chance that we’re going to be cutting out the real answer. So remember that just because something plays out differently or doesn’t come to pass or turns out differently than our predictions and estimates does not mean that the scientists screwed up. Every model and estimate has some degree of uncertainty in it. That could be because we don’t have measurement tools that are precise enough. It could be that there are unknowns in certain contributing factors. Uncertainty does not mean that scientists are clueless. It means that there’s always, always going to be a margin of error in our understanding, and when you’re writing about uncertainty, it’s really important that you reflect that.

We’ve talked about data about outcomes and data about the impacts of those outcomes, but I do also want to take a moment to talk about vulnerability to those outcomes. Social vulnerability, which is measured by the Social Vulnerability Index, is the combination of demographic and socioeconomic factors that can work against communities when they encounter stressors like tornadoes, disease outbreaks, or chemical spills. Importantly, when we’re talking about social vulnerability, social vulnerability operates on a community-wide level, not on an individual level. There are, of course, always going to be people in a community who are more or less vulnerable to external stressors. But when we’re talking about social vulnerability, we’re talking about factors that affect the community as a whole. So things like community-wide poverty and unemployment rates, the population over 65 and under 17, English language proficiency, the number of mobile homes in an area, access to transportation and cars, things like that. There’s a list of everything that goes into this factor on your screen.

Considering the social vulnerability index can help you find stories about how readily a community is able to evacuate in the event of some extreme weather, or whether there are enough emergency personnel in a specific area, or what emergency supplies are needed in an area, are they getting the emergency supplies that they need. So now that you’re familiar with the first two of the only three things you have to do to write a climate and health story. You found the data, you’ve looked through it, and determined whether there’s a possible story there. Now for the second step: talking to experts about it. You’re not always going to want to turn to one of the creators of the dataset. In some cases, like that Global Burden of Disease study, the projects are so massive that the people involved with the collection probably won’t have a lot of time to talk with you and walk you through it.

With some government agencies, it can be really, really hard to break through layers of press offices and communication staff to actually talk to one of the researchers. Sometimes you may just want somebody who wasn’t involved in the data collection at all, who can talk you through the strengths and weaknesses of the data in an unbiased way. This is where I want to emphasize that it is not just about finding an expert, it is about finding the right expert. The right expert is going to be someone who is deeply entrenched, not just in the field generally, but in the specific question you have for the specific story you’re writing. Scientific expertise is often extremely narrow. Unlike a reporter whose beat area may just be environment or health, scientists and researchers often spend their whole lives studying a tiny niche of a tiny niche.

Something like how rural aging populations are particularly vulnerable to climate change-driven extreme weather events. This is really important to note because it means that you don’t just want to be approaching anyone who studies climate change and health, or anyone that’s ever used a particular dataset. The experts who study rural aging populations and extreme weather are going to be able to go beyond just telling you, “Here’s what the labels in this dataset mean. This is a big number for this particular measure.” They’re going to be able to give you a lot more context, detail, and clearer explanations of the how and why behind certain phenomena than someone who can speak more generally will be able to.

They’re also more likely to be able to point out the flaws with a particular metric or potential caveats and limitations. They can help you find other data sets. They can help put the data you’re looking at in context, help you understand whether this is a story worth telling, introduce you to new angles you hadn’t considered, and share personal stories and anecdotes from their own experiences or people that they’ve worked with.

All of which are critical for making your stories accurate, nuanced, and engaging, and helping them stand out from other stories on the topic. So you don’t just want to turn to somebody who’s been quoted in another story or somebody who’s famous on social media, or even the chair of the local Epidemiology or Atmospheric Sciences Department at your nearby university, because they may not actually be the best person for your specific question. Instead, you want to start by looking at the science surrounding that particular question and then work backwards to find out who’s doing it. Scientific paper databases allow you to search just for academic papers, and you can reach out to the authors of any relevant work.

When you search in something like Google Scholar, you can also include the name of the dataset that you’re working with in the search bar, along with your topic keywords, to identify people who are publishing studies and research using those data. You can also try looking at faculty pages on websites and look for what people list as their research expertise. While you’re on that local university website, you can also reach out to PIOs. The public information officers can be really, really great contact points. But you do want to keep in mind that sometimes PIOs care more about getting someone from their institution quoted than they do about whether or not they’re really the right person for your story. You can try looking at expert databases. These can be associated with a single university, a field, or a historically marginalized group. As a general rule of thumb, you want people who are actively publishing on your subject. If they haven’t published on it in five or so years, roughly, I would say skip them, even if their website says that this is their area of expertise, because websites can regularly be ten years out of date. I think I recently saw one that hasn’t been updated since 2011.

In addition to the fact that the right expert is going to make your story that much stronger, doing the work to find these scientists is a really great way to introduce new and diverse voices into news stories and not just turn to the usual suspects. Scientists from diverse backgrounds are less likely to get funded, published in high-profile journals, or quoted in other news stories. So if you’re looking for the most well-known or quoted scientists in a more general field, you’re probably going to miss these scientists. Including diverse sources in science stories can add important context, help combat stereotypes, and broaden trust in science and journalism both. You have a journalistic responsibility to reflect the entirety of the communities that you serve, and you have a unique opportunity to help bolster public trust for science and scientists as a whole. I really recommend that you take advantage of that. So I will stop there, and next up, we have Melba Newsome, who is going to share what this looks like in practice, along with some real examples from her own reporting.

[00:31:59]

MELBA NEWSOME: Hi. Thank you so much. This has been great, and I don’t even know if I need to speak after Kaitlyn and Tori finished because they talked a lot about journalism and how to go about your reporting.

As Shel introduced, I am an environmental journalist. That’s my specialty. Basically, I would say, health, science, environment, and climate. I’ll tell you a little bit about my path to this. I’ve been a health reporter, a longtime freelance reporter for more years than I’d like to admit in public. But my experience on reporting on the intersection of climate and health and equity started in 2020 with the pandemic, and that was the first grant that I got from the Pulitzer Center. I said I wanted to do a story. I live in Charlotte, North Carolina, and I wanted to do a story about the impact of COVID on Charlotte’s Black community because by that time, we had seen that there was great disparities in who got the worst of COVID, who was impacted the most. North Carolina keeps really good data by race and ethnicity, and that goes back to the Voting Rights Act of 1965, and they had to determine what ethnicity people were, where they lived, and that kind of stuff. That allowed me to look at what was happening and who was most impacted. And of course, Charlotte’s Black community was suffering much greater, and there were exacerbated in that community. And so I found out that what was happening with COVID, there was a climate connection, an environmental connection, because I saw that the people who were suffering the most were the people who lived in the worst communities, the communities that had the poorest air quality. There were people who had to go to work, so they were at risk. They lived in crowded housing, and lack of insurance, and those kinds of things. And it created the perfect storm for severe health outcomes, and those things were really impacted by the environment. There was more pollution in those areas. They were sicker so led to more respiratory diseases, and fewer healthcare resources are harder to get to them. So that kind of started my focus on environmental justice and health, or environmental injustice and health, and it became very clear that that was a problem.

I started a newsletter, a local newsletter on the Meta platform called the Coastal Plains Environmental Advocate, and that focused on Eastern North Carolina, which is famous for being the site of several counties have more hogs than people by a factor like ten, and the industrial hog farms, industrial poultry farms, PFAS contamination, and then wood pellet farms, coastal erosion, sea level rise. So all of these things come together in this toxic mix for people living in that community and what they are up against. That kind of focused my reporting. And when I first started, I’m like, will I have enough to report about? And I had so much to report about because there was so much going on.

One of the things was getting the people who lived in those communities to realize their connection to climate change, because I’ve been yelling about climate change for longer than is probably healthy. But when I would talk to people in the Black community, in my community, about climate change, they would say things like, “Well, that’s a rich white person’s problem,” because they were thinking it was only about saving the polar bears or how much snow you get in Jackson Hole, that kind of thing.

And it’s absolutely not the case because the people who live in those communities, vulnerable communities, are the most impacted by climate change. When we talk about, for instance, the Gullah Geechee community, which is along the coast that stretches from Jacksonville, North Carolina, to Jacksonville, Florida, they have been identified as some of the most climate-threatened people in the world.

People on the eastern coast, there are a lot of people of color there and vulnerable populations. I’ve always said that every story is a climate story. I think there’s an angle. Then also, as Kaitlyn said, that every climate story is a health story because there is definitely an angle to that. Events like heatwaves, floods, wildfires, droughts, they cause injury, death while affecting things like mental health and food insecurity. So we did a series with Climate Central and local outlets about climate and health, and that was a Robert Wood Johnson-funded project showing these connections. Shel, can you put up the slides from that? Can you share the screen and put that up for me, please?

There are so many ways to talk about these issues. And I think we miss out on a lot of those. Infectious disease, I think one way we can look at that is to explore who’s vulnerable. When we talk about heat, everybody knows that heat kills more people each year than all other climate-related events combined. And sometimes when we report on this, we wait until it gets 100 degrees, and we show how the mercury is going up, or somebody is trying to fry an egg on the sidewalk. But there are so many ways to talk about the impact of heat. We can talk about who it impacts, vulnerable people, which are elderly, poor, pregnant people, outdoor workers.

Those kinds of things we can approach that looking at policies that impact these people. Energy poverty, for instance, they may have air conditioning, but they’re poor and can’t afford to run the air conditioning. We can talk about the inability to cool down overnight, which a lot of times we just focus on how hot it gets during the day. But overnight temperatures are very impactful for older people, for people who do outdoor work, for pregnant people; they don’t have the ability to recover overnight. Also, housing, it makes a big difference- what kind of housing stock we have. When there’s a lot of reporting on urban heat islands, which is great, but one thing that’s overlooked is there’s vulnerability for rural people as well.

One of the researchers who looks at this very closely in rural communities is at Duke, and I’ve talked to her a lot, that rural people are more vulnerable and more at risk because they have fewer resources. Their housing and ability to cool down is they don’t have a good housing stock, substandard housing, things like that, hard to get resources.

There aren’t cooling centers, and there are a lot of agricultural workers. So those are ways that we can talk about heat instead of just looking at the temperature. So one of the things that I wanted to look at, too, is about the good news, well, I think the bad news is about health, that impacts everybody’s health, but the good news is that gives us an entry point to make people care.

Because the problem of getting changing behavior is people think that climate change is so far off. We’re talking about what sea level rise will be in 2100. But we can look at what’s happening now. It’s impacting health now. And when we tell people that this can increase your risk of preterm birth, or it can impact your medications and how you do that, and flooding brings more vectors to the environment, increase respiratory diseases right now. That is a way to get people to change behavior and to care about what climate is doing right now. I think that offers a wealth of opportunities for us to write stories about that from a lot of different vantage points. I’m going to pause right here, and Shel, I think you had a few questions that you wanted to ask me about this.

[00:42:30]

SHEL WINKLEY: Yes, thanks for that. Thanks to Kaitlyn and Tori, as well. Reminder, if you do have any questions, the Q&A box is open. We’ll get to those in just a second. Melba, I’m glad you brought up overnight temperatures. You talked about you’ve been screaming about climate change for longer than you think is healthy. For me, those overnight temperatures, that’s where we look at the summertime, we know that overnight temperatures are basically twice outpacing what record highs are happening during the overnight hours. That overnight temperature is so important, especially for folks that don’t have air conditioning or the means to cool themselves down and to get their body ready for the next day. That’s where those health impacts really start to stack up, especially for these longer-duration heat waves, which we know with climate change are becoming more intense and becoming more frequent. I think my first question for you is, you’ve been at this for a long time. You have a lot of insight, and we can always do more to learn, right? The next story can always be told a little bit better than we did the last story. We take a learning from what we learned in the last story and apply it to the next. As a journalism community as a whole, not to single anybody out, but what are we doing well at connecting climate, health, and equity together into our storytelling, but also maybe the second part of that question is, where are we falling short, or what’s being missed that we could still be connecting into these stories?

[00:43:59]

MELBA NEWSOME: I think we’re doing a much better job of making climate a full-time beat for news reporters and stuff, bringing on a climate reporter and not just having it, some niche thing that maybe you’ll write a story about when it’s hot. The number of people who are focused on climate has really improved and increased, and meteorologists, too. I see more talking about not just the weather, but talk about how climate is a threat multiplier. People often ask, “Did climate change cause that?” I say, “That’s the wrong question. Did climate change make it worse?” Nine times out of ten, yes, it did make it worse. We’re talking about Hurricane Florence. The meteorologist determined that climate change made it like 50 miles wider and brought 30 additional inches of rain because of that. So I think tying in, making it relevant to people, is really important, and I see more of that being done by folks from everybody who is a climate reporter to a person who’s a weather reporter, talking about how this is impacting and who this is impacting. I have people ask me, “What am I going to do? I just feel like I can’t do anything because it’s such a great problem, and whatever I’m doing is just a drop in the bucket, and it’s not going to make a difference.” So I think we need to report more on solutions and not just doom and gloom, so people don’t feel that their efforts are wasted.

[00:49:13]

SHEL WINKLEY: This is a tough time for the planet and for environmental and climate justice when we have an administration who is looking to roll back all of those efforts. But I think we need to show solutions, show impacts, and solutions so that people feel that they can make a difference. Can I hold on one of those threads just real quick? You mentioned how a meteorologist may bring up, hey, this storm was made this much worse in terms of the wind and the rainfall. But a lot of times we get asked the question, or we hear from people who say, “You know, it’s just not the time to talk about climate change. There’s a big hurricane barreling towards the United States coast. This isn’t the time to talk about climate change.” Do you agree with that? Do you have maybe some advice to that piece? And maybe how do you do that delicately when people’s lives might be being displaced because of extreme weather?

[00:49:13]

MELBA NEWSOME: Well, I mean, when I hear people say, “This is not the time,” those are people who use that to escape that. For instance, and maybe I’m going off on something, I shouldn’t be here, but whenever there’s a mass shooting, folks who don’t want anything done about guns, they’ll say, “This is not the time to talk about this.” When is the time to talk about it? That’s just a cop out that you don’t ever get to talk about. Every time, all the time, is the time to talk about it, something that is that pervasive and that impactful, and the planet is on fire, okay?

So there’s no time not to talk about it, even when it’s snowing and we got a big blizzard. It’s still time to talk about climate change. We had a senator who brought in a snowball to the floor and said, “I don’t believe in this. Look at this big snowball I got picked up off the ground.” So people will use that to say that we don’t have a problem with that. But I think we have a responsibility of journalists to talk about something that is so impactful every single day, and there is an opportunity and a way to get into that. And I just think we need to keep making those connections, but also helping people understand not to give up and just do the best that they can to have a positive impact.

[00:49:13]

SHEL WINKLEY: Well said. All right, one more for you, and then we’ll jump into the Q&A box. During my time in a newsroom as a meteorologist, there are a lot of times when I was tasked with a story, and I was given maybe four hours to go out, find my sources, find my interviews, shoot it, edit it, and make sure that it was on air by 4:00 – 5:00 P.M. Do you have any tips, maybe some tricks? How do you make sure that your reporting reflects the communities most affected, versus just the most excessive of sources? Because I think a lot of times when we’re on this time crunch, or we’re on a very short timeline or deadline, it’s really easy just to find the first person that says yes, versus the best qualified person, like Tori was talking about.

[00:49:29]

MELBA NEWSOME: Well, I do think it’s good to have what we used to call a Rolodex, a database of sources that we can go to, and don’t wait until you have a story to put that together. But I think work on that, build connections with people in community who lead local groups, like local environmental groups, local health groups, and things like that.

They are often willing to talk and then can connect you to other people in the community, and not just waiting until you need the story to build that relationship in the time when you’re not writing a story about them. I heard about the work you’re doing here, and I find those people are really open to talking to you. Most of the time, we quote the same experts because we know they’re going to answer the phone when we call in an emergency, and they’re going to give a good quote. But I think we need to build relationships and sources in the downtime.

Another thing, we often, if there’s something that’s happening to a vulnerable community, we will go into the vulnerable community sometimes and talk to them and use their story about how oppressed they are, whatever. But the experts that we get, that we quote, are often not from the community.

So it leaves this impression that we are just the victims and aren’t doing anything to help ourselves or address this problem. I think it’s important to have experts who represent that community as well. And so that’s where you look for people who just aren’t necessarily on TV, but who’s doing the research, Google Scholar, and finding people who are doing this kind of research that may not get the front page of the New York Times every time. But there are plenty of people who are out there doing that work. We just need to take time to find them.

[00:51:50]

SHEL WINKLEY: Yes, that relationship building is important too, because they’ll maybe they think they have something interesting and they’ll reach back out like a story you didn’t know about.

[00:51:59]

MELBA NEWSOME: Exactly, because people call me all the time, but it wasn’t like that when I first started covering the beat. But now people will call me if something is happening, and that would be great.