When we talk about data-driven adaptation, I think it's best to think about this in terms of examples. So we want to use data to make the case that either our actions are doing what they were intended to do or that there's a need to make an action. So for this example, we're going to focus on some work that was done in the Northeast. So on the left, you see a map of the National Weather Service weather forecasting offices. And across the nation, there are a 122 WFOs, or weather forecasting offices, for each geographic region. So these don't follow state boundaries or any known well-identifiable geography. You can kind of see that they're a little bit based on where people are located. So these weather forecasting offices are responsible for issuing products like weather advisories, watches and warnings. And with heat, we often hear it in the summertime about heat. There's a heat advisory or there's a heat watch or heat warning. So those specific products are tied to the environmental characteristics that are being experienced. So here on the right in this map, we can see that throughout Massachusetts, Rhode Island and Connecticut. There's some really high temperatures. Ranging from 83 degrees in t the Northeast part, to 98 degrees down in New Haven. And so when these types of products are issued, they're using that type of information that they're getting from monitor data and they're issuing their heat products. But what is interesting, and something to keep in mind, is that for each weather forecasting office, they, or their chief meteorologist, has determined what the threshold will be for for issuing a product. And those are issued dependent on whatever that meteorologist has decided. These products aren't often well-documented. So it's not always easy to see why or when they're issued. So for a project that was led out of a collaboration of researchers and practitioners out of the Northeast, they were interested in understanding what the health impacts were with extreme heat in their particular region. And so they set off to look at some of the the trends that were happening there. So in these graphs what we're looking at in the top left is a model that's showing the rate ratio. So what you would expect to see in terms of hospital admissions on the day of the exposure. So if today it's it's 80 degrees or 85 degrees Fahrenheit, the heat index then is little bit above 1.00. So there's a higher risk of going to the hospital for heat-related illness on that day. So how we have hospital admissions on the left and deaths on the right. And what you can see is that as you go higher in temperature, that those rate ratios increase. The uncertainty, so these gray bands around it, those get wider, because as we get further away with our data and modeling, we're more uncertain about what that information is. But it's important to note here, take a look, is that compared to a baseline day of 75 degrees where the heat index is 75 degrees, emergency visits climbed with statistical significance, starting at 80 degrees. Especially visits that occurred during the full week following those hotter days. So that's what we're calling the lag. So we're seeing this happen much longer after the the actual temperatures occurred. So visits rose to 2% following an 80 degree day, 4% following an 85 degree day, 5 almost 6% at 90s, 7.5% at 95 and 9.1% after 100 degrees, and even more after 100 degrees higher. And so with this information, so following this up with this environment of epidemiological stud, the weather forecasting office there in Taunton, so for the New England area, decided to make a policy change. And what we can see here is that it states, effective immediately five National Weather Service offices that cover Maine, Vermont, New York, Upstate New York and Massachusetts, and of course, New Hampshire, in collaboration with Des Northeast Regional heat collaborative have lowered the heat advisory criteria for all of New England for the coming summer season. And what they specifically note here is that the old threshold of 100 to 104 Fahrenheit for two or more consecutive days has been lowered to 95 to 99 degrees Fahrenheit occurring for two more days. And they conclude with saying it's expected that this change will alert people sooner to impending heat threats. And if acted upon, reduce the number of emergency department visits. So this is an excellent example of using data to influence policy change. And this would be considered a gold standard. So if as we encourage you to think about how you might be able to adapt in your current locality, using data is going to be your best friend. It might not be as sophisticated as an epidemiological study, but there are a whole host of data sources that you can use that can help you make your case.
