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Candidates for top election post split on mail voting, election trust
The candidates for Colorado’s top election office offered different visions for the state’s election system during Saturday's debate between Democrat Amanda Gonzalez and Republican James Wiley.
WATCH: Candidates for Colorado’s top election office debate issues
Secretary of State candidates debating on Oct. 10 include Amanda Gonzalez and James Wiley.
Every Wiley College player drafted by the Boston Celtics
Let's take a look at every player who has been drafted by the Celtics out of Wiley College.
Is 50 the new 60?
Subscribe TL;DR: Younger generations are showing signs of worse health compared to earlier-born cohorts at the same age This backward “drift” is showing up both in mortality risk and chronic disease Worsening trends in obesity and diabetes are the most consistent signals Taking the long view, we’ve made astonishing gains in health and life expectancy in the last century. Global average life expectancy has more than doubled from 32 years in 1900 to 73 years in 2024. (But don’t forget, there were still old people when life expectancy was 32). As people lived longer, they also generally lived healthier along the way. It turns out that avoiding things that can kill you early, like malnutrition and lots of childhood infections, also keeps you healthier later in life. In a classic 2004 paper, Finch and Crimmins proposed that cohorts of children who benefited from fewer early life infections had less lifetime exposure to inflammation and cardiovascular disease later in life, accelerating mortality improvements at older ages. Poor nutrition in the womb has also been linked to a higher risk of later chronic disease. The upshot is that improvements in childhood health can pay dividends many decades later. The combination of better living conditions (nutrition, sanitation) and medical advances (antibiotics, vaccines, blood pressure meds, etc) meant that people’s healthy lifespan generally expanded, even as they lived longer. Aging researchers call this best-case scenario “compression of morbidity”—meaning we delay the onset of disease and disability even faster than the years of life we are adding. During my 20+ year research career, we could generally bank on the idea that younger generations would not only live longer but also keep getting healthier along the way. “60 is the new 50” as they say… Enter “Generational Health Drift” Sadly, this expectation of steady progress may be changing. You’ve probably heard that US life expectancy gains have slowed to a crawl in the past couple of decades. Mortality improvements are also slowing (though less dramatically) in many other high-income countries (you can read more about that in a review paper I wrote here). Mortality is a very blunt health measure, but it is often a good barometer for the broader population health trends happening underneath. And we are now seeing that younger generations may be in worse health at similar ages compared to earlier born cohorts. My collaborator Prof. George Ploubidis at University College London coined this worsening health in more recently born cohorts “generational health drift.” “Generational health drift” is bad news on multiple fronts. Most obviously, we don’t want our health progress to go backward. But the timing is also bad, since our populations are aging. In demographic terms, “population aging” means that the proportion of the population at older ages (i.e >65 years) is increasing. Fun demography fact: Current population aging is not due to the recent declines in the birth rates making the news, but declines in fertility decades ago, after the “baby boom.” As baby boomers reach older ages, the smaller cohorts behind them mean the percentage of older adults will increase. For example, the share of the population age 65 and older in the US increased from 12.4% in 2004 to 18.0% in 2024. This story of population aging was written long ago. Many worry that population aging will put pressure on health care and pension systems. But if people reach older ages in better health than in years past, those fiscal pressures would be less dire than projected. Healthy older people can also continue to contribute to society in many valuable ways, including paid work, caregiving, community service….even high political office 🤷. On the flip side, if younger generations reach retirement age in worse health and with more disabling conditions, this could magnify the societal challenge of population aging. Evidence for worsening generational health A recent paper that I LOVED in the Proceedings of the National Academy of Sciences by Dr. Leah Abrams and colleagues examined which birth cohorts (aka generations) were behind worsening US mortality. Behold the Lexis diagram (cue swooning demographers). Calendar time is on the x-axis, age on the y-axis. Birth cohorts can be followed over time as they age on the diagonal lines moving up and to the right. This figure plots the year-on-year % change in all-cause mortality at a given age. Green shading indicates mortality improvements over time, white is stagnation, and gray shading indicates worsening mortality. Following the diagonal line for those born in 1950, you can see a noticeable shift from green to gray that follows this cohort for most of their lives. This gray shading shows generally worsening mortality for the 1950 to 1959 birth cohorts compared to their predecessors across all ages. Concerningly for my GenX and elder millennial peeps, there was also a deterioration in mortality for birth cohorts born in the 1970s and 1980s, visible as dark gray areas at ages 30 to 45 during the 2010s. While some of these mortality changes reflect specific shocks such as the opioid epidemic, most deaths still occur from major causes of death such as cardiovascular disease and cancer. For cancer, the gray in the top left corner of the figure below reflects the legacies of smoking for older cohorts, followed by a period of green with improvements in cancer mortality as smoking rates declined. But the white/gray shading in the bottom right corner highlights a stagnation and reversal of the cancer trends, with cohorts born around 1970 to 1985 experiencing worsening cancer mortality. This deterioration was most noticeable for colon cancer mortality, possibly linked to obesity or dietary factors. Not just dying more, but less healthy along the way. A recent paper by Dr. Laura Gimeno of University College London (of which I am a co-author), found evidence that more recently born cohorts in Great Britain have stagnating or worsening health. A stylized picture of what“generational health drift” might look like: The review paper found: Rates of childhood overweight and obesity were highest among Gen Z. Rates of diabetes in the 40s almost doubled from 3.1% among the Baby Boomers to 5.9% in Generation X. Gen Z reported higher rates of poor mental health in adolescence compared to earlier-born groups. These UK results are consistent with studies showing worsening health across generations in the US as well. A 2021 US study found that more recently born cohorts had worse biological markers (like blood pressure and blood sugar) than earlier generations at the same age. A separate 2022 study of Americans aged 50 and over found that more recently born generations had a higher number of chronic diseases, with younger age of disease onset. A 2024 Lancet paper found that in the US, women born in 1945 had 33% obesity prevalence at age 50, rising to 48.2% for women born in 1970 turning 50. So what’s going on, and can we turn it around? We don’t have a definitive answer for what’s causing the deteriorating health of younger cohorts, but circumstantial evidence points to worsening trends in obesity and diabetes in both the US and UK. Remember that cohort trends reflect lagged effects of early environments, and younger generations have faced both earlier onset and more severe obesity across their lifetimes. Despite these very concerning trends, the future is not set in stone. We’ve reversed poor generational health trends before: drops in smoking are a major reason cancer and heart disease deaths fell so dramatically in recent decades. GLP-1 drugs may finally turn the tide against rising rates of obesity and diabetes, though it is still early days. But things could also get worse…for example from a return to higher levels of childhood infectious diseases that can have a lasting impact on health. See for example: [ ](https://jenndowd.substack.com/p/dont-you-forget-about-measles-the) [ Don’t You Forget About Me(asles): The Virus That Erases Immune Memory ](https://jenndowd.substack.com/p/dont-you-forget-about-measles-the) Jenn Dowd, PhD · March 24, 2025 [ Read full story ](https://jenndowd.substack.com/p/dont-you-forget-about-measles-the) Even as an optimist, emerging data on the “generational health drift” has my full attention. For most of my research career, we took it for granted that each generation would be healthier than the last. But if present trends continue, **50 could be the new 60….**and Gen Z celebrities will soon look older than Paul Rudd :). Stay well, Jenn Share the Health! If you found this post useful, please share it with a friend. Share
Braves Starter Martin Perez Gives Statement After Winning NL East
For the first time since the 2023 season, the Atlanta Braves have won the National League East division. Braves left-hander Martin Perez spoke to Wiley Ballard of BravesVision about the achievement while getting sprayed with champagne by teammates.“This is why you work so long, all year. I think we’ve been riding this together, and now […] The post Braves Starter Martin Perez Gives Statement After Winning NL East appeared first on HEAVY.
The Big Holes in Wearable Heart Rate Variability And Readiness Scores
On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!
Jonathan M. Pratt Appointed as CEO of Qiagen
The largest repository of validated, free and subject-focused analytical science resources covering from industry news to original research. Search today >>
The Literacy Crisis Reaches Dog-Bear Science
“New analysis of more than 300 bear encounters suggests our canine companions often increase odds of conflict and rarely serve as early warning systems many recreationists assume,” reads a subhead in Mountain Journal. The thing is, that study reaches no such conclusion. But you have to read beyond the abstract to understand that. Mountain Journal is not alone. Let’s look at a brief list of takeaways published elsewhere just to establish that none of them seem to know how to read science. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Here’s CBC: “A new peer-reviewed study, The Canine Conundrum: Is a Dog a Help or Hindrance in Bear Country?, dug into conflicts similar to what the video depicts, by analyzing 326 reported human-bear conflicts from 1901 to 2023 involving dogs.” “The study found 54 per cent of conflicts were started by dogs. In the encounters researchers included in the study, five people and 56 dogs were killed, and 170 people were injured.” “Lana Ciarniello, an independent researcher who co-authored the study, said researchers found when a person goes hiking with at least one other person, the odds of a negative bear encounter decrease by 50 per cent.” “However, when a dog is involved, the chances of a negative bear encounter increase because ‘a dog really adds a chaotic factor into the mix of recreation,’ said Ciarniello.” Again, the study finds no data supporting that conclusion. “Hiking with your pet dog may make you feel safer in bear country—but a hiker’s recent encounter and a new study prove that the numbers don’t add up,” writes Backpacker, which is now part of Outside. The numbers don’t add up to support this take. There’s plenty more examples, but all are equally dumb, so I’ll stop there. How did so many people get this so wrong? Three reasons: 1) the study is published behind a paywall, with only the abstract freely available. 2) the methodology sets out to create work relevant to a hyper-specific audience, not the public. And 3) the dataset studied is almost absurdly limited and doesn’t bear (get it?!) comparison to wider information. Let’s work through all three. The Problem is Money I understand the irony, since I’m writing about this on a subscription-model newsletter, but information wants to be free. The study is published by The Wildlife Society and costs $16 to access. Reprinting it is specifically prohibited by the terms of use, so I can only share snippets and my conclusions with you here. Having worked as a journalist for 24 years, I can report with some authority that journalists are both lazy and cheap. I don’t think it’s a stretch to say that much of the material you’re seeing written about this study is drawn from the freely-available abstract (above) only, and not a deep dive into its methodology or data. And that’s a problem, because abstracts are where researchers are able to break from their numbers and editorialize their opinions about conclusions. So what’s getting reported isn’t based on numbers, it’s based on an opinion designed for a specific audience, not the general public. Hell yeah ladies, kick that bear’s ass. The Methodology is Designed to Support a Predetermined Conclusion One of the biggest issues with the science that everyone likes to cite about bear spray’s efficacy is that no one understands that study was designed for a specific audience, looking for a specific answer. And since that audience is not the public, and our safety was not the question being asked, its conclusion is rendered irrelevant to our needs. Yes, I’m talking about Efficacy of Bear Deterrent Spray in Alaska, which was created by Tom Smith and Stephen Herrero, two of the same scientists who put together this dog-bear conflict analysis. In the spray study, the purpose was to present relevant agencies with the information they needed to issue their field workers with bear spray. As such, incidents where people were unable to access the spray or similarly failed to get it deployed in time were excluded from the results, and only incidents they deemed successful were included. Could a ranger roll down their vehicle’s window and scare a bear away from a dumpster by spraying it? Turns out the answer was yes, and that was good enough for the purposes of that study. The sample size of actual bear attacks—the thing you and I would want bear spray to provide a solution to, was too small to draw conclusions from, and didn’t indicate success in that role. Is a Dog a Help or Hindrance in Bear Country? has similar limitations. “Insights provided here are intended to help wildlife managers create effective guidelines regarding the presence of domestic dogs in bear habitat,” is explained clearly by the study’s text. To conduct the study, Smith and Herrero went looking for publicly available reports of dog-bear conflicts: “All incidents included in our analysis involved at least one bear, one dog, and one person who was at immediate risk of injury (e.g., not in a protective structure such as a cabin or vehicle), injured, or killed,” explains the report’s methodology. Why is that a problem? “We recognize that data used in this study are biased in that people generally only report adverse interactions with bears, not benign ones. Consequently, we assume that for the most part, people and their dogs pass through bear country without incident.” As was the problem with the bear spray study, people are drawing conclusions here from a study that was never intended to be relevant to the general public. The Smith-Herrero database famously excludes Alaska’s Defense of Life and Property records, which are elsewhere considered the largest and most authoritative source on human-bear conflict. The Dataset is Absurdly Limited A 2009 study conducted by the Washington Department of Fish & Wildlife estimates that each year there are at least 43,000 documented complaints of human-bear conflict across North America. Is a Dog a Help or Hindrance in Bear Country? Examined those reports for years ranging from 1901 to 2023, and only managed to find 326 such incidents that met the criteria explained above. Let’s do some quick math, poorly. 43,000 incidents x 122 years = 5,246,000 total incidents of documented human-bear conflict. Smith and Herrero were only able to find 326 incidents that met their criteria for dog+human+bear+risk or occurrence of injury. That means there’s a 0.006 percent chance that the study’s conclusion—that dogs make bear encounters more dangerous—even gets a chance of occurring. And remember, that’s only in documented conflicts. There’s no way to know how many people across North America run into a bear in any given year without any conflict, but it’s probably safe to assume that the total number is well in excess of 43,000. And, dig deeper into the study and you can find further restrictions that skew its conclusions even more. “While the people involved in these incidents did not report the level of training their dogs had received, it is our assumption that most were untrained companion dogs as opposed to well-trained bear dogs, such as Karelians and livestock guardian breeds that have proven to be significant aids in bear country,” the study’s methodology explains. And that’s a problem, because an ongoing study here in Montana is finding that the simple presence of a Kangal like our youngest dog Teddy can reduce visits to private property from grizzly bears by 87.8 percent. In addition to being a badass, Teddy is also a companion animal. And in my admittedly limited sample size of the three dogs currently present in our living room, she’s no more effective than her mutt brothers. Plenty of people own a dog capable of deterring bears, even if they didn’t set out to own a bear dog. The real purpose of this study appears to be a recommendation that leashes are mandated and recreationists pursue training in their deterrent of choice. But that doesn’t make for sensational headlines. Are There Any Real Takeaways Here? Let’s throw some numbers around, just to try and get our hands around what the larger picture might look like for bears and dogs here. The American Veterinary Medical Association estimates that 42.6 percent of American households own a dog. The Canadian Animal Health Institute says roughly 35 percent of households in Canuckistan own a dog. In an attempt to create the worst possible result for dogs, let’s run with that 35 percent number. And given that outdoorsy and rural people are more likely to both own a dog, and have bear encounters, let’s just make a giant assumption and apply that number to those 43,000 conflicts documented annually. If 35 percent of 43,000 human-bear encounters involve a dog, then we can assume that at least 15,050 dog-bear conflicts are occurring annually in North America. 15,050 x 122 = 1,836,100. Smith and Herrero were only able to find 326 incidents that met their human, dog, chance or occurrence of injury criteria across 122 years. And only 170 documented human injuries. So, with all the obvious caveats about these numbers in no way describing reality beyond narrow study parameters, what they actually found is that your risk of injury if your dog tangles with a bear is only 0.009 percent. I’ll take those odds next time I’m hiking my dogs in bear country. Top photo: “Superintendent Horace Albright, eating pancakes with bears, at Lake Lodge, Yellowstone National Park.” NPS. A journalist with more than two decades of experience working around the world, Wes Siler is here to cut through the outrage and disinformation to bring you the factual, insightful, actionable reporting you need to understand what’s going on. Upgrading to a paid subscription supports this reporting, and buys personal access to Wes, who will help you save money on gear, plan outdoor adventures, and prepare for real life, and who promises he’s less salty in real life than he sometimes comes across as on the Internet. Wes Siler’s Newsletter is a reader-supported publication. 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August 10 "Colorado Chamber Office Hours": Secretary of State candidates talk business issues
Colorado Secretary of State candidates James Wiley and Amanda Gonzalez talk business issues on the "Colorado Chamber Office Hours" podcast.
Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
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