DAILY REMEDY: THE GOVERNMENTS CAMPAIGN OF TERROR ON PHYSICIAN/PHARMACIST: A DISCUSSION WITH DEFENSE LAWYER RON CHAPMAN II

DAILY REMEDY: THE GOVERNMENTS CAMPAIGN OF TERROR ON PHYSICIAN/PHARMACIST: A DISCUSSION WITH DEFENSE LAWYER RON CHAPMAN II

https://youarewithinthenorms.com/2021/12/27/daily-remedy-the-new-prison-physician-pharmacist-industrial-complex-and-how-it-threatens-all-the-delivery-of-all-care-a-discussion-with-defense-lawyer-ron-chapman-ii/

DONATE LEGAL DEFENSE

REPORTED BY

NORMAN J CLEMENT RPH., DDS, NORMAN L.CLEMENT PHARM-TECH, MALACHI F. MACKANDAL PHARMD, BELINDA BROWN-PARKER, IN THE SPIRIT OF JOSEPH SOLVO ESQ., IN THE SPIRIT OF REV. C.T. VIVIAN, JELANI ZIMBABWE CLEMENT, BS., MBA., IN THE SPIRIT OF THE HON. PATRICE LUMUMBA, IN THE SPIRIT OF ERLIN CLEMENT SR., WALTER F. WRENN III., MD., JULIE KILLINGWORTH, WILLIE GUINYARD BS., JOSEPH WEBSTER MD., MBA, BEVERLY C. PRINCE MD., FACS., RICHARD KAUL, MD., LEROY BAYLOR, JAY K. JOSHI MD., MBA, ADRIENNE EDMUNDSON, ESTER HYATT PH.D., WALTER L. SMITH BS., IN THE SPIRIT OF BRAHM FISHER ESQ., MICHELE ALEXANDER MD., CUDJOE WILDING BS, MARTIN NDJOU, BS., RPH., IN THE SPIRIT OF DEBRA LYNN SHEPHERD, BERES E. MUSCHETT, STRATEGIC ADVISORS

From the Prophets Hosea and Norm we learn:

“My people are destroyed

from the lack of knowledge

Because you have rejected knowledge,

I will also reject your” 

“ignorance which can support irresponsibility and irresponsibility is not forgiven”

See Action Against Pain Physicians Link below

https://www.aapsonline.org/painman/actionsagainst.htm

“THIS NEW LEGAL INDUSTRY OF TERROR”

FROM THE DAILY REMEDY 12/24/2021:

Mr. Ron Chapman II began his career as an officer in the U.S. Marine Corps and was deployed to combat in Afghanistan. He achieved the rank of Captain before transitioning to law.

He graduated from Loyola School of Law, where he received his Master of Laws (LL.M.) with a concentration in health care compliance.

BY RON CHAPMAN ESQ

SINCE WHEN DOES LAW ENFORCEMENT DICTATE MEDICAL PROCEDURES AND ESTABLISH MEDICAL PROTOCOLS?

He quickly established himself as a premier healthcare defense counsel and is among the few attorneys to have achieved trial acquittals on behalf of clients charged with healthcare financial fraud or improper opioid prescribing.

He now shares his wisdom and learned experiences in the book – Fight the Feds: Unraveling Federal Criminal Investigations. To purchase his book, please select the link below:

A PODCAST
WAR AGAINST PATIENTS IN PAIN

From Brief of Amici Curiae Professors of Health Law and Policy in Support of Petitioner, Ruan v. the United States, Befoe the United States of America Supreme Court Case No. 20-1410 (May 7, 2021):

EXCERPTS

“Prosecutorial and judicial statutory reconstruction to more easily convict practitioners is not the cure for drug-related morbidity and mortality. See Centers for Disease Control and Prevention, National Center for Health Statistics, Drug Overdose Deaths in the U.S. Top 100,000 Annually (Nov. 17, 2021), https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2021/20211117.htm (reporting an almost 30% increase in and a record-high number of overdose deaths between April 2020 and 2021).

As we previously explained, and as the petitioners have described in the consolidated cases here, the Tenth and Eleventh Circuits have effectively eliminated Section 841(a)(1)’s mens rea requirements as applied to prescribers. 

While the government must prove intentional or knowing distribution of controlled substances for Non-prescribers under Tenth and Eleventh Circuit precedent, the government may convict an authorized prescriber of felony distribution without proof that they had any knowledge of “all the facts that make”

FOR NOW, YOU ARE WITHIN

BENJAMIN CLEMENTINE “THE NEMESIS” 2015 LONDON ENGLAND

THE NORMS

Thank You, I ask you to donate to the Pharmacist For Healthcare Legal  Defense Fund, fight the DEA attack on me & Pronto Pharmacy now Gulf Med Pharmacy our goal 100k, Appeal Court 1st Dist Wash DC. 
Click to Donate:  http://gf.me/u/2qffp4 #GoFundMe or cash app: $docnorm 
or to Zelle: 3135103378

DONATE LEGAL DEFENSE

Is this just another pt being falsely labeled with a HIGH NARXCARE SCORE ?

Good Evening Pharmacist Steve,

I am a middle-aged  educator from the east coast.  I first would like to say that I appreciate the advocacy and work you do.  For quite some time, I have followed your posts and have learned a lot about how crazy (to say the least) the world of medicine, specifically pain/controlled medication has become.  Sadly, as you pointed out on numerous occasions, it shouldn’t be this way.

I can guarantee that you have heard almost every scenario know to man, so this one may not be any different but I will give it a shot:

During an appointment with my psychiatrist, we discussed my ADHD, anxiety, Sleep Apnea, and Narcolepsy.  The Psychiatrist knows I am prescribed a controlled med by my Pulmonologist for the Sleep Apnea/Narcolepsy and my Pulmonologist knows I am prescribed controlled med for my ADHD.  Out of nowhere, my Psychiatrist berated the hell out of me for being on two low dosage controlled medications that both medical professionals know about.  The Psychiatrist went on to say that I have been red-flagged by the DEA.  Where the encounter gets interesting is that the Psychiatrist, is that right after this verbal onslaught, he sent my prescription to the pharmacy and said “Happy New Year, see you in 4-weeks”. 

  1. Why would he go-off on me, stating the DEA has flagged me and then submit the prescription for the controlled med to the pharmacy? 

                                                      

  1. I did ask him if I could have documentation and the reasoning I was flagged by the DEA, and he said, “Because its two controlled meds that’s your documentation”.  I insisted that he provide me with a law and the documentation showing that I am flagged and the reasoning, which his response was, “I will get back to you with that”.  Pretty much, he thought I was unknowledgeable of the law and other such things that he has no answer because this was over a week ago now and I haven’t been given any documentation as to what I requested.

I am no expert of the law, but I have seen what I will call a “real Psychiatrist” before (unfortunately he retired a few years ago) and know that the “legal limits” these doctors say when it comes to ADHD/ADD meds are not written into law.  Hell, I was prescribed a much better regerminate by the real Psychiatrist that worked but sadly no one is willing to follow what worked for me.

Anyway, if you have time to dissect this and/or have any insight, I would greatly appreciate it. 

This pt seems to be yet another pt snagged in the NARXCARE  OVERDOSE RISK SCORE that I made a post about earlier today.

The Pain Was Unbearable. So Why Did Doctors Turn Her Away?

 I would guess that a pt being prescribed a controlled med(s) from two different prescribers could be a AUTOMATIC RED FLAG…  According to those arbitrary rules that the DEA have developed from their observations of what substance abusers and diverters do…  and it would seem that – from the DEA’s logic/perspective – if abusers do this… then anyone doing the same/similar things – must be a substance abuser or diverter.

Narxcare states that they use (AI) artificial intelligence to come to these Overdose Risk Scores.. it is too bad that they don’t use some REAL INTELLIGENCE to derive these SCORES not not one just based on some numbers and PRESUMPTIONS

The Pain Was Unbearable. So Why Did Doctors Turn Her Away?

A sweeping drug addiction risk algorithm has become central to how the US handles the opioid crisis. It may only be making the crisis worse.

Appriss’ flagship product for doctors, pharmacies, and hospitals: an “analytics tool and care management platform” that purports to instantly and automatically identify a patient’s risk of misusing opioids.

NarxCare also offers states access to a complex machine-learning product that automatically assigns each patient a unique, comprehensive Overdose Risk Score. Only Appriss knows exactly how this score is derived, but according to the company’s promotional material, its predictive model not only draws from state drug registry data, but “may include medical claims data, electronic health records, EMS data, and criminal justice data.”

https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/

One evening in July of 2020, a woman named Kathryn went to the hospital in excruciating pain.

A 32-year-old psychology grad student in Michigan, Kathryn lived with endometriosis, an agonizing condition that causes uterine-like cells to abnormally develop in the wrong places. Menstruation prompts these growths to shed—and, often, painfully cramp and scar, sometimes leading internal organs to adhere to one another—before the whole cycle starts again.

For years, Kathryn had been managing her condition in part by taking oral opioids like Percocet when she needed them for pain. But endometriosis is progressive: Having once been rushed into emergency surgery to remove a life-threatening growth on her ovary, Kathryn now feared something just as dangerous was happening, given how badly she hurt.

In the hospital, doctors performed an ultrasound to rule out some worst-case scenarios, then admitted Kathryn for observation to monitor whether her ovary was starting to develop another cyst. In the meantime, they said, they would provide her with intravenous opioid medication until the crisis passed.

On her fourth day in the hospital, however, something changed. A staffer brusquely informed Kathryn that she would no longer be receiving any kind of opioid. “I don’t think you are aware of how high some scores are in your chart,” the woman said. “Considering the prescriptions you’re on, it’s quite obvious that you need help that is not pain-related.”

Kathryn, who spoke to WIRED on condition that we use only her middle name to protect her privacy, was bewildered. What kind of help was the woman referring to? Which prescriptions, exactly? Before she could grasp what was happening, she was summarily discharged from the hospital, still very much in pain.

Back at home, about two weeks later, Kathryn received a letter from her gynecologist’s office stating that her doctor was “terminating” their relationship. Once again, she was mystified. But this message at least offered some explanation: It said she was being cut off because of “a report from the NarxCare database.”

Like most people, Kathryn had never heard of NarxCare, so she looked it up—and discovered a set of databases and algorithms that have come to play an increasingly central role in the United States’ response to its overdose crisis.

Over the past two decades, the US Department of Justice has poured hundreds of millions of dollars into developing and maintaining state-level prescription drug databases—electronic registries that track scripts for certain controlled substances in real time, giving authorities a set of eyes onto the pharmaceutical market. Every US state, save one, now has one of these prescription drug monitoring programs, or PDMPs. And the last holdout, Missouri, is just about to join the rest.

In the past few years, through a series of acquisitions and government contracts, a single company called Appriss has come to dominate the management of these state prescription databases. While the registries themselves are somewhat balkanized—each one governed by its own quirks, requirements, and parameters—Appriss has helped to make them interoperable, merging them into something like a seamless, national prescription drug registry. It has also gone well beyond merely collecting and retrieving records, developing machine-learning algorithms to generate “data insights” and indicating that it taps into huge reservoirs of data outside state drug registries to arrive at them.

NarxCare—the system that inspired Kathryn’s gynecologist to part ways with her—is Appriss’ flagship product for doctors, pharmacies, and hospitals: an “analytics tool and care management platform” that purports to instantly and automatically identify a patient’s risk of misusing opioids.

On the most basic level, when a doctor queries NarxCare about someone like Kathryn, the software mines state registries for red flags indicating that she has engaged in “drug shopping” behavior: It notes the number of pharmacies a patient has visited, the distances she’s traveled to receive health care, and the combinations of prescriptions she receives. 

Beyond that, things get a little mysterious. NarxCare also offers states access to a complex machine-learning product that automatically assigns each patient a unique, comprehensive Overdose Risk Score. Only Appriss knows exactly how this score is derived, but according to the company’s promotional material, its predictive model not only draws from state drug registry data, but “may include medical claims data, electronic health records, EMS data, and criminal justice data.” At least eight states, including Texas, Florida, Ohio, and Michigan—where Kathryn lives—have signed up to incorporate this algorithm into their monitoring programs.

For all the seeming complexity of these inputs, what doctors see on their screen when they call up a patient’s NarxCare report is very simple: a bunch of data visualizations that describe the person’s prescription history, topped by a handful of three-digit scores that neatly purport to sum up the patient’s risk.

Appriss is adamant that a NarxCare score is not meant to supplant a doctor’s diagnosis. But physicians ignore these numbers at their peril. Nearly every state now uses Appriss software to manage its prescription drug monitoring programs, and most legally require physicians and pharmacists to consult them when prescribing controlled substances, on penalty of losing their license. In some states, police and federal law enforcement officers can also access this highly sensitive medical information—in many cases without a warrant—to prosecute both doctors and patients.

In essence, Kathryn found, nearly all Americans have the equivalent of a secret credit score that rates the risk of prescribing controlled substances to them. And doctors have authorities looking over their shoulders as they weigh their own responses to those scores.

Even after Kathryn had read up on NarxCare, however, she was still left with a basic question: Why had she been flagged with such a high score? She wasn’t “doctor shopping.” The only other physician she saw was her psychiatrist. She did have a prescription for a benzodiazepine to treat post-traumatic stress disorder, and combining such drugs with opioids is a known risk factor for overdose. But could that really have been enough to get her kicked out of a medical practice?

As Kathryn continued her research online, she found that there was a whole world of chronic pain patients on Twitter and other forums comparing notes on how they’d run afoul of NarxCare or other screening tools. And eventually she came upon an explanation that helped her understand what might have gone wrong: She had sick pets.

At the time of her hospitalization, Kathryn owned two flat-coated retrievers, Bear and Moose. Both were the kind of dog she preferred to adopt: older rescues with significant medical problems that other prospective owners might avoid. Moose had epilepsy and had required surgery on both his hind legs. He had also been abused as a puppy and had severe anxiety. Bear, too, suffered from anxiety.

The two canines had been prescribed opioids, benzodiazepines, and even barbiturates by their veterinarians. Prescriptions for animals are put under their owner’s name. So to NarxCare, it apparently looked like Kathryn was seeing many doctors for different drugs, some at extremely high dosages. (Dogs can require large amounts of benzodiazepines due to metabolic factors.) Appriss says that it is “very rare” for pets’ prescriptions to drive up a patient’s NarxCare scores.

As Kafkaesque as this problem might seem, critics say it’s hardly an isolated glitch. A growing number of researchers believe that NarxCare and other screening tools like it are profoundly flawed. According to one study, 20 percent of the patients who are most likely to be flagged as doctor-shoppers actually have cancer, which often requires seeing multiple specialists. And many of the official red flags that increase a person’s risk scores are simply attributes of the most vulnerable and medically complex patients, sometimes causing those groups to be denied opioid pain treatment. 

The AI that generates NarxCare’s Overdose Risk Score is, to many critics, even more unsettling. At a time of mounting concern over predictive algorithms, Appriss’ own descriptions of NarxCare—which boast of extremely wide-ranging access to sensitive patient data—have raised alarms among patient advocates and researchers. NarxCare’s home page, for instance, describes how its algorithm trawls patient medical records for diagnoses of depression and post-traumatic stress disorder, treating these as “variables that could impact risk assessment.” In turn, academics have published hundreds of pages about NarxCare, exploring how such use of diagnostic records could have a disparate impact on women (who are more likely to suffer trauma from abuse) and how its purported use of criminal justice data could skew against racial minorities (who are more likely to have been arrested).

But the most troubling thing, according to researchers, is simply how opaque and unaccountable these quasi-medical tools are. None of the algorithms that are widely used to guide physicians’ clinical decisions—including NarxCare—have been validated as safe and effective by peer-reviewed research. And because Appriss’ risk assessment algorithms are proprietary, there’s no way to look under the hood to inspect them for errors or biases. 

Nor, for that matter, are there clear ways for a patient to seek redress. As soon as Kathryn realized what had happened, she started trying to clear her record. She’s still at it. In the meantime, when she visits a pharmacy or a doctor’s office, she says she can always tell when someone has seen her score. “Their whole demeanor has changed,” she says. “It reminds me of a suspect and a detective. It’s no longer a caring, empathetic, and compassionate relationship. It’s more of an inquisition.”

The United States’ relationship with opioid drugs has always been fraught. We either love them or we hate them. Historically, periods of widespread availability spur addictions, which lead to crackdowns, which lead to undertreatment of pain—and then another extreme swing of the pendulum, which never seems to settle at a happy medium.

The current anti-opioid climate has its roots in the overmarketing of Purdue Pharma’s OxyContin in the mid-1990s. Between 1999 and 2010, opioid prescribing in the US quadrupled—and overdose deaths rose in tandem. To many experts, this suggested an easy fix: If you decrease prescribing, then death rates will decline too.

But that didn’t happen. While the total amount of opioids prescribed fell by 60 percent between 2011 and 2020, the already record-level overdose death rate at least doubled during the same period. Simply cutting the medical supply didn’t help; instead, it fueled more dangerous drug use, driving many Americans to substances like illegally manufactured fentanyl.

The reason these cuts hadn’t worked, some experts believed, was that they had failed to target the patients at highest risk. Around 70 percent of adults have taken medical opioids—yet only 0.5 percent suffer from what is officially labeled “opioid use disorder,” more commonly called addiction. One study found that even within the age group at highest risk, teenagers and people in their early twenties, only one out of every 314 privately insured patients who had been prescribed opioids developed problems with them.

Researchers had known for years that some patients were at higher risk for addiction than others. Studies have shown, for instance, that the more adverse childhood experiences someone has had—like being abused or neglected or losing a parent—the greater their risk. Another big risk factor is mental illness, which affects at least 64 percent of all people with opioid use disorder. But while experts were aware of these hazards, they had no good way to quantify them.

That began to change as the opioid epidemic escalated and demand grew for a simple tool that could more accurately predict a patient’s risk. One of the first of these measures, the Opioid Risk Tool (ORT), was published in 2005 by Lynn Webster, a former president of the American Academy of Pain Medicine, who now works in the pharmaceutical industry. (Webster has also previously received speaking fees from opioid manufacturers.)

To build the ORT, Webster began by searching for studies that quantified specific risk factors. Along with the literature on adverse childhood experiences, Webster found studies linking risk to both personal and family history of addiction—not just to opioids but to other drugs, including alcohol. He also found data on elevated risk from particular psychiatric disorders, including obsessive-compulsive disorder, bipolar disorder, schizophrenia, and major depression.

Gathering all this research together, Webster designed a short patient questionnaire meant to suss out whether someone possessed any of the known risk factors for addiction. Then he came up with a way of summing and weighting the answers to generate an overall score.

The ORT, however, was sometimes sharply skewed and limited by its data sources. For instance, Webster found a study showing that a history of sexual abuse in girls tripled their risk of addiction, so he duly included a question asking whether patients had experienced sexual abuse and codified it as a risk factor—for females. Why only them? Because no analogous study had been done on boys. The gender bias that this introduced into the ORT was especially odd given that two-thirds of all addictions occur in men.

The ORT also didn’t take into account whether a patient had been prescribed opioids for long periods without becoming addicted.

Webster says he did not intend for his tool to be used to deny pain treatment—only to determine who should be watched more closely. As one of the first screeners available, however, it rapidly caught on with doctors and hospitals keen to stay on the right side of the opioid crisis. Today, it has been incorporated into multiple electronic health record systems, and it is often relied on by physicians anxious about overprescription. It’s “very, very broadly used in the US and five other countries,” Webster says.

In comparison to early opioid risk screeners like the ORT, NarxCare is more complex, more powerful, more rooted in law enforcement, and far less transparent.

Appriss started out in the 1990s making software that automatically notifies crime victims and other “concerned citizens” when a specific incarcerated person is about to be released. Later it moved into health care. After developing a series of databases for monitoring prescriptions, Appriss in 2014 acquired what was then the most commonly used algorithm for predicting who was most at risk for “misuse of controlled substances,” a program developed by the National Association of Boards of Pharmacy, and began to develop and expand it. Like many companies that supply software to track and predict opioid addiction, Appriss is largely funded, either directly or indirectly, by the Department of Justice.

NarxCare is one of many predictive algorithms that have proliferated across several domains of life in recent years. In medical settings, algorithms have been used to predict which patients are most likely to benefit from a particular treatment and to estimate the probability that a patient in the ICU will deteriorate or die if discharged.

In theory, creating such a tool to guide when and to whom opioids are prescribed could be helpful, possibly even to address medical inequities. Studies have shown, for instance, that Black patients are more likely to be denied medication for pain, and more likely to be perceived as drug-seeking. A more objective predictor could—again, in theory—help patients who are undermedicated get the treatment they need.

But in practice, algorithms that originate with law enforcement have displayed a track record of running in the opposite direction. In 2016, for example, ProPublica analyzed how COMPAS, an algorithm designed to help courts identify which defendants are most likely to commit future crimes, was far more prone to incorrectly flag Black defendants as likely recidivists. (The company that makes the algorithm disputed this analysis.) In the years since then, the problem of algorithmic unfairness—the tendency of AI to obscure and weaponize the biases of its underlying data—has become a increasingly towering concern among people who study the ethics of AI.

Over the past couple of years, Jennifer Oliva, director of the Center for Health and Pharmaceutical Law at Seton Hall University, has set out to examine NarxCare in light of these apprehensions. In a major recent paper called “Dosing Discrimination,” she argues that much of the data NarxCare claims to trace may simply recapitulate inequalities associated with race, class, and gender. Living in a rural area, for example, often requires traveling longer distances for treatment—but that doesn’t automatically signify doctor shopping. Similarly, while it’s a mystery exactly how NarxCare may incorporate criminal justice data into its algorithm, it’s clear that Black people are arrested far more often than whites. That doesn’t mean that prescribing to them is riskier, Oliva says—just that they get targeted more by biased systems. “All of that stuff just reinforces this historical discrimination,” Oliva says.

Appriss, for its part, says that within NarxCare’s algorithms, “there are no adjustments to the risk scoring to account for potential underlying biases” in its source data. 

Other communications from the company, however, indicate that NarxCare’s underlying source data may not be what it seems.

Early in the reporting of this piece, Appriss declined WIRED’s request for an interview. Later, in an emailed response to specific questions about its data sources, the company made a startling claim: In apparent contradiction to its own marketing material, Appriss said that NarxCare’s predictive risk algorithm makes no use of any data outside of state prescription drug registries. “The Overdose Risk Score was originally developed to allow for ingestion of additional data sources beyond the PDMP,” a spokesperson for the company said, “but no states have chosen to do so. All scores contained within NarxCare are based solely on data from the prescription drug monitoring program.”

Some states do incorporate certain criminal justice data—for instance, drug conviction records—into their prescription drug monitoring programs, so it’s conceivable that NarxCare’s machine-learning model does draw on those. But Appriss specifically distanced itself from other data sources claimed in its marketing material.

For instance, the company told WIRED that NarxCare and its scores “do not include any diagnosis information” from patient medical records. That would seem to suggest, contra the NarxCare homepage, that the algorithm in fact gives no consideration to people’s histories of depression and PTSD. The company also said that it does not take into account the distance that a patient travels to receive medical care—despite a chatty 2018 blog post, still up on the Appriss site, that includes this line in a description of NarxCare’s machine-learning model: “We might give it other types of data that involve distances between the doctor and the pharmacist and the patient’s home.”

These latest claims from Appriss only heighten Oliva’s concerns about the inscrutability of NarxCare. “As I have said many times in my own research, the most terrifying thing about Appriss’ risk-scoring platform is the fact that its algorithms are proprietary, and as a result, there is no way to externally validate them,” says Oliva. “We ought to at least be able to believe what Appriss says on its own website and in its public-facing documents.”

Moreover, experts say, even the most simple, transparent aspects of algorithms like NarxCare—the tallying of red flags meant to signify “doctor-shopping” behavior—are deeply problematic, in that they’re liable to target patients with complex conditions. “The more vulnerable a patient is, the more serious the patient’s illness, the more complex their history, the more likely they are to wind up having multiple doctors and multiple pharmacies,” notes Stefan Kertesz, a professor of medicine and public health at the University of Alabama at Birmingham. “The algorithm is set up to convince clinicians that care of anybody with more serious illness represents the greatest possible liability. And in that way, it incentivizes the abandonment of patients who have the most serious problems.”

To take some of the heat off of these complex patients, Appriss says that its algorithm “focuses on rapid changes” in drug use and deemphasizes people who have maintained multiple prescriptions at stable levels for a long time. But as ever, the company stresses that a NarxCare score is not meant to determine any patient’s course of treatment—that only a doctor can do that.

Doctors, however, are also judged by algorithms—and can be prosecuted if they write more prescriptions than their peers, or prescribe to patients deemed high risk. “I think prescribers have gotten really scared. They are very fearful of being called out,” says Sarah Wakeman, the medical director of the Substance Use Disorder Initiative at Massachusetts General Hospital, an assistant professor of medicine at Harvard, and a doctor who regularly uses NarxCare herself. Research has found that some 43 percent of US medical clinics now refuse to see new patients who require opioids.

Doctors are also, Wakeman says, “just not really sure what the right thing to do is.” A couple of academic surveys have found that physicians appreciate prescription drug registries, as they truly want to be able to identify patients who are misusing opioids. But doctors have also said that some registries can take too much time to access and digest. NarxCare is partly a solution to that problem—it speeds everything up. It distills.

The result of all that speed, and all that fear, says Kertesz, is that patients who have chronic pain but do not have addictions can end up cut off from medication that could help them. In extreme cases, that can even drive some chronic pain sufferers to turn to more dangerous illegal supplies, or to suicide. Among patients with long-term opioid prescriptions, research shows that stopping those prescriptions without providing effective alternative care is associated with nearly triple the risk of overdose death.

“The problem that really infuses the NarxCare discussion is that the environment in which it is being used has an intense element of law enforcement, fear, and distrust of patients,” Kertesz says. “It’s added to an environment where physicians are deeply fearful for their future ability to maintain a profession, where society has taken a particularly vindictive turn against both physicians and patients. And where the company that develops this interesting tool is able to force it onto the screens of nearly every doctor in America.”

As Kathryn became more steeped in online communities of chronic pain patients, one of the people she came into contact with was a 44-year-old woman named Beverly Schechtman, who had been galvanized by her own bad experience with opioid risk screening. In 2017, Schechtman was hospitalized for kidney stones, which can cause some of the worst pain known to humans. In her case, they were associated with Crohn’s disease, a chronic inflammatory disease of the bowel.

Because Crohn’s flare-ups by themselves can cause severe pain, Schechtman already had a prescription for oral opioids—but she went to the hospital that day in 2017 because she was so nauseated from the pain that she couldn’t keep them or anything else down. Like Kathryn, she also took benzodiazepines for an anxiety disorder.

That combination—which is both popular with drug users and considered a risk factor for overdose—made the hospitalist in charge of Schechtman’s care suspicious. Without even introducing himself, he demanded to know why she was on the medications. So she explained that she had PTSD, expecting that this disclosure would be sufficient. Nonetheless, he pressed her about the cause of the trauma, so she revealed that she’d been sexually abused as a child.

After that, Schechtman says, the doctor became even more abrupt. “Due to that I cannot give you any type of IV pain medication,” she recalls him saying. When she asked why, she says he claimed that both IV drug use and child sexual abuse change the brain. “‘You’ll thank me someday, because due to what you went through as a child, you have a much higher risk of becoming an addict, and I cannot participate in that,’” she says she was told.

Schechtman says she felt that the doctor was blaming her for being abused. She was also puzzled.

She had been taking opioids on and off for 20-odd years and had never become addicted. Wasn’t that relevant? And how could it be ethical to deny pain relief based on a theoretical risk linked to being abused? She wasn’t asking for drugs to take home; she just wanted to be treated in the hospital, as she had been previously, without issue.

As would later happen for Kathryn, the experience drove Schechtman onto the internet. “I just became obsessed with researching all of it,” Schechtman says. “I was asking people in these online groups, ‘Have any of you been denied opioids due to sexual abuse history?’ And women were coming forward.”

Schechtman eventually joined an advocacy group called the Don’t Punish Pain Rally. Together with other activists in the group, she discovered that the question about sexual abuse history in the ORT unfairly targeted women, but not men. (An updated version of Webster’s tool now excludes the gender difference, but the older one seems to live on in some electronic medical record systems.)

She also found many pain patients who said they had problems with NarxCare. Bizarrely, even people who are receiving the gold standard treatment for addiction can be incorrectly flagged by NarxCare and then denied that very treatment by pharmacists.

Buprenorphine, best known under the brand name Suboxone, is one of just two drugs that are proven to cut the death rate from opioid use disorder by 50 percent or more, mainly by preventing overdose. But because it is an opioid itself, buprenorphine is among the substances that can elevate one’s NarxCare score—though typically it is listed in a separate section of a NarxCare report to indicate that the person is undergoing treatment. That separation, however, doesn’t necessarily prevent a pharmacist from looking at a patient’s high score and refusing to offer them prescriptions.

Ryan Ward, a Florida-based recovery advocate, has taken buprenorphine for nearly a decade. He also has a history of severe back pain and related surgeries. In 2018, when his pharmacy stopped carrying buprenorphine, he tried to fill his prescription at a Walmart and was turned away. Then he visited two CVS’s and three Walgreens, and was similarly stymied.

“I dress nicely. I look nice. And I would be friendly,” he says. “And as soon as they get my driver’s license, oh boy, they would change attitudes. I couldn’t figure out why.”

After panicking that he might plunge into withdrawal—and, ironically, be put at much higher risk of overdose—he changed tactics. He approached a pharmacist at a Publix, first showing her his LinkedIn page, which highlights his advocacy and employment. He described what had happened at the other drugstores.

When she checked the database, she immediately saw the problem: an overwhelmingly high Overdose Risk Score. Unlike her colleagues, however, she agreed to fill the prescription, realizing that it was nonsensical to deny a patient a medication that prevents overdose in the name of preventing overdose. Still, even three years later, if he tries another pharmacy he gets rejected.

Appriss stresses that its data is not supposed to be used in these ways. “Pharmacists and physicians use these scores as indicators or calls-to-action to further review details in the patient’s prescription history in conjunction with other relevant patient health information,” the company wrote in a statement. “The analysis and associated scores are not intended to work as sole determinants of a patient’s risk.” Appriss also says that prescriptions for buprenorphine have increased in areas of the country that use NarxCare.

But like the others, Ward has been unable to get his problem fixed. And since most states now require that physicians and pharmacists use these databases, millions are potentially affected. One survey of patients whose providers have checked these systems found that at least half reported being humiliated and 43 percent reported cuts in prescribing that increased pain and reduced quality of life.

Appriss says on its website that it’s up to each state to deal with patient complaints. Still, few know where to turn. “The states have made it very difficult,” says Oliva. Some don’t even allow for error correction. And when Ward tried contacting Appriss directly, he says, he was ignored.

In the early 2010s, Angela Kilby was seeking a topic for her PhD thesis in economics at MIT. When a member of her family, a doctor in the rural South, told her how tough it was to make decisions about prescribing opioids in a community devastated by overdoses, Kilby felt she had found her subject. She decided to study the doctor’s dilemma by examining how increased control over opioid prescribing actually affected patients. To track health outcomes, she used insurance claim data from 38 states that had implemented prescription monitoring databases at varying times between 2004 and 2014.

Going into her study, Kilby had been swayed by research and press reports—plentiful in an era of “pill mill” crackdowns and backlash against overprescribing—suggesting that opioids are not only addictive but also ineffective and even harmful for patients with chronic pain. She had predicted that reductions in prescribing would increase productivity and health. “I was expecting to see the opposite of what I saw,” she says.

In fact, her research showed that cutting back on medical opioid prescriptions led to increased medical spending, higher levels of pain in hospitalized patients, and more missed workdays. “These are people who are probably losing access to opioids, who are struggling more to return to work after injuries and struggling to get pain treatment,” she says.

Intrigued, she wanted to know more. So in the late 2010s, having become an assistant professor at Northeastern University, she decided to simulate the machine-learning model that generates NarxCare’s most algorithmically sophisticated measure, the Overdose Risk Score.

Although Appriss did not make public the factors that went into its algorithm, Kilby reverse engineered what she could. Lacking access to prescription drug registry data, Kilby decided to use de-identified health insurance claims data, a source that underlies all of the other published machine-learning algorithms that predict opioid risk. Using roughly the same method that Appriss lays out in accounts of its own machine-learning work, she trained her model by showing it cases of people who’d been diagnosed with opioid use disorder after receiving an opioid prescription. She sent it looking for resemblances and risk predictors in their files. Then she turned her model loose on a much larger sample, this time with those opioid-use-disorder diagnoses hidden from the algorithm, to see if it actually identified real cases.

What Kilby found was that while NarxCare’s model may trawl a different data set, it almost certainly shares an essential limitation with her algorithm. 

“The problem with all of these algorithms, including the one I developed,” Kilby says, “is precision.” Kilby’s complete data set included the files of roughly 7 million people who were insured by their employers between 2005 and 2012. But because opioid addiction is so rare in the general population, the training sample that the algorithm could use to make predictions was small: some 23,000 out of all those millions.

Further, 56 percent of that group had addictions before they received their first prescription, meaning that the medication could not have caused the problem—so they had to be excluded from the training sample. (This supports other data showing that most people with opioid addiction start with recreational, rather than medical, use.)

The result was that Kilby’s algorithm generated a large number of both false positive and false negative results, even when she set her parameters so strictly that someone had to score at or above the 99th percentile to be considered high risk. In that case, she found, only 11 percent of high scorers had actually been diagnosed with opioid use disorder—while 89 percent were incorrectly flagged.

Loosening her criteria didn’t improve matters. Using the 95th percentile as a cutoff identified more true positives, but also increased false ones: This time less than 5 percent of positives were true positives. (In its own literature, Appriss mentions these two cutoffs as being clinically useful.)

Kilby’s research also identified an even more fundamental problem. Algorithms like hers tend to flag people who’ve accumulated a long list of risk factors in the course of a lifetime—even if they’ve taken opioids for years with no reported problems. Conversely, if the algorithm has little data on someone, it’s likely to label them low risk. But that person may actually be at higher risk than the long-term chronic pain patients who now get dinged most often.

“There is just no correlation whatsoever between the likelihood of being said to be high risk by the algorithm and the reduction in the probability of developing opioid use disorder,” Kilby explains. In other words, the algorithm essentially cannot do what it claims to do, which is determine whether writing or denying someone’s next prescription will alter their trajectory in terms of addiction. And this flaw, she says, affects all of the algorithms now known to be in use.

In her paper “Dosing Discrimination,” about algorithms like NarxCare, Jennifer Oliva describes a number of cases similar to Kathryn’s and Schectman’s, in which people have been denied opioids due to sexual trauma histories and other potentially misleading factors. The paper culminates in an argument that FDA approval—which is currently not required for NarxCare—should be mandatory, especially given Appriss’ dominance of the market.

The larger question, of course, is whether algorithms should be used to determine addiction risk at all. When I spoke with Elaine Nsoesie, a data science faculty fellow at Boston University with a PhD in computational epidemiology, she argued that improving public health requires understanding the causes of a problem—not using proxy measures that may or may not be associated with risk.

“I would not be thinking about algorithms,” she says. “I would go out into the population to try to understand, why do we have these problems in the first place? Why do we have opioid overdose? Why do we have addictions? What are the factors that are contributing to these problems and how can we address them?”

In contrast, throughout the overdose crisis, policymakers have focused relentlessly on reducing medical opioid use. And by that metric, they’ve been overwhelmingly successful: Prescribing has been more than halved. And yet 2020 saw the largest number of US overdose deaths—93,000—on record, a stunning 29 percent increase from the year before.

Moreover, even among people with known addiction, there is little evidence that avoiding appropriate medical opioid use will, by itself, protect them. “I think undertreated pain in someone with a history of addiction is every bit, if not more, of a risk factor for relapse,” says Wakeman. She calls for better monitoring and support, not obligatory opioid denial.

Appriss has recognized the need to study NarxCare’s effects on the health and mortality of people flagged by the system—and not just whether it results in reduced prescribing. At a recent webinar, the company’s manager of data science, Kristine Whalen, highlighted new data showing that implementation of NarxCare sped up the decline in opioid prescribing in six states by about 10 percent, compared to reductions before it was used. When asked whether the company was also measuring NarxCare’s real-world effects on patients’ lives, Whalen said, “We’re actively looking for additional outcome data sets to be able to do what you are describing.”

For Kathryn, at least, NarxCare’s effect on her life and health has been pretty stark. Aside from her psychiatrist, she says, “I don’t have a doctor because of this NarxCare score.” She worries about what she’ll do the next time her endometriosis flares up or another emergency arises, and she still struggles to get medication to treat her pain.

And it’s not only Kathryn’s own pain prescriptions that require filling. Although her dog Moose died in late 2020, Bear continues to need his meds, and Kathryn has since gone on to adopt another medically demanding dog, Mouse. Some states have recognized the problem of misidentified veterinary prescriptions and require NarxCare to mark them with a paw print or animal icon on health providers’ screens. Apparently, though, those prescriptions can still influence the pet owner’s overall scores—and the next busy pharmacist who peers warily at a computer screen.


Pt safety and FOR PROFIT CORPORATE MEDICINE

Docs Sue Envision Over Violation of Bans on Corporate Practice of Medicine

https://www.medpagetoday.com/special-reports/exclusives/96364

An emergency medicine physician group sued private equity-owned Envision Healthcare, alleging it violated laws that ban corporations from practicing medicine in California, according to the lawsuit complaint.

The American Academy of Emergency Medicine Physician Group (AAEM-PG), a unit of the AAEM that supports independent physician practices, claimed that Envision Healthcare violated laws banning the corporate practice of medicine when it took over an emergency department contract at Placentia-Linda Hospital, a facility owned by Tenet.

“Envision through itself and by the direct control of its controlled medical groups, kickbacks, restrictive covenants, and other practices alleged, violated California’s ban on the practice of medicine in violation,” AAEM-PG stated in the lawsuit.

The suit does not seek monetary damages. Instead, it is asking the court to stop Envision from operating the emergency department at Placentia-Linda Hospital and other facilities in California. Envision currently operates at least a dozen emergency departments in the state, according to the complaint.

Several states have corporate practice of medicine laws that aim to keep commercial interests out of the doctor-patient relationship. California law bans corporations or any other non-licensed individuals or entities from practicing medicine, assisting in the unlicensed practice of medicine, employing physicians, or owning physician practices.

AAEM-PG alleged that Envision, which is owned by private-equity giant KKR, interfered with clinical practice, offered remuneration to hospitals in exchange for emergency department contracts, and used restrictive covenants to limit physicians’ ability to practice their profession.

In response to an inquiry from MedPage Today, a spokesperson for Envision Healthcare stated that the company does not comment on pending litigation.

“This is the bottom line interfering with medical decision-making,” Robert McNamara, MD, chair of emergency medicine at Temple University and former president of AAEM, said in an interview. “There’s all kinds of implications associated with corporations making decisions for the bottom line that may not be in the best interest of patients. Those are the greatest concerns.”

Last August, a subsidiary of Envision Healthcare was awarded an exclusive emergency department contract by Placentia-Linda Hospital, which guaranteed its employees exclusive rights to treat patients at that location. Prior to the takeover, the hospital’s emergency department was run by an independent physician group, which contracted AAEM-PG to take care of administrative services.

The complaint states that Envision allocated remuneration to hospitals in exchange for exclusive emergency department contracts. In this case, the AAEM-PG alleged that Envision provided anesthesia services to a Tenet hospital without the hospital having to pay a subsidy for those services in exchange for the Placentia-Linda ED contract, adding that the organization believes this kickback scheme is one of Envision’s “standard methods of acquiring new contracts and maintaining existing ones.”

Additionally, the AAEM-PG claimed that Envision “exercises profound and pervasive” control over physicians’ practice of medicine. The corporation not only decides which and how many physicians will be hired, their work schedules, and their compensation — it also determines how many patients a provider takes on, medical charges, and clinical decisions, the AAEM-PG stated.

“Envision establishes and promulgates physician ‘best practices,’ ‘red rules,’ and ‘evidence-based pathways’ protocols which enumerate standards for treating patients and are a form of clinical oversight,” the group noted.

The AAEM-PG also alleged that Envision requires its physicians to sign restrictive covenants — agreements that prohibit the clinicians from forming a group to compete with Envision or from helping any other groups acquire the emergency room contracts that the company currently holds. Physicians’ mobility is also restrained for a 1-year period after their departure from Envision, barring them from working with groups who compete with Envision for contracts, the complaint noted.

“The provision has the effect of reducing competition in the business and trade of emergency department physician services, reducing the number of competitors for Emergency Services Contracts, limiting the supply of emergency physicians available to patients seeking emergency services, and causing increases in the price of such patient services by limiting the facilities where emergency physicians can practice,” AAEM-PG stated.

McNamara said the organization hopes the courts “take a hard look” at how this business is set up, and examine how the alleged violations impact patients.

“The essence of the corporate practice of medicine law is rooted in protecting the physician-patient relationship,” McNamara said. “That’s what we’re looking to get upheld here.

CDC opiate guidelines: Linked to Overdose, Mental Health Crises ?

High-Dose Opioid Tapering Linked to Overdose, Mental Health Crises

https://www.specialtypharmacycontinuum.com/Clinical/Article/12-21/High-Dose-Opioid-Tapering-Linked-to-Overdose-Mental-Health-Crises/65626

Patients tapered from a stable, long-term, higher-dose opioid therapy are significantly more likely to incur an overdose or a mental health crisis within one year, compared with those who are not tapered, according to a retrospective cohort study.

“Many factors have led to a major decrease in opioid prescribing over the past several years, and many patients who were taking stable doses of opioids for chronic pain have had their doses reduced or tapered,” said principal investigator Alicia Agnoli, MD, MPH, MHS, an assistant professor of family and community medicine at the University of California, Davis, in Sacramento. “However, there have been reports of patients becoming suicidal as their doses were reduced, as well as overdose events.”

The study culled deidentified medical and pharmacy claims and enrollment data from the OptumLabs Data Warehouse from 2008 to 2019. Adults in the United States prescribed stable higher doses of opioids, defined as a mean 50 morphine milligram equivalents per day for a 12-month baseline period with at least two months of follow-up, were eligible for inclusion. Opioid tapering was designated as at least a 15% relative reduction in mean daily dose during any of six overlapping 60-day windows within a seven-month follow-up period.

The two main outcomes were emergency or hospital encounters for drug overdose or withdrawal, and mental health crises such as depression, anxiety and suicide attempt, during the first 12 months of follow-up. The final study cohort consisted of 113,618 patients, of whom 18.2% underwent dose tapering. Patients who did and did not undergo dose tapering were matched for age, sex and insurance type (women, taper 54.3% vs. no taper 53.2%; age, taper 57.7 vs. no taper 58.3 years; commercially insured, taper 38.8% vs. no taper 41.9%).

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Tapered patients were found to be more highly associated with overdose, with an adjusted incidence rate of 9.3 overdose events per 100 person-years compared with 5.5 events per 100 person-years in nontapered patients. The same was true for experiencing a mental health crisis, where tapered patients had an adjusted incidence rate of 7.6 mental health crisis events per 100 person-years versus 3.3 events per 100 person-years in nontapered patients.

Furthermore, increasing the maximum monthly dose reduction by 10% was connected to an adjusted incidence rate ratio of 1.09 for overdose and 1.18 for mental health crisis.

Dr. Agnoli and her research team are troubled by the magnitude of the associations they found. “For every 100 patients followed for one year, tapering was connected to about four additional patients having an overdose event and four additional patients having a mental health crisis event,” Dr. Agnoli said. “Because we only looked at hospital and emergency events, this could be just the tip of the iceberg of suffering that patients experience when tapering.”

Clinicians need to understand that the period of tapering is one of heightened vulnerability, Dr. Agnoli said. “The decision to embark on tapering should depend on the patient’s goals and priorities. Also, when possible, the rate of dose reduction should be gradual.”

Dr. Agnoli believes clinicians should try to schedule patients frequently and be on the lookout for symptoms of withdrawal, worsening pain and/or depression. She also encourages medical practices to implement the recommendations outlined in the recent Health and Human Services guideline for opioid dose reduction, released in October 2019.

“Opioid therapy is complex, with important risk–benefit considerations for patients and prescribers alike,” she said. “Our study sheds light on specific and significant risks associated with the complicated endeavor of deprescribing. We hope the study will inform a more cautious and compassionate approach to decisions around opioid dose tapering.”

Due to the increased risk for overdose and mental health crisis following dose reduction, “patients undergoing tapering need significant support to safely reduce or discontinue their opioids,” Dr. Agnoli added.

Jeffrey C. Gadsden, MD, FRCPC, FANZCA, the chief of the Division of Orthopaedics, Plastics and Regional Anesthesiology and an associate professor of anesthesiology at Duke University, in Durham, N.C., noted that the study results “support some other evidence to date, suggesting that the psychologic strain o f rapid tapering, especially from high baseline doses, can precipitate mental health crises or accidental overdose.”

However, the conclusions should be interpreted with some caution, said Dr. Gadsden, who was not part of the research, “particularly because its particular methodology used patients who presented to the hospital with either overdose or mental health crisis.”

Dr. Gadsden said he suspects the data would look somewhat different if the study had included patients in each cohort who succumbed to overdose or suicide attempt at home, “which is a more alarming outcome.”

Regardless, the study “serves as a good reminder that opioid tapering—a noble and worthwhile goal—should only be attempted with the appropriate support structure in place to ensure patient safety and compliance,” he said. “The study also underscores the notion that preventive strategies remain our best weapon against opioid-related morbidity and mortality. Every effort should be made by physicians to provide patients with a variety of nonopioid analgesic strategies and therapies before moving on to prescribing opioids.”

ONE MILLION DOLLAR SETTLEMENT: USA paid for VA prescribed induced SUICIDE

‘He was always here, and now he is not’: Mom of veteran speaks out following his suicide in Dublin VA parking lot

https://www.13wmaz.com/article/news/local/deposition-reveals-va-doctor-reduced-veterans-drug-medicine-mistakenly-4/93-2d9dc181-1f28-4bcc-9d5a-a2e7a4b74b72

It’s been more than two-and-a-half years since Navy Veteran Gary Steven Pressley died by suicide in the parking lot of the Carl Vinson VA Medical Center in Dublin. Legal documents say a serious car accident left Pressley with a fractured hip, pelvis, and chronic lower back pain. His family says Pressley shot himself because he was in so much pain after his outside pain management doctor stopped seeing him and his VA doctor reduced his pain medicine. Recently, the United States paid his mother a $1 million for 40 days of pain and suffering Pressley experienced before death, but court documents in the case show what her lawyer calls “negligence” in the VA system. 

How Activists Are Hardwiring ‘Race Marxism’ Into The Medical Field – the USA – FOR PROFIT – healthcare system

How Activists Are Hardwiring ‘Race Marxism’ Into The Medical Field

https://dailycaller.com/2021/12/24/race-marxism-medicine/

  • Activists in the medical field are urging doctors to prioritize patients on the basis of race to resolve racial disparities in health outcomes.
  • Doctors who question this practice or suggest that personal choices drive racial health disparities are punished and silenced.
  • The highest institutions of medical research, including organizations in the federal government, are pushing a radical, racist ideological takeover of medicine. 

The Biden administration proposed giving bonus payments to physicians who acknowledge systemic racism as the primary cause of health differences between racial groups and incorporate so-called “anti-racism” into their medical practices.

The move to pressure healthcare professionals to repeat the claim that racial health disparities are caused by racism and not lifestyle choices is part of a broader, years-long push to hardwire “race Marxism” into the medical field. The effort stretches from medical schools and research institutions to patient care and medical administration, with potentially devastating effects for patients and the healthcare system as a whole.

“Race Marxism,” analogous to “anti-racism” as popularized by Ibram X. Kendi, seeks to promote equal outcomes across racial groups, as opposed to a “colorblind” approach which favors equal opportunity and does not take race into account.

Dr. Erica Li, a pediatrician, told the Daily Caller News Foundation that “race Marxism” — a phrase for which she does not take credit — pits “classes” of people against each other on the basis of race, gender or sexuality rather than economic class, as classical Marxism did.

The ideology’s newfound popularity caused a frenzy in the medical community in 2020 as doctors, researchers, medical schools and other medical institutions sought to infuse “anti-racist” practices into their work.

PATIENT CARE

Doctors and medical institutions are questioning how they allocate limited resources in crisis situations in light of unequal health outcomes for different racial groups. Specifically, some medical professionals have advocated for prioritizing black and Latino patients on the basis of race when rationing limited, life-saving medical resources.

When deciding which groups would receive the first vaccines, the Centers for Disease Control and Prevention (CDC) recommended prioritizing essential workers over the elderly — despite the elderly facing higher risk of death from COVID-19 — in order to be more racially equitable (the elderly tend to be more white while essential workers tend to be less white, demographically), according to the Los Angeles Times.

The CDC walked back the suggestions after public outcry, according to Dr. Sally Satel, but Vermont explicitly granted vaccine priority on the basis of race to non-white households before the general public became eligible. The vaccination rate for white residents (33%) had been outpacing that of non-white residents (20%); Republican Governor Phil Scott said this gap was unacceptable at the time.

Dr. Harald Schmidt of the University of Pennsylvania medical school advocated for updating guidance for rationing ventilators to account for race and other socioeconomic factors in April 2020. He suggested that hospitals use a zip code-based “Area Deprivation Index” to avoid the “legal complications” of explicitly race-based allocation of medical resources. Dr. Schmidt and the University of Pennsylvania medical school did not respond to DCNF’s requests for comment.

Brigham and Women’s hospital in Boston considered a pilot program which would prioritize patients for cardiovascular care explicitly on the basis of race. Described by doctors Michelle Morse and Bram Wispelwey in a March article in Boston Review, the program would have given preferential admissions to black and Latino people for cardiological services to reduce heart health gaps between white and non-white patients.

Morse and Wispelwey argued that health gaps between different racial groups are driven by racism, and they viewed their plan as a form of racial reparations. The proposal drew from the 2010 proposal titled, “Critical Race Theory, Race Equity, and Public Health: Toward Antiracism Praxis.”

Brigham publicly distanced itself from Morse and Wispelwey’s article following public outcry, and it released a statement denying that the hospital offered or planned to offer preferential care on the basis of race, repeatedly stating that the pilot program was merely under consideration.

Brigham’s statement said news stories about the proposal were misleading, but it did not denounce the Boston Review article or its authors or contest the article’s claim in the article that “[racial reparations are] exactly what we have tried to achieve in the design our new pilot initiative at Brigham and Women’s Hospital.” Brigham also did not challenge the authors’ claims that the colorblind approach to medicine was insufficient.

Mark Murphy, a Brigham spokesman, told the DCNF the final version of the pilot program set to be implemented later this year to address racial health disparities would give “educational notices” to clinicians admitting patients with heart failure to the hospital. The notices would educate employees that black and Latino individuals are historically less likely to be admitted to cardiological services, but they would not restrict clinicians’ individual judgement and decision-making, according to Murphy.

Murphy told DCNF the Boston Review article was “an opinion piece and reflects the perspective of these two physicians,” but the article’s authors, who work at Brigham, called the pilot program “our pilot program,” a fact Brigham has not disputed. Murphy confirmed that both Morse and Wispelwey helped create the final pilot program going into effect this year.

More than 1,000 health professionals publicly supported mass protests in the wake of George Floyd’s death in June 2020 despite COVID-19 concerns, arguing that racism was a public health threat which superseded the medical community’s social distancing advice. Jennifer Nuzzo of Johns Hopkins argued at the time that “in this moment the public health risks of not protesting to demand an end to systemic racism greatly exceed the harms of the virus.”

RESEARCH

Three scientists argued that “researchers must name and interrogate structural racism and its sociopolitical consequences as a root cause of the racial health disparities we observe” in the prominent Journal of the American Medical Association in September 2020. Their insistence that researchers ignore the impact of personal choice and environmental factors is part of a broader effort within medicine to erase individual agency and blame all health disparities on systemic racism.

The National Institutes of Health (NIH), the largest funder of biomedical research in the world, has also turned its attention to racial issues. Its plan for ending structural racism in biomedical sciences includes pouring funding into research projects on structural racism and expanding diversity and inclusion programs for NIH administrators.

The NIH plays a major role in determining what kind of scientific research goes on in the U.S., funding more than $30 billion of biomedical research each year. Its new emphasis on race has driven important research on racial health disparities and their causes. It has also resulted in millions of taxpayer dollars being poured into research which is distinctly ideological rather than scientific.

The NIH gave $3.4 million to a Tulane researcher in October to develop an app that helps white parents teach “anti-racism,” as opposed to color-blindness, to their children. It also gave $600,000 to a University of Michigan professor to teach “anti-racism” to middle school students, Campus Reform reported.

ACADEMIA

A 2020 study on racial disparities in birthing mortality for newborns found that black newborns cared for by black doctors are half as likely to die compared to black babies treated by white physicians. The study failed to note that, in cases of a bad NICU outcome, the department chair or division chief is more likely to be listed as the doctor of record regardless of whether that doctor was ever involved in the care of the newborn. Department chairs and division chiefs are more likely to be white, according to Li.

“It’s garbage data in, garbage conclusion out … but what the public takes away is that white doctors are killing black babies. How is that going to create trust among our African American patients? I worry they will stop going to the hospital if they get sick,” Li said. (RELATED: Professor Sues UCLA After Refusing To Grade Black Students’ Work Differently)

Dr. Norman Wang, a program director at the University of Pittsburgh medical school, was removed from his position after publishing a paper which questioned the efficacy of race-based affirmative action.

Dr. Edward Livingston, an editor of the Journal of the American Medical Association (JAMA), argued on a podcast that socioeconomic factors, not structural racism, held back communities of color. Livingston and the top editor at JAMA both resigned after public outcry, with the latter being suspended for three months before his resignation.

The Association of American Medical Colleges wrote that leaders in academic medicine “are weaving content and experiences throughout their curricula to significantly boost awareness of social inequities and structural drivers of health” and argued that equity-related “social drivers need to be woven into the very fiber of medical education.”

Li told the DCNF she is concerned that practices based in “race Marxism” could negatively affect medical education and ultimately patient care by detracting from the limited time medical students have to learn critical scientific information. (RELATED: Professor Resigns In Open Letter Because His College Transformed ‘Into A Social Justice Factory’)

Doctors are noticing a decline in newly-graduated medical interns, Li explained.

Li also worries that doctors may be asked in the future to pledge allegiance to “race Marxism” ideology in the maintenance of license process, meaning that doctors who do not comply would risk losing their medical licenses or board certifications. The American Board of Medical Specialties (ABMS), which controls medical licensing in the U.S., already incorporates diversity, equity and inclusion (DEI) content into its continuing certification programs.

ABMS member boards, which license doctors in specific fields, such as family medicine or pediatrics, collect racial data on candidates and physicians to evaluate certification exams and incorporate the data into “ongoing improvement efforts,” according to the ABMS website. Most of these boards also provide implicit bias training for item writers and examiners and plan to expand these trainings further, the website states.

ABMS did not respond to the Daily Caller’s requests for comment.

INCENTIVES

Dr. Carrie Mendoza, a Chicago-based emergency medicine physician and Fellow of the American College of Emergency Physicians, spoke with the DCNF about how new ideas travel from academia into patient care and medical administration using the example of the opioid crisis.

Doctors use CPT codes, which are owned by the American Medical Association (AMA), to bill insurance and government  programs such as Medicare. Since the AMA derives income through doctors’ use of CPT codes, there is an incentive to create more codes, Mendoza explained.

In the early 2000s, widespread concern that patients’ pain was not being adequately addressed led regulators to require doctors and hospitals to measure pain, introducing the pain scale as the “fifth vital sign,” Mendoza said. Doctors’ improvement of their patients’ pain scores was used to determine whether doctors were “meeting goals,” and it even impacted doctors’ bonuses, according to Mendoza. Doctors were incentivized to prescribe more pain medication, and the AMA’s CPT codes for pain treatment were the structure through which those financial incentives were fulfilled.

“In emergency medicine we quickly saw that people were getting inappropriate prescriptions for things like ankle sprains and then becoming addicts, then there were diversions and overdoses,” Mendoza explained. 

Mendoza sees a link between the early stages of the opioid crisis and the current popularity of racial essentialism in the medical field. By creating CPT codes for Social Determinants of Health (SDH), a new umbrella term adopted by the medical industry to focus on patients’ education and their experiences with discrimination, poverty and incarceration, among many other factors, the AMA is incentivizing a bureaucracy to focus on issues outside the doctor’s control, Mendoza argued.

“There’s a parallel here where admission requirements for medical schools and residency are being loosened. When these factors converge, you get into an environment where there can be patient harm,” Mendoza said.

Mendoza speculated that the government could use data collected through SDH codes to justify its priorities in healthcare. For example, the University of Illinois, citing data on homelessness as a social determinant of health, partnered with the Center for Housing and Health to provide housing for homeless patients.

The AMA, which develops CPT codes, released a 2021-2023 “strategic plan to embed racial justice and advance health equity” which aims to “understand and operationalize anti-racism equity strategies … develop structures and processes to consistently center the experiences and ideas of historically marginalized … and minoritized (Black, Indigenous, Latinx, Asian and other people of color) physicians” and “amplify and integrate often ‘invisible-ized’ narratives of historically marginalized physicians and patients in all that AMA does.”

The American Medical Association did not respond to DCNF’s requests for comment.

What Your Death Certificate Says About You May Be Wrong: A Narrative Review on CDC’s Efforts to Quantify Prescription Opioid Overdose Deaths

What Your Death Certificate Says About You May Be Wrong: A Narrative Review on CDC’s Efforts to Quantify Prescription Opioid Overdose Deaths

https://www.cureus.com/articles/68550-what-your-death-certificate-says-about-you-may-be-wrong-a-narrative-review-on-cdcs-efforts-to-quantify-prescription-opioid-overdose-deaths

Abstract

Mortality data in most countries are reported using the International Classification of Diseases (ICD), managed by the WHO. In this paper, we show how the ICD is ill-suited for classifying drug-involved deaths, many of which involve polysubstance abuse and/or illicitly manufactured fentanyl (IMF).

Opioids identified in death certificates are categorized according to six ICD T-codes: opium (T40.0), heroin (T40.1), methadone (T40.3), other synthetic narcotics (T40.4), and other and unspecified narcotics (T40.6). Except for opium, heroin, and methadone, all other opioids except those that are unspecified are aggregated in two T-codes (T40.2 and T40.4), depending upon whether they are natural/semisynthetic or synthetic opioids other than methadone. The result is a system that obscures the actual cause of most drug overdose deaths and, instead, just tallies the number of times each drug is mentioned in an overdose situation.

We examined the CDC’s methodology for coding other controlled substances according to the ICD and found that, besides fentanyl, the ICD does not distinguish between other licit and illicitly manufactured controlled substances. Moreover, we discovered that the CDC codes all methadone-related deaths as resulting from the prescribed form of the drug. These and other anomalies in the CDC’s mortality reporting are discussed in this report.

We conclude that the CDC was at fault for failing to correct the miscoding of IMF. Finally, we briefly discuss some of the public policy consequences of this error, the misguided focus by public health and safety officials on pharmaceutical opioids, their prescribers and users, and the pressing necessity for the CDC to reassess how it measures and reports drug-involved mortality.

Introduction & Background

Vital statistics provide an important measure of a nation’s health and welfare. In this report, we focus on mortality statistics compiled and reported by the Centers for Disease Control and Prevention (CDC). In 2019, the CDC reported a total of 2,854,838 deaths in the United States (US) [1]. Of this number, 173,040 deaths (6%) were “unintentional injury deaths,” a category that includes falls, motor vehicle traffic deaths, and unintentional poisoning deaths (i.e., drug overdose deaths) [2]. The number of unintentional poisoning deaths reported by the CDC for 2019 was 71,130 [3]. While only 2.5% of the total deaths in 2019, unintentional poisoning deaths account for more than a third (38%) of all unintentional injury deaths. This percentage is likely to increase as CDC’s preliminary estimates for 2020 show 93,000 drug overdose deaths – an increase of 30.7% over the previous year [3].

The process of recording deaths begins with the death certificate, a document that has critical administrative and epidemiologic applications. It is used to settle estates, resolve insurance claims, terminate pensions and public benefits, identify causes of morbidity and mortality, establish public health policies, and inform the allocation and expenditure of public funds for such purposes [4]. In the US, registering individual births and deaths is a state responsibility, while the responsibility for compiling and publishing national vital statistics rests with the federal government, specifically the CDC [5].

William Farr, a 19th-century British registrar general, in his annual report of deaths in 1840, warned certifiers to be specific in recording cause of death descriptions and to avoid the use of vague statements like “sudden death,” “natural death,” “visitation of God,” and “old age.” These, he said, obscured the proximate cause – what he referred to as the “internal morbid process.” [6]. Voiced more than 180 years ago, Farr’s words remain true today and apply to death certifiers in the US and more than a hundred nations of the world that use some form of the death certification process to register and report mortality.

Although not a focus of this paper, the recent COVID-19 pandemic and the unusual fluctuations in global mortality ascribed to the coronavirus have uncovered some of the limitations in the current systems used by nations reporting COVID-19 deaths. According to figures compiled by Johns Hopkins University of Medicine’s Coronavirus Resource Center, between the beginning of the pandemic and December 15, 2020, global COVID-19 deaths fluctuated from a low of 0.3 per 100,000 population in China to a high of 156.5 per 100,000 population in Belgium. The US reported 91.6 deaths per 100,000 during this period [7]. A statistical range of such magnitude suggests inconsistencies in how individual nations define and report COVID-19 mortality.

In the US, timelines and procedures for reporting and investigating accidental or suspicious deaths are set by state law and vary among states and even within states [8]. In the case of deaths that are neither accidental nor suspicious, the process of certifying the cause of death begins with the attending physician who is likely to have access to the decedent’s health record, including details of preexisting conditions that may have caused or contributed to death [9]. Upon completion and receipt by state registrars of vital statistics, portions of the death certificate, including the cause of death entries, are forwarded electronically to the CDC National Center for Health Statistics (NCHS) for inclusion in the National Death Index (Figure 1) [10].

One of the weakest links in this chain is at the beginning when the immediate and contributory causes of death are recorded by the certifying official. An error made or an incomplete cause of death at this juncture will pass from state to federal officials and result in a loss of data or worse, the corruption of the mortality database itself.

Review

The US standard death certificate

As noted, the federal government has legal responsibility for annually publishing the nation’s vital statistics, including the manner and causes of deaths [11]. The US standard death certificate is the source document for compiling these statistics [11]. Since 1960, collecting this information has been the responsibility of the NCHS, a subdivision of the CDC [11,12]. The National Vital Statistics System (NVSS), a program within the NCHS, is responsible for receiving, compiling, and publishing this information [12].

Death certificate data, including cause(s) of death, are entered into the NVSS database by technicians using software that converts the literal language of the certifiers into category codes according to the WHO’s International Classification of Diseases (ICD) [13]. Each year, the NVSS handles about 2.9 million death reports. They pass through an automated system that does not control for the accuracy or completeness of the data. Thus, the CDC begins its task of compiling national mortality data with raw source data that, according to the CDC’s estimate, arrives at the agency with an acknowledged error rate between 20-30% [14].

CDC guidelines for completing the standard death certificate require certifiers to list a single immediate cause of death in Section 32, Part I, along with a brief description of the sequence and timing of contributing causes leading up to the immediate cause of death. In Section 32, Part II, of the death certificate a certifier may include up to 20 contributory causes of death [13].

Over 20 years ago, an expert panel of state registrars and CDC officials proposed changes to the standard death certificate that eventually resulted in the creation of the electronic death registration system (EDRS) [15]. Being able to file a death certificate electronically greatly improved the efficiency of the process, but did little to improve accuracy. Hanzlick, a noted forensic pathologist, described this process as follows: “If a cause of death is stated improperly or not clearly, the person who classifies and codes the cause of death (a nosologist) uses a somewhat arbitrarily established system of rules to identify a cause of death for coding and official classification.” [16]

The “arbitrarily established system of rules” Hanzlick mentioned is the ICD [16]. “Our national mortality statistics,” Hanzlick cautioned, “are derived from codes, which, in turn, have been derived from causes of death as written on death certificates by certifiers. The value of an accurately and clearly stated cause of death to ensure proper coding and classification of deaths cannot be overstated.” [16]. Although addressing present-day issues involving the EDRS and the ICD, Hanzlick’s concerns echoed those of 19th-century Registrar General Farr mentioned earlier.

In 2004, the CDC published detailed instructions and examples for completing its revised death certificate [17]. Examples of the completed cause of death entries for Section 32, Parts I & II, of the standard death certificate are given in Figure 2.

While Section 32 of the death certificate provides the immediate and contributory causes of death, other sections, notably Sections 32-37, contain useful and relevant information. The CDC’s guidance notes that in a drug-involved death, a toxicological analysis may detect multiple substances but only those in the opinion of the certifying official that is determined to have caused or contributed to the death should be listed [13]. In the example shown in Figure 3, acetaminophen and nicotine were detected along with several other drugs in the toxicology screen of a drug overdose victim but since acetaminophen and nicotine did not cause nor contribute to death, they were not listed as an immediate cause of death [13].

How ICD coding may obscure the true cause of death

Using the example in Figure 2, we see that the CDC’s NVSS codes each drug identified as a cause of death separately according to its ICD T-code (e.g., T40.2 for hydrocodone, T40.2 for oxycodone, and T42.4 for alprazolam). Lost in many fatal drug overdose cases are the true cause of death, namely the additive toxic and often fatal consequences of co-ingesting opioids, benzodiazepines (e.g., alprazolam), and other central nervous system depressants. Studies have linked these drugs, especially when co-ingested in non-therapeutic doses, to increased risk of respiratory depression, coma, and death [18,19].

In a research letter published by the Journal of the American Medical Association (JAMA) in 2018, several government scientists reported that among the 42,249 opioid-related overdose deaths in 2016, 19,413 deaths (45.9%) involved synthetic opioids. Of these deaths, 79.7% involved another drug or alcohol. The most common co-involved substances were another opioid (47.9%), heroin (29.8%), cocaine (21.6%), prescription opioids (20.9%), benzodiazepines (17.0%), alcohol (11.1%), psychostimulants (5.4%), and antidepressants (5.2%) [20].

In drug overdose death cases, significant epidemiologic information is lost when each drug mentioned in the death certificate finds its way into a specific ICD T-code category. Presumably, this enables CDC officials to be able to state a percentage of drug overdose deaths each year in which, for example, T40.2 or T42.4 drugs were involved. However, aggregating data like this obscures not only the prevalence of specific drugs in causing overdose deaths but also ignores entirely the real cause of most fatal overdoses, namely, the additive or toxic interactive effects of polysubstance abuse.

In 2013, CDC scientists published a research letter in which they characterized specific classes of drugs involved in drug-related overdose deaths [21]. More than 20 drug classes were identified by T-codes in a chart showing the number and percentages of times they were mentioned in “pharmaceutical overdose deaths” in 2010 [21]. “Opioid analgesics (T40.2-T40.4)” were involved in 16,651 overdose deaths and “Benzodiazepines (T42.4)” were involved in 5,017 overdose deaths (77.2% of all opioid-involved deaths) [21]. A footnote seemed to acknowledge the limited usefulness of this information: “Deaths are not mutually exclusive. Deaths involving more than one drug or drug class are counted multiple times.” [21]. Thus, the death of the hypothetical person whose cause of death information appears in Figure 2, according to the CDC’s methodology for coding drug overdose deaths, would be counted three times – once for each ICD T-coded drug in the death certificate! This limitation, coupled with the CDC’s estimate that 20-30% of death certificates in drug-related cases arrive listing incomplete or imprecise causes, such as “multiple drug intoxication,” or “suspected drug overdose,” etc., draws our attention to Hanzlick’s concerns.

Becoming a mortality statistic

The path between being certified a death of any cause or manner and becoming a national statistic is a relatively short but important one that typically involves a funeral director, medical certifier (attending physician, medical examiner/coroner, etc.), state vital statistics registrar, and, ultimately, the CDC [13]. As previously mentioned, anonymized death certificate information is shared by state registrars with the CDC [13]. At a minimum, this includes sharing demographic characteristics of the decedent, the time, place, and manner of death, and the literal text of the cause of death as provided by the certifier [13]. Timelines and procedures for reporting deaths and conducting postmortem examinations are set by state law and vary widely among states and sometimes even within states [22].

If a death certificate is not completed properly, the public registrar of vital statistics in the jurisdiction where it is filed may be expected to return it to the certifying official [23]. Multiple studies have shown, however, that death certificate errors are common and certificates rarely are returned to the certifier for correction or completion [24-26]. Death certificate errors have been discussed in the literature since the 1950s, with analyses of data going back to the 1800s [25-28]. Errors and incompletions in death certificates have not been limited to drug overdose cases. For example, studies have shown that ischemic heart disease is vastly overrepresented as a cause of death [29,30]. In a 1998 study of death certificates, it was estimated that the error rate for overestimating coronary heart disease as a cause of death was between 7.9-24.3%, and in older persons, it is as much as twofold [29,31]. Conversely, diabetes and dementia are often under-reported as causes of death [29,30]. In drug overdose cases, when a prescription opioid is believed to be involved, the cause of death frequently is listed as “opioid overdose,” regardless of any other aspect of the clinical situation [32].

As noted earlier, 20-30% of drug overdose death certificates arrive at the CDC with the erroneous or incomplete cause of death certifications [33]. Dr. Robert Anderson, chief of the Mortality Statistics Branch of the NVSS, has been quoted as saying that before the current coronavirus pandemic, one in every three death certificates was ‘wrong’ and that things were about to get worse [14]. This percentage of error is in keeping with the findings of previous studies of the accuracy of death certificate source data [33,34].

Swedish researchers, conducting a metadata assessment of 44 death certificate studies found that methodological variances among certifiers were responsible for different results, some of which were inconsistent with WHO standards [35]. Comparing death certificates with hospital discharge records, they noted a greater risk of certification errors for some diagnostic groups [35]. Malignant neoplasm cases had the highest degree of accuracy, whereas benign and unspecified tumor and chronic obstructive lung disease had the lowest degree of accuracy [36].

Physicians and death certificate errors

Wexelman conducted an anonymous online survey of 531 physician residents in New York City responding to a questionnaire about their experiences in completing death certificates [37]. Only 33.3% believed that cause of death reporting is accurate [37]. Of the 531 respondents, 48.6% admitted to knowingly certifying an inaccurate cause of death [37]. Of respondents who indicated they reported an inaccurate cause of death, 76.8% said that they did so because the system would not accept the correct cause. Of these, 40.5% said admitting office personnel instructed them to “put something else” (as a cause of death), and 30.7% said a medical examiner instructed them to change their initial entry [37]. If the initial cause was rejected by the EDRS software, almost two-thirds (64.6%) of the respondents said that they would instead cite cardiovascular disease, reasoning that everyone dies of cardiac arrest [37].

Education appears to exert a positive effect on improving the accuracy of death certificate information. In a literature review of educational programs designed to improve death certificate accuracy, Aung et al. (2010) concluded that “Pragmatic education on best practice for cause-of-death certification is a basic step to ensure accurate information for each individual case.” [38].

While physicians are often the focus of death certificate errors and inaccurate cause of death entries, it is worth noting that they are not the only professionals responsible for completing the death certificate. Funeral directors are responsible for completing the second and third sections of the standard death certificate, as well as making sure the cause of death section has been completed by the appropriate medical or certifying official [39]. Funeral directors are not authorized to complete the cause of death entries reserved exclusively for medical personnel [39]. However, in some states, non-physicians, such as nurse-practitioners and coroners, are authorized to certify a decedent’s cause of death [40]. In Texas, state law requires that a Justice of the Peace must conduct an inquest into the death of someone who dies of suspicious or unusual circumstances, not necessarily requiring autopsies or even viewing the body [41]. The inquest may require a formal autopsy by a medical examiner or forensic pathologist, which are only in larger counties, and a hearing to determine the cause of death [41]. At the conclusion of the inquest, the Justice of the Peace completes the death certificate, including certifying the cause of death [41].

Coroners and medical examiners

Although they share similar medico-legal responsibilities, there are significant differences between medical examiners and coroners. The origin of the coroner system is obscure, but the first mention of it dates to the 12th century [42]. The system of coroner followed England’s colonization around the world [42]. In the US, coroners are elected officials in many jurisdictions and only four states require that they be physicians [43]. According to the CDC, 16 states (and Washington, DC) have centralized medical examiner systems, six states have a county or district-based medical examiner system, 14 states have a county-based system with a mixture of coroner and medical examiner office, 14 states have a county-, district-, or parish-based coroner system, and 25 states solely have state medical examiners [44]. Qualifications to become a coroner differ among states. In Georgia, to run for the office of coroner one must be at least 25 years of age, a registered voter, a high school graduate, and not have a felony conviction [45]. In 2016, of Georgia’s 154 coroners, only one was a physician and four had criminal records [45]. According to the Atlanta Journal-Constitution (AJC), the state’s largest newspaper, the job of coroner is considered a part-time job in Georgia and the AJC’s review of state records showed coroners listing their other occupations as farmer, car wash owner, hairdresser, plumber, etc. [45]. The AJC examined thousands of death certificates issued statewide since 2011 and found almost three dozen cases in which coroners ruled that shooting victims died of “natural causes.” [45]. In nearly four of every 10 cases ruled a suicide, no autopsy was performed to confirm the cause and manner of death [45]. Although coroners who are not physicians cannot perform autopsies, they are responsible for making the initial decision for when an autopsy is required. If the initial decision is erroneous, as mentioned in the examples of suicide reported by the AJC investigation, the result is a loss of the data for which the death certification system was created in the first place.

The US is not alone in tolerating these conditions. Kelsall and Bowes [46] reported that in Canada, “(T)here is no accreditation system for coroner or medical examiner offices, no national standards for the investigation or classification of death, no nationally recognized training program or credentialing system for coroners and medical examiners, and no agreement on common outcome measures against which to evaluate performance.” Autopsy requirements vary widely among Canadian provinces and the authors, both physicians, contend that “assigning deaths as ‘undetermined’ in cases of drug overdose, for example, because an autopsy was not done, precludes efforts to prevent future deaths.” [46].

International Classification of Diseases

Technically known as the International Statistical Classification of Diseases and Related Health Problems, the ICD is considered by the WHO to be the world’s “bedrock for health statistics.” [47]. As the WHO describes the ICD, “It maps the human condition from birth to death: any injury or disease we encounter in life − and anything we might die of − is coded.” [48]. While coding, that is, the assigning of alphanumeric codes to diseases and injuries represents the core genius of the ICD for harmonizing global health statistics, it also may hamper the proper classification of important information when coding categories and guidelines are too restrictive. Originally developed to classify mortality and to provide a coding schema for reporting causes of death, the ICD has expanded over the years to include classifying morbidity and many other items, services, and procedures related to the delivery of healthcare [48].

In the US, the ICD consists of two components, identified as ICD-10-CM, for clinical modifications, and ICD-10-PCS, for procedural coding systems [48]. The NCHS, with guidance from the Centers for Medicare and Medicaid Services, has responsibility for developing the ICD-10’s clinical modifications used in the US [48]. In 1999, the final year for using ICD-9, there were 13,000 diagnostic codes in the US clinical modifications version [49]. ICD-10 that followed ICD-9 included 68,000 codes in its clinically modified version – a fivefold increase [50]. Chapter 19 of the ICD-10 established two subcategories identified as S and T codes [51]. The S codes are for various single body region injuries, and the T codes cover injuries to unspecified body locations, poisonings, and other external consequences [51]. Containing more than 40,000 individual codes for characterizing health-related topics and items, the ICD has just six T-codes for identifying all opioids: T40.0 (opium), T40.1 (heroin), T40.2 (other natural and semisynthetic opioids, including morphine, codeine, oxycodone, hydrocodone, hydromorphone, and oxymorphone), T40.3 (methadone), T40.4 (synthetic opioids other than methadone, including fentanyl, meperidine, pentazocine, propoxyphene, tapentadol, buprenorphine, and tramadol), and, finally, T40.6 (other and unspecified narcotics) (Figure 4) [50].

CDC finally comes clean – maybe!

In 2018, four senior CDC analysts, including the head of the Epidemiology and Surveillance Branch, published a three-page editorial acknowledging that the number of US deaths in 2016 attributed to prescription opioid overdoses was erroneously overstated [51]. The problem was caused by including IMF in the coding category – T40.4, synthetic opioids – for the prescribed version: “Thus, rates of prescription opioid-involved deaths estimated with the traditional method may have been inflated in recent years because of the increase in death rates involving synthetic opioids (e.g., fentanyl).” [52]. According to internal CDC reports, the IMF problem was discovered during the analysis of 2015 data being prepared for the 2016 report of prescription opioid overdose deaths [Courtney Lenard, Public Affairs Officer, CDC. Email to Puja Seth, Chief, Epidemiology and Surveillance Branch, CDC, dated March 27, 2018, Subject: Interview Request (talking points) (unpublished); Obtained by John J. Coleman on January 20, 2021, via Freedom of Information Act Request re: Case 21-00194-FOIA, along with the letter from HHS signed Roger Andoh, FOIA Officer (unpublished; available from the correspondence author of this article upon reasonable request). 2018.]. Although it took two years for the problem to be explained in a journal article, the CDC was aware of IMF long before a sudden spike in deaths attributed to synthetic opioids in 2015 was traced to IMF [53]. In 2008, the CDC published a report of what it called “Nonpharmaceutical Fentanyl-Related Deaths,” a form of illicitly produced fentanyl that appeared in multiple US states during the period from April 2005 to March 2007 [54]. Nonpharmaceutical fentanyl, according to the 2008 CDC report, was responsible for more than a thousand deaths in the US over the course of two years [54]. In 2013, IMF reappeared causing deaths in the northeast [55]. Rhode Island authorities notified the CDC that acetyl fentanyl, a fentanyl analog up to five times as potent as fentanyl, had been identified in 10 drug overdose deaths in the state between March 7 and April 11, 2013 [56]. During and shortly after the month-long investigation in Rhode Island, four more overdose deaths occurred [56]. Besides the Rhode Island deaths, a CDC field report at the time cited a cluster of 50 similar IMF deaths reported by authorities in Pennsylvania [56]. In their editorial, the CDC analysts reported that until 2016, the NVSS calculated annual prescription opioid overdose deaths by summing deaths coded T40.2, T40.3, and T40.4 [57]. The latter code – T40.4 – was identified as the source of error in the 2016 data [58]. All mentions of fentanyl – including non-prescribed IMF – were coded T40.4 by the agency’s nosologists [57]. In 2016, the sum of the three codes amounted to 32,445 deaths [58]. This figure, the CDC analysts acknowledged, was erroneous because it included deaths involving IMF that were mistakenly coded and counted as a “prescribed” synthetic opioid [57,58]. To correct the error, the CDC analysts proposed what they called a ‘conservative’ method for re-calculating prescription opioid overdose deaths in 2016 and prior years [57]. Their conservative method simply removed all deaths coded T40.4 from the count [57]. This, in turn, reduced the number of prescription opioid overdose deaths in 2016 from 32,445 to 17,087 – a sizable drop of 47.3% [57]. The analysts candidly conceded, however, that the “conservative” approach likely produced an undercount error because by deleting all T40.4 deaths, they were removing an unknown number of deaths caused by, or involving, prescription fentanyl, as well as deaths caused by, or involving, other prescription opioids identified with the same T40.4 code (e.g., meperidine, pentazocine, propoxyphene, tapentadol, buprenorphine, and tramadol) [57].

The discovery of the T-code error in the CDC’s prescription opioid overdose death figures for 2016 prompted us to search for other examples of similar errors in the CDC’s coding system for controlled substances. We discovered that benzodiazepines, a class of drugs often associated with fatal opioid drug overdoses, are undifferentiated in ICD T-codes [59]. More than a dozen FDA-approved benzodiazepine drugs are identified by a single ICD T-code (T42.4) [59]. In 2018, according to the National Institute on Drug Abuse, benzodiazepines were involved in 15.8% of all drug overdose deaths (Figure 5) [60]. While the aggregate coding of all benzodiazepines is not an error, per se, it does reduce the epidemiological value and specificity of the data. In their 2018 article, the CDC analysts did not acknowledge these limitations or other miscoding errors for drugs other than IMF. We, however, discovered similar anomalies in the CDC’s use of the ICD for reporting non-opioid overdose mortality. For example, cocaine, a controlled substance that is FDA-approved for medical use, is also manufactured illicitly and sold on the street as cocaine or cocaine base (also known as “crack”) [61,62]. Despite important epidemiological differences between the licit and illicit forms of cocaine, all references to this drug in the ICD are categorized under a single T-code (T40.5) [59].

Methadone: prescribed for pain but administered/dispensed for opioid use disorder (OUD)

While investigating the topic, we discovered a more serious source of error involving the CDC’s handling of methadone-related overdose deaths. Methadone is a Schedule II opioid agonist with dual indications for the management of severe chronic pain and for treating opioid use disorder (OUD) [63]. When used for pain treatment, methadone is subject to the same regulations as any other Schedule II prescription drug. However, when used for treating OUD, federal law prohibits the prescribing of methadone [64,65]. Methadone used for treating OUD must be administered or dispensed by an authorized practitioner in an opioid treatment program (OTP) certified by the Substance Abuse and Mental Health Services Administration (SAMHSA) and registered by the Drug Enforcement Administration (DEA) [63,66,67]. Patients admitted for OUD treatment with methadone receive the medication under the direct supervision of a practitioner authorized to dispense or administer (but not prescribe) the medication to the patient in liquid oral form. Patients must visit the OTP for the first 90-days to be administered daily doses of methadone. After this period, take-home doses of methadone may be approved for dispensing to the patient by the OTP, provided that certain criteria are met [66,67]. During the COVID-19 pandemic, SAMHSA relaxed take-home rules to allow states with declared health emergencies to request an exemption for stable methadone patients, regardless of time in the program, to receive as much as 28 days of take-home doses of methadone [68]. While necessary, take-home methadone has been identified with diversion and misuse because of the drug’s high street value [69]. According to the director of the largest opiate treatment program in Baltimore, a 28-day supply of diverted methadone is worth as much as $2,000 on the street [69].

 In 2014, according to the CDC, methadone accounted for approximately 1% of all opioids prescribed for pain but was involved in approximately 23% of all prescription opioid deaths [70]. The CDC did not report how many methadone deaths resulted from the prescribed form (i.e., for pain), and how many methadone deaths resulted from the nonprescribed form (i.e., administered/dispensed for OUD treatment). Citing no evidence other than the total number of deaths from methadone, a CDC report in 2017 claimed that “the preponderance of methadone-associated morbidity and mortality likely arises from its use for pain.” [70]. This assumption, however, is not supported by the record. Prescribed methadone, dispensed by retail pharmacies, in the US decreased 71.2% between 2010 and 2019 (from 6,068,686.51 grams to 1,746,684.03 grams) [71]. During the same period, the volume of nonprescribed methadone administered/dispensed by OTPs increased 49.9% (from 8,746,056.41 grams to 13,114,262.44 grams) [71]. In 2017, the year that the CDC reported the data “the preponderance of methadone-associated morbidity and mortality likely arises from its use for pain,” the volume of nonprescribed methadone administered or dispensed by OTPs was more than fourfold the volume of methadone prescribed for pain (11,686,565 grams administered/dispensed by OTPs vs. 2,740,641 grams prescribed for pain) (Figure 6) [71].

The data in Figure 6 show what appears to be an inverse relationship in the use of methadone between the two populations, i.e., chronic pain patients and OUD patients. Given what is known about ICD coding and how its use skewed CDC mortality figures for prescription opioid overdose deaths in 2016, the subject of how the CDC codes methadone-involved deaths demand scrutiny. It seems like CDC’s annual tally of prescription opioid overdose deaths, which includes all methadone-involved deaths, continues to be skewed by including deaths involving non-prescribed methadone.

The CDC ignored error signals in calculating prescription opioid overdose deaths

In their 2018 article, the CDC analysts (Seth et al.) provided a table showing the calculation of prescription opioid overdose deaths from 1999 to 2016, describing what they termed a “conservative” approach and a “traditional” approach (Table 1) [52]. By 2013, the number of deaths in the T40.4 category (see the column in Table 1 titled, “Synthetic opioids, other than methadone”) began to increase sharply after several years of relative stability [52]. By 2016, this category had increased over 525% (from 3,105 deaths in 2013 to 19,413 deaths in 2016) [52]. By 2016, the CDC’s data were showing that more than 37.4% of all prescription opioid deaths were being caused by “synthetic opioids other than methadone,” the T40.4 category that besides fentanyl, includes pethidine (meperidine), pentazocine, propoxyphene, tapentadol, tramadol, and buprenorphine [52]. The CDC findings regarding T40.4 drugs were inconsistent with prescribing data available at the time showing that the volume of fentanyl dispensed by retail pharmacies in the US declined between 2013 and 2016 by 13.2% (from 403,773.3 grams to 350,397.3 grams) [72]. In the past, CDC analysts have described a close linear relationship between sales volumes of opioids and opioid overdose deaths, showing that whenever sales increase, they are followed by proportional increases in overdose deaths [73-76]. If one assumes that the converse of this also is true, i.e. a decrease in opioid sales is associated with fewer overdose deaths, there should have been a decrease – not an increase – in prescription fentanyl overdose deaths for the period in question.

Conservative definition for prescription opioids: natural and semisynthetic opioids and methadone Traditional definition for prescription opioids: natural and semisynthetic opioids and methadone, and other synthetic opioids Synthetic opioids other than methadone
Year Number Overdose deaths per 100,000 Number Overdose deaths per 100,000 Number Overdose deaths per 100,000
2000 3785 1.3 4400 1.5 782 0.3
2001 4770 1.7 5528 1.9 957 0.3
2002 6483 2.3 7456 2.6 1295 0.4
2003 7461 2.6 8517 2.9 1400 0.5
2004 8577 2.9 9857 3.4 1664 0.6
2005 9612 3.2 10928 3.7 1742 0.6
2006 11589 3,9 13723 4.6 2707 0.9
2007 12796 4.2 14408 4.8 2213 0.7
2008 13149 4.3 14800 4.8 2306 0.8
2009 13523 4.4 15597 5.0 2946 1.0
2010 14583 4.7 16651 5.4 3007 1.0
2011 15140 4.9 16917 5.4 2666 0.8
2012 14240 4.5 16007 5.1 2628 0.8
2013 14145 4.4 16235 5.1 3105 1.0
2014 14838 4.6 18893 5.9 5544 1.8
2015 15281 4.7 22598 7.0 9580 3.1
2016 17087 5.2 32445 10.2 19413 6.2

By 2013, the number of deaths in the T40.4 category (see the column in Table 1 titled, “Synthetic opioids, other than methadone”) began to increase sharply after several years of relative stability [52]. By 2016, this category had increased over 525% (from 3,105 deaths in 2013 to 19,413 deaths in 2016) [52]. By 2016, the CDC’s data were showing that more than 37.4% of all prescription opioid deaths were being caused by “synthetic opioids other than methadone,” the T40.4 category that besides fentanyl, includes pethidine (meperidine), pentazocine, propoxyphene, tapentadol, tramadol, and buprenorphine [52]. The CDC findings regarding T40.4 drugs were inconsistent with prescribing data available at the time showing that the volume of fentanyl dispensed by retail pharmacies in the US declined between 2013 and 2016 by 13.2% (from 403,773.3 grams to 350,397.3 grams) [72]. In the past, CDC analysts have described a close linear relationship between sales volumes of opioids and opioid overdose deaths, showing that whenever sales increase, they are followed by proportional increases in overdose deaths [73-76]. If one assumes that the converse of this also is true, i.e. a decrease in opioid sales is associated with fewer overdose deaths, there should have been a decrease – not an increase – in prescription fentanyl overdose deaths for the period in question.

 Instead, according to Table 1, three of every five drug overdose deaths (19,413/32,445 = 59.8%) in 2016 were coded T40.4 (“Synthetic opioids, other than methadone”). A rapid increase in overdose deaths caused by T40.4 drugs, a category that includes fentanyl and several other scheduled opioids, simply made no sense at a time when the prescribing of all T40.4 drugs, including fentanyl, was stable or declining (Figure 7).

Buprenorphine, the only T40.4 drug showing an increase during this time, like methadone, has a dual indication for treating pain and OUD [77]. Because it is a Schedule III drug, physicians can apply for authorization to prescribe buprenorphine for the treatment of OUD [78,79]. DEA distribution records do not differentiate between buprenorphine prescribed for treating pain and buprenorphine prescribed for treating OUD. Given the expansion of out-patient treatment of OUD with buprenorphine in the last decade, it is reasonable to assume the increased volume of prescribed buprenorphine depicted reflects the increased prescribing of the drug for OUD treatment [80].

Discussion

The modern death certificate may have taken five centuries to create, but, as our study is showing, some of the problems that plagued its earliest versions continue today. The decentralization of authority for certifying and reporting deaths has produced substantial differences in how causes of death are characterized and reported. By any measure, the error rate in death certifications, especially in drug-involved cases, remains excessive. It is critical that death data are accurate for obvious reasons. If data used by the CDC have significant error rates, which appears to be the case, there needs to be adjustments made to improve the accuracy of this program. We are not the first authors to suggest this; errors in mortality causes and reporting, as noted above, have been described in the literature for decades. 

The US government reports that health expenditures in 2017 amounted to $3.4 trillion, a sizable portion of which was spent on disease prevention and control [81]. When the source data for achieving public health goals are less than accurate, as in the situations highlighted in this paper, policymakers and public health officials are likely to focus attention and resources on the wrong things – such as the sources for prescribed fentanyl instead of the sources for illicitly manufactured non-prescribed fentanyl and fentanyl analogs. Mortality resulting from cardiovascular disease may be imprecisely overstated because of software limitations and input errors. As briefly mentioned earlier, reports of COVID-19 deaths according to WHO guidelines appear inconsistent and unreliable among reporting nations. All these problems begin but do not end with the death certificate.

In 2018, the same year that the CDC finally admitted that its system for tabulating prescription opioid overdose deaths was flawed, Congress enacted the Support for Patients and Communities Act [82]. The 250-page Act contained more than a hundred references to drug overdose prevention [82]. In Subtitle Q, Sect. 392A, “Preventing Overdoses of Controlled Substances,” the CDC was directed to coordinate “controlled substance overdose data collection” by, among other things, “(C) Modernizing the system for coding causes of death related to controlled substance overdoses to use an electronic-based system.” [82]. This provision likely was intended to have the CDC correct the present flawed system for coding causes of death related to drug overdoses before the next update of the ICD, an all-electronic online version, expected to debut in January 2022 [83].

The Act also mandated the reinstatement of the Drug Abuse Warning Network (DAWN), a retrospective survey of drug-related hospital admissions that inexplicably was halted by SAMHSA at the height of the opioid abuse crisis in 2011 [82]. Well into two years since these important improvements were statutorily ordered by Congress, neither is yet functional (although a contract to reinstitute the DAWN program was awarded in November 2018 to Westat Inc. (Rockville, MA), its former vendor [84]). The Department of Health and Human Services, the parent agency for the CDC and SAMHSA, has opted instead to provide grants to state agencies to perform tasks assigned by federal law to the CDC, namely, to compile drug-involved mortality statistics with a greater emphasis on expanding the specificity and characteristics of drug-involved overdose deaths. Thus far, the CDC has recruited and funded about 47 states to provide this information that the CDC gathers, reformats, and publishes under its own rubric [72].

Why the CDC ignored for years clear signals that its methodology for calculating prescription opioid overdose deaths was flawed is unknown. It seems like, even today, the CDC has no way of determining the actual number of prescription opioid overdose deaths each year. For more than a decade, the CDC’s erroneous reports of prescription opioid overdose deaths went unchallenged while being used by Congress and the Executive Branch as the justification for public policy. In just nine years, from 2012 to 2020, the federal government expended $261.3 billion for drug control [85]. It is estimated that the states spent at least as much, if not more, for the same purpose. The full effect of the CDC’s reporting failures as discussed herein may never be known. What is known is that the amount of money spent on drug control in the US has continued to increase along with the magnitude of the problem. An improved system that is responsive and accurate would likely produce better results. Moreover, it would generate less confounding scientific literature, which most of the time is based on US official data.

It should be noted that in 2021 (beginning October 1, 2020), CMS finalized clinical modifications to specifically identify poisonings by fentanyl or fentanyl analogs within the T40.4 coding category [86]. The same treatment was applied to poisonings by tramadol [86]. Unfortunately, these changes did nothing to remedy the IMF problem because they did not offer a specific coding definition for distinguishing illicit from licit fentanyl. Actually, they were responsible for misinformation appearing in the White House report [87]. Definitely, it is a difficult problem to fix.

Limitations

This narrative review has several limitations. Amongst others, the US situation, which was described in detail, was not compared with reports from the rest of the world. As previously stated, the huge majority of the scientific literature on this topic derives from US official data. The 117 nations that use the ICD for reporting mortality may experience similar limitations in the coding of drug-involved deaths. The CDC’s problem directly results from its use of the ICD codes to categorize drug-involved mortality. Presumably, any other country presently using the same ICD system and encountering counterfeit opioids or benzodiazepines (mostly alprazolam), or using methadone for opiate addiction treatment by administration/dispensing-only (not by prescription except for pain treatment) – would obviously encounter the same problems as the CDC unless, that is, they used more sensitive reporting elements to properly identify drug deaths by specific drugs rather than general categories. The CDC has begun to do this but only from about 2016 when it discovered the error caused by including IMF in its compilation of prescription opioid overdose deaths. Considering that in other countries too, the official data are prevalently based on ICD, it would be important (and interesting) to compare most of the aspects of the cross-pollination, and its influence on the local political and social choices.

Conclusions

The CDC’s problems in reporting drug-involved mortality begin with the source data. Drug-involved overdose deaths pose a unique problem for medical examiners and coroners because of the unavoidable delay in receiving postmortem toxicology results. We found that this problem is not unique to drug overdose deaths, and problems involving deaths caused by other causes/diseases affecting the accuracy of death certificates are also common. The recent pandemic has raised additional questions as to how nations interpret WHO guidelines in recording COVID-19 mortality. 

Why it took more than a decade and several problems before the CDC was finally called to task to answer questions about the issues addressed in this paper is not yet entirely clear. The CDC’s solution for addressing the IMF problem was not a solution at all but simply a way to produce a smaller inaccurate number. Prescription drug abuse remains a national crisis and prescription opioids taken for non-therapeutic purposes contribute to tens of thousands of overdose deaths every year. Reducing drug-involved mortality will likely elude us until we have trustworthy systems that provide reliable data unobscured by antiquated algorithms and codes better suited to conditions of a bygone era.


CDC: won’t let FACTS interfere with a agenda narrative ?

CDC Admits It Has No Record of an Unvaccinated Person Spreading Covid After Recovering From Covid

https://www.thegatewaypundit.com/2021/11/cdc-admits-no-record-unvaccinated-person-spreading-covid-recovering-covid/

Lawyers smell blood in the water.

The CDC admitted it has no record of an unvaccinated person spreading Covid after recovering from Covid in response to an attorney’s FOIA request.

A New York attorney filed a FOIA request in September asking for “documents reflecting any documented case of an individual who (1) never received a Covid-19 vaccine; (2) was infected with Covid-19 once, recovered, and then later became infected again; and (3) transmitted SARS CoV-2 to another person when reinfected.”

The CDC responded: “A search of our records failed to reveal any documents pertaining to your request. The CDC Emergency Operation Center (EOC) conveyed that this information is not collected.”

TRENDING: HORROR: Four Young Soccer Stars from Four Different Countries Die This Week After Suffering Sudden Heart Attacks

A study examining T cell responses in Covid-19 convalescent individuals published earlier this year revealed natural immunity provides better protection against the China virus than vaccination.

Natural immunity doesn’t make Big Pharma any money which is why the Biden Regime and the CDC never talk about it.

USA Healthcare: Nothing more … nothing less than a FOR PROFIT BUSINESS ?

‘Get that money!’ Dermatologist says patient care suffered after private equity-backed firm bought her practice

https://www.nbcnews.com/health/health-care/get-money-dermatologist-says-patient-care-suffered-private-equity-back-rcna9152

A former doctor at a private equity-owned dermatology chain alleges lost biopsies, overbooking and questionable quality control in the company-owned lab.

The email to the health care workers was like something out of “The Wolf of Wall Street.” “We are in the last few days of the month and are only 217 appointments away from meeting our budget,” the August 2020 memo stated. “Don’t forget the August bonus incentive for all patients scheduled in August! That’s the easiest money you can make. Get that money!!”

The “Get that money!!” entreaty wasn’t addressed to a bunch of hard-charging, coke-snorting stockbrokers. It went to Michigan-based employees of Pinnacle Dermatology, a private equity-owned group of 90 dermatology practices across America.

The memo was shared with NBC News by a former Pinnacle employee, Dr. Allison Brown, a board-certified dermatologist and dermatopathologist. Brown says Pinnacle terminated her shortly after she advised management of questionable practices that she contends were hurting patients. 

Among the practices Brown alleges: overlooked diagnoses, lost patient biopsies, questionable quality control in the company-owned lab and overbooking of patients without sufficient support staff.

Physicians have a duty to put their patients’ interests first. But when aggressive financiers take over medical operations, the push for profits can take precedence, doctors in an array of specialties have told NBC News. Paying bonuses for increased patient visits may result in unnecessary appointments and costs, for example.

Among the most aggressive health care financiers in the market today are private equity firms. The new titans of finance, these firms have taken over broad swaths of U.S. industry in recent years. Using large amounts of debt to finance their acquisitions, private equity firms acquire companies, aim to increase their profits and then try to resell them a few years later for more than they paid.

Outside investors, such as public pension funds and endowments, commit big money to the deals in hope of generating high returns.

Private equity is reshaping the health care industry, practitioners, economists and academic researchers contend. Private equity funds dedicated solely to health care operations have been especially busy, raising $350 billion from investors over the past decade, according to Preqin, a private equity data source. Last year, almost $50 billion was raised from investors for health care buyouts, up from $8 billion in 2010.

A focal point in such takeovers has been physician-owned dermatology practices, a highly fragmented sector of small operations that private equity firms have considered ripe for consolidation over the past decade. Just before the pandemic, researchers counted more than 30 private equity-backed dermatology groups in the country and said about 15 percent of dermatology practices were private equity-owned. The number has probably grown, the researchers say.  

Private equity firms contend that they create jobs, support businesses and help provide comfortable retirements for pensioners invested in the strategy. But many outside the industry are especially critical of the industry’s involvement in health care. One private equity-owned hospital staffing company, for example, was behind many of the surprise emergency department bills that outraged hospital patients and resulted in a new law to curb the practices. It takes effect next month. 

“The private equity business model is fundamentally incompatible with sound health care that serves patients,” concluded a paper in May co-authored by Richard M. Scheffler, professor of health economics and public policy at the University of California, Berkeley; Laura M. Alexander, the vice president of policy at the American Antitrust Institute, a nonprofit organization; and James R. Godwin, a Ph.D. candidate at the UCLA Fielding School of Public Health.

The researchers found that private equity’s focus on short-term profits “leads to pressure to prioritize revenue over quality of care, to overburden health-care companies with debt, strip their assets, and put them at risk of long-term failure, and to engage in anticompetitive and unethical billing practices.”

In addition, economists and practitioners who have studied private equity-backed health care entities say they often try to increase revenue by providing services typically outsourced to third parties. For example, many dermatology practices backed by private equity acquire their own labs to analyze specimens. They can be a source of additional revenue, research shows, and may provide incentives for the practices to run extra tests, presenting possible conflicts of interest.

Pinnacle Dermatology, which is based in Brentwood, Tennessee, and operates in 11 states, has been buying small physician-owned practices and outpatient services. 

Dr. Jose Rios, Pinnacle’s president and chief medical officer, provided the following statement: “Our top priorities are always patient safety and clinical quality. Pinnacle Dermatology’s compliance and quality assurance programs lead the industry. We are proud of our track record, our high levels of patient satisfaction and the equally high patient loyalty that results and will continue to provide valuable dermatological care at the highest possible levels.”

Backing Pinnacle is Chicago Pacific Capital, a private equity firm founded in 2014. The firm “invests in companies that it believes are positioned to lead innovations in health-care delivery and in caring for aging populations,” a regulatory filing says. Chicago Pacific had $1.8 billion under management, including borrowings, as of December 2020.

Chicago Pacific didn’t respond to a phone call and a detailed email seeking comment about Pinnacle. 

Brown, the former Pinnacle physician, who has also taught dermatology at two medical schools, said she decided to share her experience at the company out of concern for patient safety. “I worked in an office that was physician-owned until the physician passed away and we were bought out,” Brown said. “I experienced from the inside what happened to the practice” after private equity arrived.  

Among the changes Brown said she saw after Pinnacle took over were an increase in patient biopsies that got lost and a drop in the quality and number of instruments purchased for the practice. She said the office booked her for 40 patient appointments a day without adequate support staff. Brown also described cases of patients were seen multiple times for problems that could have been resolved in single visits, raising the patients’ costs.

Brown says that when private equity firms take over health care practices, it hurts the quality of health care and is bad for patients.
Brown says that when private equity firms take over health care practices, it hurts the quality of health care and is bad for patients. Sarah Rice for NBC News

Even worse, Brown said, patient diagnoses fell through the cracks; for months, the office didn’t follow through on treating a patient’s melanoma, for example. “If you miss a melanoma and you’re not being treated, there could be significant morbidity and mortality with that,” she said.

A letter Brown’s lawyer sent to Pinnacle in the fall of 2020 and reviewed by NBC News detailed her criticisms. Shortly after the letter went out, Brown was let go.

The company contended that she had behaved unethically, Brown said, but she said she and her lawyer obtained her personnel file and found nothing in it to support the claim. “They started targeting me,” Brown said. “They weren’t happy with me sending emails up the chain about stuff going wrong.”