With about one month left on the existing 90-day Public Health Emergency that’s eased regulations and improved reimbursement to help make telehealth, remote monitoring, and other virtual care services easier for providers to implement and patients to use, health tech companies across the US are wondering what it will take to make these changes permanent. One of digital health’s few ‘DC Insiders,’ Livongo Health’s VP of Government Affairs, Leslie Krigstein, gets us up-to-speed on what’s happening on Capitol Hill and what we can expect moving forward. What changes will (literally) require an Act of Congress? And what can be handled by HHS and CMS? From codes and co-pays to e-visits and licensing, Leslie breaks it down and tells us whether or not we can continue to expect a ‘health tech-friendly’ agenda in Washington DC.
I knew about TikTok, but not “TikTok Teens.” I was vaguely aware of K-Pop, but I didn’t know its fans had common interests beyond, you know, K-Pop. I’d been tracking Gen X and Millennials but hadn’t really focused on Gen Z. It turns out that these overlapping groups are quite socially aware and are starting to make their influence felt.
I can’t wait for them to pay more attention to health care.
This is the generation that has grown up during/in the wake of 9/11, the War on Terror, the War on Drugs, the 2008 recession, the coronavirus pandemic, and the current recession — not to mention smartphones, social media, online shopping, and streaming. Greta Thunberg is Gen Z, as is Billie Eilish, each of whom is leading their own social movements. This generation has a lot to protest about, and a lot of ways to do it.
They were in the news this past weekend due to, of all things, President Trump’s Tulsa rally. His campaign had boasted about having a million people sign up for the rally, only to find that the arena was less than a third filled. An outdoor rally for the expected overflow crowd was cancelled.
It didn’t take long for the TikTok Teens/K-Pop fans to boast on social media about their covert — to us older folks — campaign to register for the rally as a way to gum up the campaign efforts. Steve Schmidt, an anti-Trump Republican strategist, tweeted: “The teens of America have struck a savage blow against @realDonaldTrump.”
In the wake of the protests related to George Floyd’s death, there have been many calls to “defund police.” Those words come as a shock to many people, some of whom can’t imagine even reducing police budgets, much less abolishing entire police departments, as a few advocates do indeed call for.
If we’re talking about institutions that are supposed to protect us but too often cause us harm, maybe we should be talking about defunding health care as well.
America loves the police. They’re like mom and apple pie; not supporting them is essentially seen as being unpatriotic. Until recent events, it’s been political suicide to try to attack police budgets. It’s much easier for politicians to urge more police, with more hardware, even military grade, while searching for budget cuts that will attract less attention.
It remains to be seen whether the current climate will actually lead to action, but there are faint signs of change. The mayor of Los Angeles has promised to cut $150 million from its police budget, the New York City mayor vowed to cut some of its $6b police budget, and the Minneapolis City Council voted to “begin the process of ending the Minneapolis Police Department,” perhaps spurred by seeing the mayor do a “walk of shame” of jeers from protesters when he would not agree to even defunding it.
Something didn’t seem right to epidemiologist Eric Weinhandl when he glanced at an article published in the venerated Journal of the American Medical Association (JAMA) on a crisp fall evening in Minnesota. Eric is a smart guy – a native Minnesotan and a math major who fell in love with clinical quantitative database-driven research because he happened to work with a nephrologist early in his training. After finishing his doctorate in epidemiology, he cut his teeth working with the Chronic Disease Research Group, a division of the Hennepin Healthcare Research Institute that has held The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) contract for the United States Renal Data System Coordinating Center. The research group Eric worked for from 2004-2015 essentially organized the data generated from almost every dialysis patient in the United States. He didn’t just work with the data as an end-user, he helped maintain the largest, and most important database on chronic kidney disease in the United States.
For all these reasons this particular study published in JAMA that sought to examine the association between dialysis facility ownership and access to kidney transplantation piqued Eric’s interest. The provocative hypothesis is that for-profit dialysis centers are financially motivated to keep patients hooked to dialysis machines rather than refer them for kidney transplantation. A number of observational trials have tracked better outcomes in not-for-profit settings, so the theory wasn’t implausible, but mulling over the results more carefully, Eric noticed how large the effect sizes reported in the paper were. Specifically, the hazard ratios for for-profit vs. non-profit were 0.36 for being put on a waiting list, 0.5 for receiving a living donor kidney transplant, 0.44 for receiving a deceased donor kidney transplant. This roughly translates to patients being one-half to one-third as likely to get referred for and ultimately receiving a transplant. These are incredible numbers when you consider it can be major news when a study reports a hazard ratio of 0.9. Part of the reason one doesn’t usually see hazard ratios that are this large is because that signals an effect size that’s so obvious to the naked eye that it doesn’t require a trial. There’s a reason there are no trials on the utility of cauterizing an artery to stop bleeding during surgery.
But it really wasn’t the hazard ratios that first struck his eye. What stuck out were the reported event rates in the study. 1.9 million incident end-stage kidney disease patients in 17 years made sense. The exclusion of 90,000 patients who were wait-listed or received a kidney transplant before ever getting on dialysis, and 250,000 patients for not having any dialysis facility information left ~1.5 million patients for the primary analysis. The original paper listed 121,000 first wait-list events, 23,000 living donor transplants and ~50,000 deceased donor transplants. But the United Network for Organ Sharing (UNOS), an organization that manages the US organ transplantation system, reported 280,000 transplants during the same period.
The paper somehow was missing almost 210,000 transplants.
I am writing this blog post (the first after nearly two years!) in lockdown mode because of the rapidly spreading SARSCoV2 virus, the causative agent of the COVID19 disease (a poor choice of a name, since the disease itself is really SARS on steroids).
One interesting feature of this disease is that a large number of patients will manifest minimal or no symptoms (“asymptomatic” infections), a state which must clearly be distinguished from the presymptomatic phase of the infection. In the latter, many patients who will eventually go on to develop the more serious forms of the disease have minimal symptoms. This is contrast to asymptomatic patients who will never develop anything more bothersome than mild symptoms (“sniffles”), for which they will never seek medical attention. Ever since the early phases of the COVID19 pandemic, a prominent narrative postulated that asymptomatic infections are much more common than symptomatic ones. Therefore, calculations such as the Case Fatality Rate (CFR = deaths over all symptomatic cases) mislead about the Infection Fatality Rate (IFR = deaths over all cases). Subthreads of this narrative go on to postulate that the lockdowns which have been implemented widely around the world are overkill because COVID19 is no more lethal than the flu, when lethality is calculated over ALL infections.
Whereas the politicization of the lockdown argument is of no interest to the author of this blog (after all the virus does not care whether its victim is rich or poor, white or non-white, Westerner or Asian), estimating the prevalence of individuals who were exposed to the virus but never developed symptoms is important for public health, epidemiological and medical care reasons. Since these patients do not seek medical evaluation, they will not detected by acute care tests (viral loads in PCR based assays). However such patients, may be detected after the fact by looking for evidence of past infection, in the form of circulating antibodies in the patients’ serum. I was thus very excited to read about the release of a preprint describing a seroprevalence study in Santa Clara County, California. This preprint described the results of a cross-sectional examination of the residents in the county in Santa Clara, with a lateral flow immunoassay (similar to a home pregnancy kit) for the presence of antibodies against the SARSCoV2 virus. The presence of antibodies signifies that the patient was not only exposed at some point to the virus, but this exposure led to an actual infection to which the immune system responded by forming antibodies. These resulting antibodies persist for far longer than the actual infection and thus provide an indirect record of who was infected. More importantly, such antibodies may be the only way to detect asymptomatic infections, because these patients will not manifest any symptoms that will make them seek medical attention, when they were actively infected. Hence, the premise of the Santa Clara study is a solid one and in fact we need many more of these studies. But did the study actually deliver? Let’s take a deep dive into the preprint.
Since the World Health Organization (WHO) officially declared COVID-19 a pandemic on March 11, 2020, we have been changing our daily lives to protect the highest-risk populations: older adults and people with chronic medical conditions. We are asked to follow sensible guidelines like social distancing and thorough hand-washing. Although one may have a gut-reaction to put their own safety at the forefront during these times of crisis, it is essential that we are taking the necessary steps to protect populations with additional vulnerabilities – rural tribal communities.
With the announcement that COVID-19 reached the Confederated Tribes of Umatilla Indian Confederation on March 9, 2020, it was evident the virus would not stay confined to urban and metropolitan centers like some previously predicted. The experience in China with COVID-19 clearly reflects the vulnerability of rural communities because many people travel routinely from urban to rural. Experts who conducted an epidemiological study in Hubei province, the initial epicenter of the COVID-19 pandemic, noted in their report: “…most public medical resources are concentrated in cities but are relatively scarce in rural areas. Therefore, prevention and treatment of 2019-nCoV in rural areas will be more challenging if new phases of the epidemic emerge.”
What do the coronavirus
and Navy ships have in common? For that matter, what do our military
spending and our healthcare spending have in common? More than you might
think, and it boils down to this: we spend too much for too little, in large
part because we tend to always be fighting the wrong wars.
I started thinking about this a couple weeks ago due to a WSJ article about the U.S. Navy’s “aging and fragmented technology.” An internal Navy strategy memo warned that the Navy is “under cyber siege” by foreign adversaries, leaking information “like a sieve.” It grimly pointed out:
adversaries gain an advantage in cyberspace through guerrilla tactics within
our defensive perimeters. Once inside, malign actors steal, destroy
and/or modify critical data and information.
I recently took care of Rosaria, a cheerful 60-year-old woman who came in for chronic joint pain. She grew up in rural Mexico, but came to the US thirty years ago to work in the strawberry fields of California. After examining her, I recommended a few blood tests and x-rays as next steps. “Lo siento pero no voy a tener seguro hasta el primavera — Sorry but I won’t have insurance again until the Spring.” Rosaria, who is a seasonal farmworker, told me she only gets access to health care during the strawberry season. Her medical care will have to wait, and in the meantime, her joints continue to deteriorate.
Migrant and seasonal agricultural workers (MSAW) are people who work “temporarily or seasonally in farm fields, orchards, canneries, plant nurseries, fish/seafood packing plants, and more.” MSAW are more than temporary laborers, though— they are individuals and families who have time and time again helped the US in its greatest time of need. During WWI, Congress passed the Immigration and Nationality Act of 1917 because of the extreme shortage of US workers. This allowed farmers to bring about 73,000 Mexican workers into the US. During WWII, the US once again called upon Mexican laborers to fill the vacancies in the US workforce under the Bracero Program in 1943. Over the 23 years the Bracero Program was in place, the US employed 4.6 million Mexican laborers. Despite the US being indebted to the Mexican laborers, who helped the economy from collapsing in the gravest of times, the US deported 400,000 Mexican immigrants and Mexican-American citizens during the Great Depression.
“By the way, Doc, why am I tired, what’s this lump and how do I get rid of my headaches?”
Every patient encounter is a potential deadly disease, disastrous outcome, or even a malpractice suit. As clinicians, we need to have our wits about us as we continually are asked to sort the wheat from the chaff when patients unload their concerns, big and small, on us during our fifteen minute visits.
But something is keeping us from listening to our patients with our full attention, and that something, in my opinion, is not doctor work but nurse work or even tasks for unlicensed staff: Our Public Health to-do list is choking us.
You don’t need a medical degree to encourage people to get flu and tetanus shots, Pap smears, breast, colon and lung cancer screening, to quit smoking, see their eye doctor or get some more blood pressure readings before your next appointment. But those are the pillars of individual medical providers’ performance ratings these days. We must admit that the only way you can get all that health maintenance done is through a team effort. Medical providers neither hire nor supervise their support staff, so where did the idea ever come from that this was an appropriate individual clinician performance measure?
When Samuel Morse left his New Haven home to paint a portrait of the
Maquis du Lafayette in Washington DC, it was the last time he would see his
pregnant wife. Shortly after his arrival in Washington, his wife developed
complications during childbirth. A messenger took several days on horseback to
relay the message to Mr Morse. Because the trip back to New Haven took several
more, his wife had died by the time he arrived at their home. So moved was he by the tragedy of lost time
that he dedicated the majority of the rest of his life to make sure that this
would never happen to anyone again. His subsequent work on the telegraph and in
particular the mechanism of communication for the telegraph resulted in Morse
code – the first instantaneous messaging system in the world.
Mr Morse’s pain is not foreign to us in the 21st century. We feel the loss of new mothers so deeply that, when earlier this year new statistics on the rate of maternal death were released and suggested that American women died at three times the rate of other developed countries during child birth, doctors, patient advocates, and even Congress seemed willing to move heaven and earth to fix the problem. As someone who cares for expectant mothers at high risk for cardiovascular complications, I too was moved. But beyond the certainty of the headlines lay the nuance of the data, which seemed to tell a murkier story.
First at issue was the presentation of the data. Certainly, as a rate
per live births, it would seem that the United States lagged behind other OECD
countries – our maternal mortality rate was between 17.2 and 26.4 deaths per
100,000 live births, compared to 6.6 in the UK or 3.7 in Spain. But this
translated to approximately 700 maternal deaths per year across the United
States (among approximately 2.7 million annual births). While we would all agree
that one avoidable maternal death is one too many, the low incidence means that
small rates of error could have weighty implications on the reported results.
For instance, an error rate of 0.01% would put the United States in line with
other developed countries.
Surely, the error rate could not account for half the reported
deaths, right? Unfortunately, it is difficult to estimate how close to reality
the CDC reported data is, primarily because the main source data for maternal
mortality is a single question asked on the application for death certificates.
The question asks whether the deceased was pregnant at the time of death,
within 42 days of death, or in the 43 to 365 days prior to death. While
pregnancy at the time of death may be easy to assess, the latter two categories
are subject to significantly more error.