Findings suggest new medical guidelines may be needed to determine when to perform clinical laboratory cancer screenings on people under 50
From 1990-2019, new diagnoses of early-onset cancer in individuals under 50 years of age increased by 79%, according to a British Medical Journal (BMJ) news release describing research published last year in BMJ Oncology. The question for anatomic pathology laboratories to consider is, why are more people under 50 being diagnosed with cancer than in earlier years? And do medical guidelines need to be changed to allow more cancer screening for individuals under 50-years old?
This new revelation challenges previously held beliefs about the number of younger adults under 50 experiencing early-onset cancer. Patients can sometimes miss symptoms by attributing them to a more benign condition.
“While cancer tends to be more common in older people, the evidence suggests that cases among the under 50s have been rising in many parts of the world since the 1990s. But most of these studies have focused on regional and national differences; and few have looked at the issue from a global perspective or the risk factors for younger adults, say the researchers. In a bid to plug these knowledge gaps, they drew on data from the Global Burden of Disease 2019 Study for 29 cancers in 204 countries and regions,” the BMJ news release states.
According to the news release, “Breast cancer accounted for the highest number of ‘early-onset’ cases in this age group in 2019. But cancers of the windpipe (nasopharynx) and prostate have risen the fastest since 1990, the analysis reveals. Cancers exacting the heaviest death toll and compromising health the most among younger adults in 2019 were those of the breast, windpipe, lung, bowel, and stomach.”
Although these statistics are being seen worldwide, the highest rates are in North America, Australasia, and Western Europe. However, high death rates due to cancer are also being seen in Eastern Europe, Central Asia, and Oceania. Economic disparities in the latter geographical regions may account for both fewer diagnoses and higher death rates.
“And in low to middle income countries, early onset cancer had a much greater impact on women than on men, in terms of both deaths and subsequent poor health,” the BMJ news release noted.
In an editorial they published in BMJ Oncology on the study findings, Ashleigh Hamilton, PhD (left), Academic Clinical Lecturer, and Helen Coleman, PhD (right), Professor, School of Medicine, Dentistry and Biomedical Sciences, both at the Center for Public Health at Queen’s University Belfast in the UK wrote, “The epidemiological landscape of cancer incidence is changing. … Prevention and early detection measures are urgently required, along with identifying optimal treatment strategies for early-onset cancers, which should include a holistic approach addressing the unique supportive care needs of younger patients.” Anatomic pathology laboratories will play an important role in diagnosing and treating younger cancer patients. (Photo copyrights: Queen’s University Belfast.)
What Caused the Increase?
“It’s such an important question, and it points to the need for more research in all kinds of domains—in population science, behavioral health, public health, and basic science as well,” said medical oncologist Veda Giri, MD, Professor of Internal Medicine, Yale School of Medicine, in a news release. Giri directs the Yale Cancer Center Early-Onset Cancer Program at Smilow Cancer Hospital.
Although experts are still trying to determine exactly where these cases are coming from, signs point to both genetic and lifestyle factors, the BMJ news releases noted. Tobacco and alcohol use, diets high in cholesterol and sodium, and physical inactivity are all lifestyle risk factors. Experts recommend a healthy diet and exercise routine with minimal alcohol consumption.
As for family history? “We’re beginning to recognize that family history is very important,” says Jeremy Kortmansky, MD, also a Yale Medicine medical oncologist.
According to CNN Health, these rates of early-onset cancer are more common in female patients, with rates going up an average of 0.67% each year.
“For young women who have a significant family history of cancer in the family, we are starting to refer them to a high-risk clinic—even if the cancer in their family is not breast cancer,” Kortmansky noted.
Doctors advise patients to implement healthy habits into their lives, not ignore symptoms, advocate for themselves, and be aware of their family history. Cancer patients may be prescribed cancer treatments at a much earlier age. Medical guidelines for patients may continue to shift and change. And oncologists may be incorporating alternative therapies to help younger patients deal with the shock of their diagnosis.
Will Cancer Rates Continue to Rise?
“Based on the observed trends for the past three decades, the researchers estimate that the global number of new early-onset cancer cases and associated deaths will rise by a further 31% and 21% respectively in 2030, with those in their 40s the most at risk,” the BMJ news release noted.
In an editorial they penned for BMJ Oncology on the findings of the cancer study titled, “Shifting Tides: The Rising Tide of Early-Onset Cancers Demands Attention,” Ashleigh Hamilton, PhD, Academic Clinical Lecturer, and Helen Coleman, PhD, Professor, School of Medicine, Dentistry and Biomedical Sciences, both at the Center for Public Health at Queen’s University Belfast in the UK wrote, “Full understanding of the reasons driving the observed trends remains elusive, although lifestyle factors are likely contributing, and novel areas of research such as antibiotic usage, the gut microbiome, outdoor air pollution, and early life exposures are being explored. It is crucial that we better understand the underlying reasons for the increase in early-onset cancers, in order to inform prevention strategies.”
Clinical laboratories should be aware of these findings and the changing landscape of cancer screenings, as they will play a key role in diagnoses. Younger patients may be advocating for cancer screenings and doctors may be ordering them depending on the patient’s symptoms and family history. Anatomic pathology professionals should expect new guidelines when it comes to cancer diagnostics and treatment.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc.
Automation and AI-Powered Workflow Paves the Way for Consistent, Optimized Molecular Diagnostics and Pathology RCM
Third in a three-part series, this article will discuss how sophisticated revenue cycle management technology, including artificial intelligence (AI) capabilities, drives faster, more efficient revenue reimbursement for molecular and pathology testing.
Financial and operational leaders of molecular testing laboratories and pathology groups are under pressure to maximize the revenue collected from their services rendered. This is no easy task. Molecular claims, in particular, can be especially complex. This article outlines the specific areas in which automation and artificial intelligence (AI)-based workflows can improve revenue cycle management (RCM) for molecular diagnostic and pathology organizations so they can better meet their operational and financial goals.
AI can play a number of important roles in business. When it comes to RCM for diagnostic organizations, first and foremost, AI can inform decision-making processes by generating new or derived data, which can be used in reporting and analytics. It can also help understand likely outcomes based on historical data, such as an organization’s current outstanding accounts receivable (AR) and what’s likely to happen with that AR based on historic performance.
AI is also deployed to accelerate the creation of configurations and workflows. For example, generated or derived data can be used to create configurations within a revenue cycle workflow to address changes or shifts in likely outcomes, such as denial rates. Suppose an organization is using AI to analyze historical denial data and predict denial rates. In that case, changes in those predicted denial rates can be used to modify a workflow to prevent those denials upfront or to automate appeals on the backend. This helps organizations adapt to changes more quickly and accelerates the time to reimbursement.
“Furthermore, AI is used to automate workflows by providing or informing decisions directly,“ says Clarisa Blattner, XiFin Senior Director of Revenue and Payor Optimization. “In this case, when the AI sees shifts or changes, it knows what to do to address them. This enables an organization to take a process in the revenue cycle workflow that is very human-oriented and automate it.”
AI is also leveraged to validate data and identify outcomes that are anomalous, or that lie outside of the norm. This helps an organization:
Ensure that the results achieved meet the expected performance
Understand whether the appropriate configurations are in place
Identify if an investigation is required to uncover the reason behind any anomalies so that they can be addressed
Finally, AI can be employed to generate content, such as letters or customer support materials.
Everything AI starts with data
Everything AI-related starts with the data. Without good-quality data, organizations can’t generate AI models that will move a business forward. In order to build effective AI models, an organization must understand the data landscape and be able to monitor and measure performance and progress and adjust the activities being driven, as necessary.
Dirty, unstructured data leads to unintelligent AI. AI embodies the old adage, “garbage in, garbage out.” The quality of the AI decision or prediction is entirely based on the historical data that it’s seen. If that data is faulty, flawed, or incomplete, it can lead to bad decisions or the inability to predict or make a decision at all. Purposeful data modeling is critical to AI success, and having people and processes that can understand the complicated RCM data and structure it so it can be effectively analyzed is vital to success.
The next step is automation. Having effective AI models that generate strong predictions is only as valuable as the ability to get that feedback into the revenue cycle system effectively. If not, that value is minimal, because the organization must expend a lot of human energy to try to reconfigure or act on the AI predictions being generated.
There is a typical transformation path, illustrated below, that organizations go through to get from having data stored in individual silos to fully embedded AI. If an organization is struggling with aggregating data to build AI models, it’s at stage one. The goal is stage five, where an organization uses AI as a key differentiator and AI is a currency, driving activity.
The transformation starts with structuring data with an underlying data approach that keeps it future-ready. It is this foundation that allows organizations to realize the benefits of AI in a cost-effective and efficient way. Getting the automation embedded in the workflow is the key to getting to the full potential of AI in improving the RCM process.
Real-world examples of how AI and automation improve RCM
One example of how AI can improve the RCM process is using AI to discover complex payer information. One significant challenge for diagnostic service providers is ensuring that the right third-party insurance information for patients is captured. This is essential for clean claims submission. Often, the diagnostic provider is not the organization that actually sees the patient, in which case it doesn’t have the ability to collect that information directly. The organization must rely on the referring physician or direct outreach to the patient for this data when it’s incorrect or incomplete.
Diagnostic providers are sensitive to not burdening referring clients or patients with requests for demographic or payer information. It’s important to make this experience as simple and smooth as possible. Also, insurance information is complicated. A lot of data must be collected or corrected if the diagnostic provider doesn’t have the correct information.
Automating this process is difficult. Frequently, understanding who the payer is and how that payer translates into contracts and mapping within the revenue cycle process requires an agent to be on the phone with the patient. It can be very difficult for a patient to get precise payer plan information from their insurance card without the help of a customer service representative.
This is where AI can help. The goal is to require the smallest amount of information from a patient and be able to verify eligibility through electronic means with the payer. Using optical character recognition (OCR), an organization can take an image of the front and back of a patient’s insurance card, isolate the relevant text, and use an AI model to get the information needed in order to generate an eligibility request and confirm eligibility with that payer.
In the event that taking an image of the insurance card is problematic for a patient, the organization can have the patient walk through a simplified online process, for example, through a patient portal, and provide just a few pieces of data to be able to run eligibility verification and get to confirmed eligibility with the payer.
AI can help with this process too. For example, the patient can provide high-level payer information only, such as the name of the commercial payer or whether the coverage is Medicare or Medicaid, the state the patient resides in, and the subscriber ID and AI can use this high-level data to get an eligibility response and confirmed eligibility.
Once the eligibility response is received, the more detailed payer information can be presented back to the patient for confirmation. AI can map the eligibility response to the appropriate contract or payer plan within the RCM system.
Now that the patient’s correct insurance information is captured, the workflow moves on to collecting the patient’s financial responsibility payment. To do that, the organization needs to be able to calculate the patient’s financial responsibility estimate. The RCM system has accurate pricing information and now has detailed payer and plan information, a real-time eligibility response, as well as test or procedure information. This data can be used to estimate patient financial responsibility.
AI can also be used to address and adapt to changes in ordering patterns, payer responses, and payer reimbursement behavior. The RCM process can be designed to incorporate AI to streamline claims, denials, and appeals management, as well as to assign work queues and prioritize exception processing (EP) work based on the likelihood of reimbursement, which improves efficiency.
One other way AI can help is in understanding and or maintaining “expect” prices—what an organization can expect to collect from particular payers for particular procedures. For contracted payers, contracted rates are loaded into the RCM system. It’s important to track whether payers are paying those contracted rates and whether the organization is receiving the level of reimbursement expected. For non-contracted payers, it’s harder to know what the reimbursement rate will be. Historical data and AI can provide a good understanding of what can be expected. AI can also be used to determine if a claim is likely to be rejected because of incorrect or incomplete payer information or patient ineligibility, in which case automation can be applied to resolve most issues.
Another AI benefit relates to quickly determining the probability of reimbursement and assigning how claims are prioritized if a claim requires intervention that cannot be automated. With AI, these claims that require EP are directed to the best available team member, based on that particular team member’s past success with resolving a particular error type.
The goal with EP is to ensure that the claims are prioritized to optimize reimbursement. This starts with understanding the probability of the claim being reimbursed. An AI model can be designed to assess the likelihood of the claim being reimbursed and the likely amount of reimbursement for those expected to be paid. This helps prioritize activities and optimize labor resources. The AI model can also take important factors such as timely filing dates into account. If a claim is less likely to be collected than another procedure but is close to its timely filing deadline, it can be escalated. The algorithms can be run nightly to produce a prioritized list of claims with assignments to the specific team member best suited to address each error.
AI can also be used to create a comprehensive list of activities and the order in which those activities should be performed to optimize reimbursement. The result is a prioritized list for each team member indicating which claims should be worked on first and which specific activities need to be accomplished for each claim.
Summing it all up, organizations need an RCM partner with a solid foundation in data and data modeling. This is essential to being able to effectively harness the power of AI. In addition, the RCM partner must offer the supporting infrastructure to interface with referring clients, patients, and payers. This is necessary to maximize automation and smoothly coordinate RCM activities across the various stakeholders in the process.
Having good AI and insight into data and trends is important, but the ability to add automation to the RCM process based on the AI really solidifies the benefits and delivers a return on investment (ROI). Analytics are also essential for measuring and tracking performance over time and identifying opportunities for further improvement.
Diagnostic executives looking to maximize reimbursement and keep the cost of collection low will want to explore how to better leverage data, AI, automation, and analytics across their RCM process.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc. Missed the first two articles? www.darkdaily.com
Researchers intend their new AI image retrieval tool to help pathologists locate similar case images to reference for diagnostics, research, and education
Researchers at Stanford University turned to an unusual source—the X social media platform (formerly known as Twitter)—to train an artificial intelligence (AI) system that can look at clinical laboratory pathology images and then retrieve similar images from a database. This is an indication that pathologists are increasingly collecting and storing images of representative cases in their social media accounts. They then consult those libraries when working on new cases that have unusual or unfamiliar features.
The Stanford Medicine scientists trained their AI system—known as Pathology Language and Image Pretraining (PLIP)—on the OpenPath pathology dataset, which contains more than 200,000 images paired with natural language descriptions. The researchers collected most of the data by retrieving tweets in which pathologists posted images accompanied by comments.
“It might be surprising to some folks that there is actually a lot of high-quality medical knowledge that is shared on Twitter,” said researcher James Zou, PhD, Assistant Professor of Biomedical Data Science and senior author of the study, in a Stanford Medicine SCOPE blog post, which added that “the social media platform has become a popular forum for pathologists to share interesting images—so much so that the community has widely adopted a set of 32 hashtags to identify subspecialties.”
“It’s a very active community, which is why we were able to curate hundreds of thousands of these high-quality pathology discussions from Twitter,” Zou said.
“The main application is to help human pathologists look for similar cases to reference,” James Zou, PhD (above), Assistant Professor of Biomedical Data Science, senior author of the study, and his colleagues wrote in Nature Medicine. “Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing, and education.” Leveraging pathologists’ use of social media to store case images for future reference has worked out well for the Stanford Medicine study. (Photo copyright: Stanford University.)
Retrieving Pathology Images from Tweets
“The lack of annotated publicly-available medical images is a major barrier for innovations,” the researchers wrote in Nature Medicine. “At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter.”
In this case, the goal “is to train a model that can understand both the visual image and the text description,” Zou said in the SCOPE blog post.
“Pathology is perhaps even more suited to Twitter than many other medical fields because for most pathologists, the bulk of our daily work revolves around the interpretation of images for the diagnosis of human disease,” wrote Jerad M. Gardner, MD, a dermatopathologist and section head of bone/soft tissue pathology at Geisinger Medical Center in Danville, Pa., in a blog post about the Pathology Hashtag Ontology project. “Twitter allows us to easily share images of amazing cases with one another, and we can also discuss new controversies, share links to the most cutting edge literature, and interact with and promote the cause of our pathology professional organizations.”
The researchers used the 32 subspecialty hashtags to retrieve English-language tweets posted from 2006 to 2022. Images in the tweets were “typically high-resolution views of cells or tissues stained with dye,” according to the SCOPE blog post.
The researchers collected a total of 232,067 tweets and 243,375 image-text pairs across the 32 subspecialties, they reported. They augmented this with 88,250 replies that received the highest number of likes and had at least one keyword from the ICD-11 codebook. The SCOPE blog post noted that the rankings by “likes” enabled the researchers to screen for high-quality replies.
They then refined the dataset by removing duplicates, retweets, non-pathology images, and tweets marked by Twitter as being “sensitive.” They also removed tweets containing question marks, as this was an indicator that the practitioner was asking a question about an image rather than providing a description, the researchers wrote in Nature Medicine.
They cleaned the text by removing hashtags, Twitter handles, HTML tags, emojis, and links to websites, the researchers noted.
The final OpenPath dataset included:
116,504 image-text pairs from Twitter posts,
59,869 from replies, and
32,041 image-text pairs scraped from the internet or obtained from the LAION dataset.
The latter is an open-source database from Germany that can be used to train text-to-image AI software such as Stable Diffusion.
Training the PLIP AI Platform
Once they had the dataset, the next step was to train the PLIP AI model. This required a technique known as contrastive learning, the researchers wrote, in which the AI learns to associate features from the images with portions of the text.
As explained in Baeldung, an online technology publication, contrastive learning is based on the idea that “it is easier for someone with no prior knowledge, like a kid, to learn new things by contrasting between similar and dissimilar things instead of learning to recognize them one by one.”
“The power of such a model is that we don’t tell it specifically what features to look for. It’s learning the relevant features by itself,” Zou said in the SCOPE blog post.
The resulting AI PLIP tool will enable “a clinician to input a new image or text description to search for similar annotated images in the database—a sort of Google Image search customized for pathologists,” SCOPE explained.
“Maybe a pathologist is looking at something that’s a bit unusual or ambiguous,” Zou told SCOPE. “They could use PLIP to retrieve similar images, then reference those cases to help them make their diagnoses.”
The Stanford University researchers continue to collect pathology images from X. “The more data you have, the more it will improve,” Zou said.
Pathologists will want to keep an eye on the Stanford Medicine research team’s progress. The PLIP AI tool may be a boon to diagnostics and improve patient outcomes and care.
Already-existing clinical laboratory blood test may be new standard for detecting Alzheimer’s biomarkers
In Sweden, an independent study of an existing blood test for Alzheimer’s disease—called ALZpath—determined that this diagnostic assay appears to be “just as good as, if not surpass, lumbar punctures and expensive brain scans at detecting signs of Alzheimer’s in the brain,” according to a report published by The Guardian.
Alzheimer’s disease is one of the worst forms of dementia and it affects more than six million people annually according to the Alzheimer’s Association. Clinical laboratory testing to diagnose the illness traditionally involves painful, invasive spinal taps and brain scans. For that reason, researchers from the University of Gothenburg in Sweden wanted to evaluate the performance of the ALZpath test when compared to these other diagnostic procedures.
Motivated to seek a less costly, less painful, Alzheimer’s biomarker for clinical laboratory testing, neuroscientist Nicholas Ashton, PhD, Assistant Professor of Neurochemistry at the University of Gothenburg, led a team of scientists that looked at other common biomarkers used to identify changes in the brain of Alzheimer’s patients. That led them to tau protein-based blood tests and specifically to the ALZpath blood test for Alzheimer’s disease developed by ALZpath, Inc., of Carlsbad, Calif.
In their JAMA article, they wrote, “the pTau217 immunoassay showed similar accuracies to cerebrospinal fluid biomarkers in identifying abnormal amyloid β (Aβ) and tau pathologies.”
In an earlier article published in medRxiv, Ashton et al wrote, “Phosphorylated tau (pTau) is a specific blood biomarker for Alzheimer’s disease (AD) pathology, with pTau217 considered to have the most utility. However, availability of pTau217 tests for research and clinical use has been limited.”
Thus, the discovery of an existing pTau217 assay (ALZpath) that is accessible and affordable is a boon to Alzheimer’s patients and to the doctors who treat them.
“The ALZpath pTau217 assay showed high diagnostic accuracy in identifying elevated amyloid (AUC, 0.92-0.96; 95%CI 0.89-0.99) and tau (AUC, 0.93-0.97; 95%CI 0.84-0.99) in the brain across all cohorts. These accuracies were significantly higher than other plasma biomarker combinations and equivalent to CSF [cerebrospinal fluid] biomarkers,” an ALZpath press release noted.
“This is an instrumental finding in blood-based biomarkers for Alzheimer’s, paving the way for the clinical use of the ALZpath pTau217 assay,” stated Henrik Zetterberg, MD, PhD (above), Professor of Neurochemistry at the University of Gothenburg and co-author of the study. “This robust assay is already used in multiple labs around the globe.” Clinical laboratories may soon be receiving doctors’ orders for pTau217 blood tests for Alzheimer’s patients. (Photo copyright: University of Gothenburg.)
Study Details
Ashton’s team conducted a cohort study that “examined data from three single-center observational cohorts.” The cohorts included:
“Participants included individuals with and without cognitive impairment grouped by amyloid and tau (AT) status using PET or CSF biomarkers. Data were analyzed from February to June 2023,” the researchers wrote.
These trials from the US, Canada, and Spain featured 786 participants and featured “either a lumbar puncture or an amyloid PET scan to identify signs of amyloid and tau proteins—hallmarks of Alzheimer’s disease,” The Guardian reported, adding that results of the University of Gothenburg’s study showed that the ALZpath pTau217 blood test “was superior to brain atrophy assessments, in identifying signs of Alzheimer’s.”
“80% of individuals could be definitively diagnosed on a blood test without any other investigation,” Ashton told The Guardian.
Diagnosis Needed to Receive Alzheimer’s Disease Treatments
“If you’re going to receive [the new drugs], you need to prove that you have amyloid in the brain,” Ashton told The Guardian. “It’s just impossible to do spinal taps and brain scans on everyone that would need it worldwide. So, this is where the blood test [has] a huge potential.”
Even countries where such drugs were not yet available (like the UK) would benefit, Ashton said, because the test, “Could potentially say that this is not Alzheimer’s disease and it could be another type of dementia, which would help to direct the patient’s management and treatment routine.”
However, Ashton himself noted the limitations of the new findings—specifically that there is no success shown yet in Alzheimer’s drugs being taken by symptom-free individuals.
“If you do have amyloid in the brain at 50 years of age, the blood test will be positive,” he said. “But what we recommend, and what the guidelines recommend with these blood tests, is that these are to help clinicians—so someone must have had some objective concern that they have Alzheimer’s disease, or [that] their memory is declining,” he told The Guardian.
Experts on the Study Findings
“Blood tests could be used to screen everyone over 50-years old every few years, in much the same way as they are now screened for high cholesterol,” David Curtis, MD, PhD, Honorary Professor in the Genetics, Evolution and Environment department at University College London, told The Guardian.
“Results from these tests could be clear enough to not require further follow-up investigations for some people living with Alzheimer’s disease, which could speed up the diagnosis pathway significantly in future,” Richard Oakley, PhD, Associate Director of Research and Innovation at the Alzheimer’s Society, UK, told The Guardian.
Though Oakley found the findings promising, he pointed out what should come next. “We still need to see more research across different communities to understand how effective these blood tests are across everyone who lives with Alzheimer’s disease,” he said.
“Expanding access to this highly accurate Alzheimer’s disease biomarker is crucial for wider evaluation and implementation of AD blood tests,” the researchers wrote in JAMA Neurology.
“ALZpath makers are in discussions with labs in the UK to launch it for clinical use this year, and one of the co-authors, Henrik Zetterberg, MD, PhD, Professor of Neurochemistry at the University of Gothenburg, is making the assay available for research use as part of the ‘biomarker factory’ at UCL,” The Guardian reported.
In the US, to be prescribed any of the available Alzheimer’s medications, a doctor must diagnose that the patient has amyloid in the brain. A pTau217 diagnostic blood test could be used to make such a diagnosis. Currently, however, the test is only available “for research studies through select partner labs,” Time reported.
“But later this month, doctors in the US will be able to order the test for use with patients. (Some laboratory-developed tests performed by certain certified labs don’t require clearance from the US Food and Drug Administration.),” Time added.
It may be that the University of Gothenburg study will encourage Alzheimer’s doctors in the UK and around the world to consider ordering pTau217 diagnostic blood tests from clinical laboratories, rather than prescribing spinal taps and brains scans for their Alzheimer’s patients.
HHS Office of Inspector General was the latest to examine the quality control problems that led to distribution of inaccurate test to clinical laboratories nationwide
Failure on the part of the Centers for Disease Control and Prevention (CDC) to produce accurate, dependable SARS-CoV-2 clinical laboratory test kits at the start of the COVID-19 pandemic continues to draw scrutiny and criticism of the actions taken by the federal agency.
In the early weeks of the COVID-19 pandemic, the CDC distributed faulty SARS-CoV-2 test kits to public health laboratories (PHLs), delaying the response to the outbreak at a critical juncture. That failure was widely publicized at the time. But within the past year, two reports have provided a more detailed look at the shortcomings that led to the snafu.
“We identified weaknesses in CDC’s COVID-19 test kit development processes and the agencywide laboratory quality processes that may have contributed to the failure of the initial COVID-19 test kits,” the OIG stated in its report.
Prior to the outbreak, the agency had internal documents that were supposed to provide guidance for how to respond to public health emergencies. However, “these documents do not address the development of a test kit,” the OIG stated.
“If the CDC can’t change, [its] importance in health in the nation will decline,” said microbiologist Jill Taylor, PhD (above), Senior Adviser for the Association of Public Health Laboratories in Washington, DC. “The coordination of public health emergency responses in the nation will be worse off.” Clinical laboratories that were blocked from developing their own SARS-CoV-2 test during the pandemic would certainly agree. (Photo copyright: Columbia University.)
Problems at the CDC’s RVD Lab
Much of the OIG’s report focused on the CDC’s Respiratory Virus Diagnostic (RVD) lab which was part of the CDC’s National Center for Immunization and Respiratory Diseases (NCIRD). The RVD lab had primary responsibility for developing, producing, and distributing the test kits. Because it was focused on research, it “was not set up to develop and manufacture test kits and therefore had no policies and procedures for developing and manufacturing test kits,” the report stated.
The RVD lab also lacked the staff and funding to handle test kit development in a public health emergency, the report stated. As a result, “the lead scientist not only managed but also participated in all test kit development processes,” the report stated. “In addition, when the initial test kit failed at some PHLs, the lead scientist was also responsible for troubleshooting and correcting the problem.”
To verify the test kit, the RVD lab needed samples of viral material from the agency’s Biotechnology Core Facility Branch (BCFB) CORE Lab, which also manufactured reagents for the kit.
“RVD Lab, which was under pressure to quickly create a test kit for the emerging health threat, insisted that CORE Lab deviate from its usual practices of segregating these two activities and fulfill orders for both reagents and viral material,” the report stated.
This increased the risk of contamination, the report said. An analysis by CDC scientists “did not determine whether a process error or contamination was at fault for the test kit failure; however, based on our interviews with CDC personnel, contamination could not be ruled out,” the report stated.
The report also cited the CDC’s lack of standardized systems for quality control and management of laboratory documents. Labs involved in test kit development used two different incompatible systems for tracking and managing documents, “resulting in staff being unable to distinguish between draft, obsolete, and current versions of laboratory procedures and forms.”
Outside Experts Weigh In
The OIG report followed an earlier review by the CDC’s Laboratory Workgroup (LW), which consists of 12 outside experts, including academics, clinical laboratory directors, state public health laboratory directors, and a science advisor from the Association of Public Health Laboratories. Members were appointed by the CDC Advisory Committee to the Director.
This group cited four major issues:
Lack of adequate planning: For the “rapid development, validation, manufacture, and distribution of a test for a novel pathogen.”
Ineffective governance: Three labs—the RVD Lab, CORE Lab, and Reagent and Diagnostic Services Branch—were involved in test kit development and manufacturing. “At no point, however, were these three laboratories brought together under unified leadership to develop the SARS-CoV-2 test,” the report stated.
Poor quality control and oversight: “Essentially, at the start of the pandemic, infectious disease clinical laboratories at CDC were not held to the same quality and regulatory standards that equivalent high-complexity public health, clinical and commercial reference laboratories in the United States are held,” the report stated.
Poor test design processes: The report noted that the test kit had three probes designed to bind to different parts of the SARS-CoV-2 nucleocapsid gene. The first two—N1 (topology) and N2 (intracellular localization)—were designed to match SARS-CoV-2 specifically, whereas the third—N3 (functions of the protein)—was designed to match all Sarbecoviruses, the family that includes SARS-CoV-2 as well as the coronavirus responsible for the 2002-2004 SARS outbreak.
The N1 probe was found to be contaminated, the group’s report stated, while the N3 probe was poorly designed. The report questioned the decision to include the N3 probe, which was not included in European tests.
Also lacking were “clearly defined pass/fail threshold criteria for test validation,” the report stated.
Advice to the CDC
Both reports made recommendations for changes at the CDC, but the LW’s were more far-reaching. For example, it advised the agency to establish a senior leader position “with major responsibility and authority for laboratories at the agency.” This individual would oversee a new Center that would “focus on clinical laboratory quality, laboratory safety, workforce training, readiness and response, and manufacturing.”
In addition, the CDC should consolidate its clinical diagnostic laboratories, the report advised, and “laboratories that follow a clinical quality management system should have separate technical staff and space from those that do not follow such a system, such as certain research laboratories.”
The report also called for collaboration with “high functioning public health laboratories, hospital and academic laboratories, and commercial reference laboratories.” For example, collaborating on test design and development “should eliminate the risk of a single point of failure for test design and validation,” the LW suggested.
CBS News reported in August that the CDC had already begun implementing some of the group’s suggestions, including agencywide quality standards and better coordination with state labs.
However, “recommendations for the agency to physically separate its clinical laboratories from its research laboratories, or to train researchers to uphold new quality standards, will be heavy lifts because they require continuous funding,” CBS News reported, citing an interview with Jim Pirkle, MD, PhD, Director, Division of Laboratory Sciences, National Center for Environmental Health, at the CDC.
By emphasizing HPV vaccinations while having clinical laboratories continue to perform Pap smears, Australia’s rate of cervical cancer has dropped notably
There is currently a global push to completely eradicate cervical cancer and Australia is leading the way with increased funding. It is also focusing on hard-to-reach and underserved populations. Australia is hoping to be first in the world to accomplish this feat by 2035.
For a number of decades, the Pap smear has been the primary screening tool for cervical cancer, as most pathologists and clinical laboratory managers know. However, today it plays a lesser role due to the effectiveness of HPV (human papillomavirus) diagnostic testing, which was put into cervical cancer screening guidelines in 2004.
Then came the first HPV vaccine in 2006. Australia was one of the first nations to implement HPV vaccination programs. By 2010, Australia was working to vaccinate every child. Now, 14 years later, the pool of adults vaccinated against HPV in that nation is causing the rates of cervical cancer to fall.
That means much less cervical cancer test volume for cytotechnologists and cytopathologists, freeing them up to devote their skills to other diagnostic tests.
As the country continues to funnel resources into hitting a zero cancer status, the additional drive will “connect Australia’s world-leading cervical cancer expertise with governments across the region to get HPV vaccine programs up and running, expand screening and treatment, and build health workforce capacity,” said Australia’s Minister for Foreign Affairs office in a press release.
“Australia has always punched above its weight when it comes to cervical cancer, and now Australia is on track to be the first country in the world to eliminate this deadly disease,” said Hon Ged Kearney, MP, RN (above), Assistant Minister for Health and Aged Care and a member of the government’s House of Representatives, in a press release. “By supporting the Pacific and Southeast Asia region [to] eliminate cervical cancer, we are another step closer to ridding the world of this disease.” Clinical laboratories and cytopathologists may soon see less reliance on Pap smears for screening and a shift toward HPV vaccinations to lower the rate of cervical cancer in the US as well. (Photo copyright: Australian Labor Party.)
90% of eligible people will be vaccinated against HPV (including girls and boys).
70% of eligible people will be screened every five years.
95% of eligible people will receive the best possible treatment for precancer and cancer.
In addition to $48.2 million in funding over four years, the program provides:
On the spot testing of samples in First Nations [aka, First Peoples] communities, allowing immediate follow up.
Support for nurses, First Nations health practitioners, and midwives to request pathology for cervical screening.
Increasing support for GPs to undertake colposcopies.
Helping the Underserved
Reaching a wider audience is a large part of Australia’s focus.
“One of my priorities is to address inequities in our health system. I want to make sure that everyone can get access to screening—and all healthcare—no matter where [they] live,” Kearney added. Among the populations sought are First Nations, LGBTIQA+, disabled individuals, and those living away from large cities.
“$8.3 million has been allocated to implement innovate screening models to support such communities,” the Minister for Foreign Affairs office noted in the press release.
Meeting people where they are, and reaching underserved populations, can make a huge difference, especially considering how cervical cancer affects these people. “First Nations women are almost twice as likely to be diagnosed with cervical cancer and face significant barriers to participating in cervical screening compared to non-indigenous women,” the press release notes.
“These tests allow privacy and help to break down barriers for thousands of people who have never screened—including women who have experienced sexual violence, LGBTIQA+ people, and culturally and linguistically diverse and First Nations communities,” the Minister for Foreign Affairs office stated.
There is hope that the push will cause a great shift to other underserved communities as well.
“A quarter of global cervical cancer cases occur in our region, the Indo-Pacific. Tragically, in the Pacific, women are dying at up to 13 times the rate of women in Australia,” said Penny Wong, Australian Minister for Foreign Affairs, in the press release.
How the US Fares in Cervical Cancer Vaccinations
Australia’s vaccination rates far exceed those in the United States. The US government currently recommends HPV vaccination between the ages of 11-12 years old, though it could be administered starting at age nine.
“HPV vaccination is recommended for all persons through age 26 years who were not adequately vaccinated earlier,” the NIH’s National Cancer Institute (NCI) reports.
For years the standard focus for cervical cancer screening has been on the Pap smear. Data show the US lags behind many countries on the rate of HPV vaccination. NCI data show that, as of 2021, in the US just 58.5% of 13-15 year-olds “had received two or three doses of HPV vaccine as recommended,” NCI reported.
With the US’s standard of care still focused on the Pap smear, patients are beginning their cervical cancer prevention journey at a later age. This is because the preliminary age to get a Pap smear in the US is 21 years old, with follow-up exams every three years, the NCI reported.
Even those in this country who are sexually active are not recommended to get screening earlier than 21.
The NCI recommends HPV testing every five years starting at age 30 until 65, with Pap tests every three years.
Clinical laboratories may soon find that, while the US has been slower to get on board with HPV vaccinations, trends in other nations indicate that this may soon change. The reliance that was once placed on the Pap smears prior to 2000 will likely give way to HPV vaccinations at ages and vaccination rates that mirror programs in countries like Australia—where marked reductions in the rate of cervical cancer demonstrate the effectiveness of a successful HPV vaccination program.
