The authors propose a novel approach in which physicians’ responsibility for inpatient stays is expressed through physician-specific attribution ratios informed by patient characteristics.
ABSTRACT
Objectives: More robust attribution methods are necessary to understand physician-level variation in quality of care across risk-adjusted inpatient measures. We address a gap in the literature involving attribution of physicians to inpatient stays using administrative claims data, in which rule-based methods often inadequately attribute physicians.
Study Design: Methodology comparison study using a cross-section of inpatient stays.
Methods: A novel approach is proposed in which physicians’ relative degrees of responsibility for inpatient stays are expressed through physician-specific attribution ratios informed by existing patient characteristics and comorbidities. Attribution results are compared with the rule-based benchmark method for 7 CMS-defined clinical cohorts, including a COVID-19 cohort.
Results: Using 6,835,460 unique patient encounters during 2020 (n = 136,339 in out-of-sample cohort), the proposed approach favored specialists generally considered responsible for primary clinical conditions when compared with the benchmark. The most salient shift within the acute myocardial infarction (+17.0%), heart failure (+20.2%), and coronary artery bypass graft (+4.0%) cohorts was toward the cardiovascular diseases specialty, and the chronic obstructive pulmonary disease (+24.0%) and pneumonia (+16.2%) cohorts resulted in a shift toward the pulmonary diseases specialty. The COVID-19 cohort resulted in considerable shifts toward infectious diseases and pulmonary diseases specialties (+17.4% and +14.1%, respectively). The stroke cohort experienced a considerable shift toward the neurology specialty (+42.2%).
Conclusions: We provide a robust method to attribute physicians to patients, which is a necessary tool to understand physician-level variation in quality of care within the inpatient acute care setting. The proposed method provides consistency across facilities and eliminates unattributed patients resulting from unsatisfied business rules.
Am J Manag Care. 2022;28(7):e263-e270. https://doi.org/10.37765/ajmc.2022.89185
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Takeaway Points
A novel approach is proposed in which physicians’ responsibility for inpatient stays is expressed through physician-specific attribution ratios informed by patient characteristics.
- The proposed method provides an automated approach to attribute physicians to patients within any system capturing inpatient discharge data.
- The proposed method identifies patients most likely to be within the physicians’ locus of control.
- The proposed method offers health care organizations a consistent tool to more fairly compare outcomes across physicians.
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The health care industry has invested substantially in methods to fairly measure quality of care within and across acute inpatient care settings.1-4 The Yale Center for Outcomes Research and Evaluation (CORE)5,6 and Agency for Health Research and Quality (AHRQ)7 risk-adjusted measure methodologies illustrate the lengths to which measure developers attempt to account for varying clinical and demographic characteristics within a facility’s inpatient population. Although the sophistication of measure development has matured considerably, methods to properly attribute those risk-adjusted outcomes to a physician remain largely underdeveloped.8-10
Best practices for attribution of physicians to patients, and their resulting outcomes, remain a perennial challenge from a quality perspective, as no industry-accepted method of such assignment presently exists.8 A National Quality Foundation (NQF)–commissioned environmental scan of more than 170 proposed or implemented attribution models concluded that “the quality measurement field has not yet determined best practices for attribution models” and that the methods currently employed lacked necessary rigor.9-11
The subject of physician attribution is broad and, as expected, the “appropriate” attribution method will “depend on the purpose, context, and stakeholder perspective.”9 Novel approaches are needed to retrospectively identify responsible physicians, specifically for episodes centered around hospitalizations. An improved retrospective and hospital-centered attribution method is especially necessary given the latest growth of hospital pay-for-performance (P4P) and public reporting programs managed by CMS. These programs include the Hospital Value-Based Purchasing Program (HVBP), Hospital Readmissions Reduction Program (HRRP), Hospital-Acquired Condition Reduction Program, Hospital Inpatient Quality Reporting Program, and Overall Star Rating Program.12,13 Within these programs, there are a variety of acute inpatient claims-based measures, such as the risk-adjusted patient safety indicators (PSIs)3 and Yale CORE 30-day risk-standardized readmissions and mortality measures,1,2 that have significant influence in hospital Medicare reimbursement.12
Prior research has shown that physician involvement has a meaningful impact on a patient’s quality of care and, as such, the identification of physicians most responsible during an inpatient stay is an essential component in quality improvement endeavors.14-16 As a result, there is growing interest among hospital quality administrators in understanding physician-level quality variation for those measures that contribute to overall performance in hospital value-based programs. In its 2016 report discussing attribution approaches and principles, the NQF recommends that such models should “attribute results to entities who can influence care and outcomes.”9 Although the inpatient facility is deemed the accountable entity within CMS hospital P4P programs, knowledge of physician-level quality variation, through improved attribution, is necessary to effect change and to conduct root-cause analyses.
A commonly employed attribution method in the industry is based on the “plurality rule,”17,18 in which the physician with the highest quantity of visits, or highest total cost for a given patient across a clinical episode, is marked as the responsible physician. The plurality method is not dependent on an inpatient stay and further, as observed by Pham et al,19 is largely unaffected by the inclusion of inpatient data. This approach is particularly common for assigning attribution to a primary care provider (PCP) during an episodic period spanning multiple visits. Given the hospital-centric nature of inpatient-based measures within hospital P4P programs, the scope of the data is limited to physicians recorded on singular patient hospitalizations. Further, outcomes typically measured within hospital P4P programs are inpatient outcomes (ie, PSIs) or 30-day outcomes for mortality, readmissions, and complications. The plurality rule for these measures likely produces erroneous attributions, as it is suboptimal for surgical complications, pressure ulcers, or 30-day mortality events to be attributed to the physician with the highest volume of visits within a larger temporal encounter, because the attributed physician is likely to be the patient’s PCP. These findings indicate that the generally accepted method for episodic attribution does not translate well when evaluating outcomes resulting from an inpatient index stay.
Current methods for identifying the responsible physician for outcomes centered around an inpatient index stay are lacking; they rely on business rules that largely neglect the complexity of the patient-physician relationship, as well as of patient characteristics and comorbidities. The novel measure proposed in this study builds upon a broader and more robust set of administrative claims data to produce a more informed estimate of the physician with greatest responsibility within inpatient settings.
METHODS
Data
This study uses data from the Premier Healthcare Database, an all-payer database composed of administrative and utilization data concerning more than 121 million inpatient stays across 700 US hospitals, including more than 10 million stays per year since 2012, totaling approximately 25% of all US discharges.20 The database houses more than 231 million unique patient records, with standard classifications for physician specialties and roles. Comorbidities for each patient encounter were identified through International Classification of Diseases, Tenth Revision (ICD-10) codes having a present-on-admission code of Y (yes) or W (clinically undetermined) and were further mapped to broader AHRQ Clinical Classification Software codes (n = 295).21,22
Specialists unlikely to assume primary responsibility of a patient during an inpatient stay (eg, radiology, anesthesiology, pathology) were excluded.19 Nurse practitioners and physician assistants were further excluded because they provide care under the auspices of licensed medical doctors. Additionally, physicians with anomalous total visit quantities were excluded, as it is the practice of some facilities to create artificial placeholder physicians, accumulating large visit counts, who do not represent practicing physicians. A natural log transformation was applied to physician 2-year inpatient case quantities due to the strong positive skewness of the case count distribution. Physicians with z scores above 3 (0.8%) were excluded.
Features of interest included patient age, sex, primary reason for admission coded as a Medicare Severity–Diagnosis Related Group (herein referred to as DRG), and comorbidities. Patient comorbidities were extracted as binary variables. Additionally, the standardized role and specialty of each physician recorded on inpatient records were extracted.
The patient population was limited to inpatient visits with discharge dates from January 1 to July 31, 2020, to ensure that ICD-10 coding reflected the most current industry practices and procedures, including data for COVID-19. A stratified randomized demo by DRG for each specialty stratum was created totaling 1,695,690 unique patient encounters and 133 strata, out of a superset of 6,835,460 encounters. The data were further randomly divided into derivation (80%; n = 1,356,552 unique patient visits across 700 facilities) and validation (20%; n = 339,138 unique patient visits from the same facilities) cohorts. Both the rule-based and our proposed approaches were applied to a third, out-of-sample cohort composed of 136,339 inpatient encounters between August 1 and September 30, 2020, across 698 distinct acute inpatient facilities (eAppendix Figure 1 [eAppendix available at ajmc.com]).
Physician Attribution Ratio
Patients are often seen by multiple physicians during hospital visits. Physicians are assigned to care for both existing comorbidities and the patient’s primary reason for the current visit, with the physician treating the primary reason for the visit (ie, the DRG) typically assigned as the attributable physician. Some physicians may have large probabilities of being assigned to patients’ visits, regardless of the DRG, based on patient comorbidities. For example, patients with major renal problems may have a high probability of being seen by nephrologists during acute myocardial infarction (AMI)–related hospital stays. However, the DRGs of inpatient stays, instead of comorbidities, should be the primary factors influencing physician attributions.
Additionally, the most likely physicians given the patients’ clinical conditions will tend to be physicians with generalized specialties, such as hospitalists and internists, as they are ipso facto more likely to occur on the patient record, regardless of clinical condition. Such approaches would fail to identify the specialists who actually have the major responsibility for treating the driving clinical conditions of the patients. Hence, absolute measures of physician probabilities to be assigned to a patient may not be as informative as relative increases of those physician probabilities upon identifying the patient’s DRG for the current visit. In the previous example, the cardiovascular surgeon’s probability will increase drastically whereas the nephrologist’s probability may remain at high levels but with lower changes before and after the AMI episode.
The proposed measure, a physician attribution ratio (PAR), utilizes 2 probabilities that are estimated across all physicians who see the patient during the visit: (1) probability (P) of the physician being assigned, given the patient’s demographic characteristics and comorbid conditions (prior to the patient’s visit, denoted as Fi); and (2) updated probability (P*) of the physician being assigned, given the patient’s DRG, demographic characteristics, and comorbid conditions.
These probabilities are estimated through multivariate mixed-effects logistic regression models, and they are combined in patient (i)-physician (j)–specific ratios (see the equation below). Further model and PAR details are provided in the eAppendix.
Logistic models are stratified such that a separate model stratum exists for each of the 133 possible standard specialties (binary responses). For each model stratum, a stratified random demo by DRG was extracted to ensure representation of the full range of coded clinical conditions and procedures. Analyses were conducted using the lme4 package23 in R statistical software version 3.6.2 (R Foundation for Statistical Computing).
Our proposed method adjusts for differences between absolute and relative physician relevance by formulating patient- and physician-specific attribution ratios that account for relative proportional increases in physician probabilities. Proportional increases in physicians’ probabilities (before and after identification of the DRG of the inpatient visit) are compared through the PARs with respect to a reference physician across visiting physicians. PARs larger than 1 indicate that the physician’s specialty has a higher relative increase in relevance compared with a reference physician, driven by the patient’s clinical condition (ie, DRG), while accounting for the patient’s characteristics and comorbidities. The physician with the largest relative proportional increase (ie, largest PAR) is assigned as the responsible physician.
Performance Comparison
Physician attribution results that were derived from our proposed method were evaluated against a commonly employed rule-based attribution approach, in which responsibility is assigned to the attending physician for medical cases and the (principal) surgeon for surgical cases.11 Although our proposed approach produces additional granularity (attribution ranking across physicians), for the purpose of the comparative analysis, the physician with the highest PAR is attributed to the patient so that a singular assignment can be compared between the 2 approaches (see eAppendix for details).
We evaluated differences between the rule-based method and our proposed PAR approach for each of the CMS clinical cohorts used within the 30-day mortality and readmissions measures included in the HVBP, HRRP, and CMS Overall Star Rating programs, as well as a COVID-19 cohort. These include AMI, coronary artery bypass graft (CABG), chronic obstructive pulmonary disease (COPD), COVID-19, heart failure (HF), pneumonia (PN), stroke (STK), and total hip arthroplasty/total knee arthroplasty (THA/TKA).
RESULTS
Table 1 illustrates PAR calculations for 4 patient encounters from the AMI, COVID-19, STK, and THA/TKA cohorts. specialists most likely to be responsible for the care of the patient given the patient DRG, although not necessarily the ones selected under the rule-based approach, are associated with higher PAR values. Whereas rule-based methods overweigh hospitalists or internists, under our proposed PAR-based method, the cardiovascular diseases (CD) physician is attributed to the AMI patient, the neurologist is attributed to the STK patient, the orthopedic surgeon is attributed to the THA/TKA patient, and the pulmonary diseases (PD) physician is attributed to the COVID-19 patient. Performance across the 133 strata (ie, specialties) resulted in a mean area under the curve of 0.759 (95% CI, 0.738-0.780), demonstrating substantial differences between the 2 approaches.
Performance Comparison: PAR vs Rule-Based Approach
An out-of-sample cohort of 136,339 patient visits was defined across the following clinical cohorts: AMI (n = 14,918); CABG (n = 4896); COPD (n = 9882); COVID-19 (n = 47,211); HF (n = 14,817); PN (n = 7701); STK (n = 21,836); and THA /TKA (n = 15,078). We estimated physician attributions given the scope of data recorded in patient records. For example, 2349 of the 14,918 (15.7%) AMI patient visits evaluated in our demo had no recorded physician with a specialty related to cardiology. In the absence of a specialist, it was not uncommon to see internists or hospitalists attributed to AMI patients. All physicians on the patient record were evaluated to identify the most probable responsible physician so that no patient encounter was left unattributed.
A common challenge with the rule-based approach is the occurrence of orphaned records, in which no attributed physician is identified through the attribution rules. The stroke cohort was most affected by orphaned records with a 1.8% orphaning rate (n = 397), followed by the PN cohort with a 0.7% orphaning rate (n = 53). Details are provided in Table 2.
The degree of discord between the 2 methods is affected by the clinical cohort that is being evaluated. To measure the range of discord, a match rate, defined as total attribution records in agreement between the 2 methods over the total encounters, was calculated for each facility and cohort. Table 3 exhibits the match rate means and 95% CIs by clinical cohort across all hospitals included in the third, out-of-sample cohort. Match rates ranged from 46.2% (PN) to 96.1% (THA/TKA).
Table 4 [part A and part B] lists the top 5 specialties by cohort, using the rule-based method first and then the resulting shifts with the proposed PAR-based approach. Shifts toward specialists are seen across all cohorts. The most salient shifts within the AMI (+17.0%), CABG (+4.0%), and HF (+20.2%)cohorts were toward the CD specialty, and the COPD (+24.0%) and PN (+16.2%) cohorts resulted in shifts toward the PD specialty. The COVID-19 cohort resulted in a considerable shift toward the infectious diseases (IF) and PD specialties (+17.4% and +14.1%, respectively), with IF attributions almost negligible under the rule-based approach. The most and least affected cohorts were STK (+42.2% shift to neurology) and THA/TKA (+0.5% shift to orthopedic surgery), respectively.
The Figure illustrates physician attribution shifts of 500 encounters or more from the rule-based approach to the PAR-based approach for the COVID-19, out-of-sample cohort. The largest shift within this cohort was toward the IF physician, drawing largely from the hospitalist (n = 4207), internal medicine (n = 2692), and family practice (n = 579) specialties. A meaningful shift toward the PD physician can also be observed, pulling away from the hospitalist (n = 3106), internal medicine (n = 2708), and family practice (n = 572) specialties.
DISCUSSION
Because plurality-based methods attribute physicians by calculating the one with the greatest quantity of visits or the highest total cost across visits, they are not suitable for identifying responsibility within the scope of a single inpatient stay. This study demonstrated the value of a novel method to identify physician attribution for patient outcomes during inpatient stays. The PAR approach provided a standardized method to attribute patient outcomes across a complete census of hospital inpatients by leveraging readily available data and an unbiased methodology, further allowing users of this methodology to fairly compare physician performance within and across hospitals and health systems.
Medical (vs surgical) cohorts are most affected by this PAR method, indicating that physicians assigned to the attending role are more likely to provide generalized care and are not the specialists of interest who would assume overall clinical responsibility of the patient. The PAR method proposed in this study improves upon rule-based attribution assignment by identifying those specialists who most closely manage the care of a hospitalized patient. Less discord can be observed with surgical cohorts, such as THA/TKA encounters, as physicians in the principal surgeon role are generally specialists who are, indeed, responsible for the primary condition of the patient.
The impact of enhanced attribution results will fluctuate depending on the operational practices to assign roles within individual facilities. For example, some hospitals may choose to assign all specialists to a consulting role (regardless of their degree of contact with the patient), whereas hospitalists are assigned the attending role. Other systems may choose to identify specialists as acting in the attending role.
Moreover, the PAR method improves the integrity of comparative evaluation of physicians by eliminating unattributed patients, as is seen with the rule-based approach, and provides a consistent approach to attribution regardless of the hospital-specific practices for coding physician roles.
Study Limitations and Future Research
The PAR methodology was demonstrated in our motivating example using a small set of commonly available patient-level characteristics so that PARs can be estimated in most real-world settings. The conditioning variables in the model could be further expanded in future research to enhance the estimation of the PAR’s conditional probability inputs. Additional patient-level fixed effects could be incorporated, and facility, health care system, and/or physician-specific random effects could also be added to enhance the estimation of the conditional probabilities in future research. The addition of or stratification by conditioning variables that are not common to all patients would, however, limit the usability of PARs in new settings in which those variables may not yet be available for a sufficient number of patients (eg, facility-based random effects for new/low-volume facilities).
The PAR method is based on administrative data, which rely on consistent documentation by clinicians in medical records and coding practices across multiple institutions. This is a limitation facing all clinically abstracted data and, although coding practices can vary by institution, the impact of such variability should be minor given the large study size and could be further mitigated in practice through intrainstitution clustering. Natural expansions of our approach could include a prospective form of attribution to be used at the point of care and variations of the model specific to targeted outcome measures.
Although the PAR approach identifies physicians assuming overall responsibility for an inpatient encounter, the desired attribution decision might vary according to the specific outcome being measured (eg, mortality, readmissions, patient experience, length of stay, complications, and cost).9 For example, an orthopedic surgeon might assume overall responsibility for a patient undergoing THA; however, an occurrence of deep vein thrombosis may be a result of care provided by the hospitalist or nursing team. A discharging internal medicine physician may be responsible for poor discharge instructions for a patient with AMI, rather than a cardiologist assuming overall responsibility. Therefore, in the evaluation of the length-of-stay measure, a hospitalist might be more appropriately attributed.
Despite the limitations, the PAR method has significant advantages over current attribution methods. It provides an automated approach that can be used with any system capturing inpatient discharge data, it identifies patients most likely to be within the physicians’ locus of control for all patients’ visits, and it offers health care organizations a tool to more fairly compare outcomes across physicians, allowing for both retrospective and live attribution.
CONCLUSIONS
The PAR-based method introduced in this study identifies and ranks physician specialties, based on their increased relevance due to the DRG driving the patient’s visit, that are likely to provide specialized care unique to the patient’s clinical condition(s), while accounting for patient-specific characteristics and comorbidities. Our results demonstrate that, compared with a rule-based method for inpatient attribution, the PAR-based method favors the specialist most likely to be responsible for the overall care of the patient during the inpatient stay.
Acknowledgments
Dena Richardson, MBA; Darla Belt, MSN; Patti Hollifield, MS; and Jennifer Pack, MHA, iteratively reviewed the results of the analyses and provided clinical guidance.
Author Affiliations: ITS Data Science, Premier Inc (MK, JM), Charlotte, NC; Independent Researcher (GM), Charlotte, NC; formerly of Baptist Memorial Healthcare (HS), Memphis, TN; Department of Public Health Sciences and School of Data Science, University of North Carolina at Charlotte (LHG), Charlotte, NC; School of Public Health, Faculty of Medicine, Imperial College London (LHG), London, UK.
Source of Funding: The authors received no specific funding for this work.
Author Disclosures: Mr Korvink reports that the model discussed in this paper will be employed within a commercial product. Dr Martin is employed by and has stock in Premier Inc. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (MK, GM, HS, LHG); acquisition of data (MK); analysis and interpretation of data (MK, GM, LHG); drafting of the manuscript (MK, GM, LHG); critical revision of the manuscript for important intellectual content (MK, GM, JM, HS, LHG); statistical analysis (MK); provision of patients or study materials (MK); administrative, technical, or logistic support (JM); and supervision (JM, HS).
Address Correspondence to: Michael Korvink, MA, ITS Data Science, Premier Inc, 13034 Ballantyne Corporate Pl, Charlotte, NC 28277. Email: michael_korvink@premierinc.com.
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Within minutes of the final heartbeat, a cascade of biochemical events triggered by a lack of blood flow, oxygen and nutrients begins to destroy a body’s cells and organs. But a team of Yale scientists has found that massive and permanent cellular failure doesn’t have to happen so quickly.
Using a new technology the team developed that delivers a specially designed cell-protective fluid to organs and tissues, the researchers restored blood circulation and other cellular functions in pigs a full hour after their deaths, they report in the Aug. 3 edition of the journal Nature.
The findings may help extend the health of human organs during surgery and expand availability of donor organs, the authors said.
“All cells do not die immediately, there is a more protracted series of events,” said David Andrijevic, associate research scientist in neuroscience at Yale School of Medicine and co-lead author of the study. “It is a process in which you can intervene, stop, and restore some cellular function.”
The research builds upon an earlier Yale-led project that restored circulation and certain cellular functions in the brain of a dead pig with technology dubbed BrainEx. Published in 2019, that study and the new one were led by the lab of Yale’s Nenad Sestan, the Harvey and Kate Cushing Professor of Neuroscience and professor of comparative medicine, genetics, and psychiatry.
“If we were able to restore certain cellular functions in the dead brain, an organ known to be most susceptible to ischemia [inadequate blood supply], we hypothesized that something similar could also be achieved in other vital transplantable organs,” Sestan said.
In the new study — which involved senior author Sestan and colleagues Andrijevic, Zvonimir Vrselja, Taras Lysyy, and Shupei Zhang, all from Yale — the researchers applied a modified version of BrainEx called OrganEx to the whole pig. The technology consists of a perfusion device similar to heart-lung machines — which do the work of the heart and lungs during surgery — and an experimental fluid containing compounds that can promote cellular health and suppress inflammation throughout the pig’s body. Cardiac arrest was induced in anesthetized pigs, which were treated with OrganEx an hour after death.
Six hours after treatment with OrganEx, the scientists found that certain key cellular functions were active in many areas of the pigs’ bodies — including in the heart, liver, and kidneys — and that some organ function had been restored. For instance, they found evidence of electrical activity in the heart, which retained the ability to contract.
“We were also able to restore circulation throughout the body, which amazed us,” Sestan said.
Normally when the heart stops beating, organs begin to swell, collapsing blood vessels and blocking circulation, he said. Yet circulation was restored and organs in the deceased pigs that received OrganEx treatment appeared functional at the level of cells and tissue.
“Under the microscope, it was difficult to tell the difference between a healthy organ and one which had been treated with OrganEx technology after death,” Vrselja said.
As in the 2019 experiment, the researchers also found that cellular activity in some areas of the brain had been restored, though no organized electrical activity that would indicate consciousness was detected during any part of the experiment.
The team was especially surprised to observe involuntary and spontaneous muscular movements in the head and neck areas when they evaluated the treated animals, which remained anesthetized through the entire six-hour experiment. These movements indicate the preservation of some motor functions, Sestan said.
The researchers stressed that additional studies are necessary to understand the apparently restored motor functions in the animals, and that rigorous ethical review from other scientists and bioethicists is required.
The experimental protocols for the latest study were approved by Yale’s Institutional Animal Care and Use Committee and guided by an external advisory and ethics committee.
The OrganEx technology could eventually have several potential applications, the authors said. For instance, it could extend the life of organs in human patients and expand the availability of donor organs for transplant. It might also be able to help treat organs or tissue damaged by ischemia during heart attacks or strokes.
“There are numerous potential applications of this exciting new technology,” said Stephen Latham, director of the Yale Interdisciplinary Center for Bioethics. “However, we need to maintain careful oversight of all future studies, particularly any that include perfusion of the brain.”
Reference: Andrijevic D, Vrselja Z, Lysyy T, et al. Cellular recovery after prolonged warm ischaemia of the whole body. Nature. 2022:1-8. doi: 10.1038/s41586-022-05016-1
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On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) a global pandemic. The WHO was too late. Travel bans and lockdowns followed, but COVID had breached containment. Two-plus years of pain—physical, emotional and economic—ensued, injuring individuals, businesses and economies worldwide.
During COVID’s twists and turns, decision makers, including supply chain professionals, acted like deer caught in the headlights: Startled, vulnerable, they froze. So did global supply chains. As if struck by an unyielding force, the global economy staggered. Eventually, COVID became endemic, supply chains thawed and the global economy rebounded. Many inquired: “Why was the COVID response so hard?”
We have another question: After a half dozen infectious-disease events since SARS in 2003, why didn’t we see COVID coming? Once spotted, why didn’t we sense the nature of COVID’s threat earlier? The answer stares us in the face: Our sensing abilities, at all levels, are under-evolved.
Critically, we don’t just miss disruptions—large and small—we often fail to get out in front of emerging trends, and we seldom sense and make sense of changing competitive rules. Do you remember Blockbuster, Compaq Computer or PanAm? Each was an industry leader killed off by a disruptive marketplace.
Now, a little good news: The deer-in-the-headlights idiom offers hurry insight into how to Improve our sensing abilities to achieve actionable awareness. Let’s take a closer look.
The origins of the idiom
Have you ever tried to sneak up on a deer in the wild? It’s quite the impossible task. Deer possess highly evolved senses. Their eyes, ears and nose keep them fully aware of their setting. The eyes are especially well adapted for survival. You may know that as a prey species, a deer’s eyes are widely spaced. They can spot and track movement across a 310° field of view. But do you know the rest of the story?
On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) a global pandemic. The WHO was too late. Travel bans and lockdowns followed, but COVID had breached containment. Two-plus years of pain—physical, emotional and economic—ensued, injuring individuals, businesses and economies worldwide.
During COVID’s twists and turns, decision makers, including supply chain professionals, acted like deer caught in the headlights: Startled, vulnerable, they froze. So did global supply chains. As if struck by an unyielding force, the global economy staggered. Eventually, COVID became endemic, supply chains thawed and the global economy rebounded. Many inquired: “Why was the COVID response so hard?”
We have another question: After a half dozen infectious-disease events since SARS in 2003, why didn’t we see COVID coming? Once spotted, why didn’t we sense the nature of COVID’s threat earlier? The answer stares us in the face: Our sensing abilities, at all levels, are under-evolved.
Critically, we don’t just miss disruptions—large and small—we often fail to get out in front of emerging trends, and we seldom sense and make sense of changing competitive rules. Do you remember Blockbuster, Compaq Computer or PanAm? Each was an industry leader killed off by a disruptive marketplace.
Now, a little good news: The deer-in-the-headlights idiom offers hurry insight into how to Improve our sensing abilities to achieve actionable awareness. Let’s take a closer look.
The origins of the idiom
Have you ever tried to sneak up on a deer in the wild? It’s quite the impossible task. Deer possess highly evolved senses. Their eyes, ears and nose keep them fully aware of their setting. The eyes are especially well adapted for survival. You may know that as a prey species, a deer’s eyes are widely spaced. They can spot and track movement across a 310° field of view. But do you know the rest of the story?
July 7, 2022
Bookmark synchronization has become a standard feature in modern browsers: It gives Internet users a way to ensure that the changes they make to bookmarks on a single device take effect simultaneously across all their devices. However, it turns out that this same helpful browser functionality also gives cybercriminals a handy attack path.
To wit: Bookmarks can be abused to siphon out reams of stolen data from an enterprise environment, or to sneak in attack tools and malicious payloads, with little risk of being detected.
David Prefer, an academic researcher at the SANS Technology Institute, made the discovery as part of broader research into how attackers can abuse browser functionality to smuggle data out from a compromised environment and carry out other malicious functionality.
In a latest technical paper, Prefer described the process as "bruggling" — a portmanteau of browser and smuggling. It's a novel data exfiltration vector that he demonstrated with a proof-of-concept (PoC) PowerShell script called "Brugglemark" that he developed for the purpose.
The Fine Art of Bruggling
"There's no weakness or vulnerability that is being exploited with the synchronization process," Prefer stresses. "What this paper hones in on is the ability to name bookmarks whatever you want, and then synchronize them to other signed-in devices, and how that very convenient, helpful functionality can be twisted and misused in an unintended way."
An adversary would already need access — either remote or physical — to the environment and would have already infiltrated it and collected the data they want to exfiltrate. They could then either use stolen browser synchronization credentials from a legitimate user in the environment or create their own browser profile, then access those bookmarks on another system where they've been synchronized to access and save the data, Prefer says. An attacker could use the same technique to sneak malicious payloads and attack tools into an environment.
The benefit of the technique is, put simply, stealth.
Johannes Ullrich, dean of research at the SANS Institute, says data exfiltration via bookmark syncing gives attackers a way to bypass most host and network-based detection tools. To most detection tools, the traffic would appear as normal browser synch traffic to Google or any other browser maker. "Unless the tools look at the volume of the traffic, they will not see it," Ullrich says. "All traffic is also encrypted, so it is a bit like DNS over HTTPs or other 'living off the cloud' techniques," he says.
Bruggling in Practice
In terms of how an attack might be carried out in the real world, Prefer points to an example where an attacker might have compromised an enterprise environment and accessed sensitive documents. To exfiltrate the data via bookmark synching, the attacker would first need to put the data into a form that can be stored as bookmarks. To do this, the adversary could simply encode the data into base64 format and then split the text into separate chunks and save each of those chunks as individual bookmarks.
Prefer discovered — through trial and error — that modern browsers allow a considerable number of characters to be stored as single bookmarks. The real number varied with each browser. With the Brave browser, for example, Prefer discovered he could synchronize, very quickly, the entirety of the book Brave New World using just two bookmarks. Doing the same with Chrome required 59 bookmarks. Prefer also discovered during testing that browser profiles could synchronize as many as 200,000 bookmarks at a time.
Once the text has been saved as bookmarks and synchronized, all that the attacker would need to do is sign into the browser from another device to access the content, reassemble it, and decode it from base64 back into the original text.
"As for what kind of data could be exfiltrated via this technique, I think that's up to the creativity of an adversary," Prefer says.
Prefer's research was primarily focused on browser market share leader Google Chrome — and to a lesser extent on other browsers such as Edge, Brave, and Opera, which are all based on the same open source Chromium project that Chrome is built upon. But there's no reason why bruggling won't work with other browsers such as Firefox and Safari, he notes.
Other Use Cases
Significantly, bookmark syncing is not the only browser function that can be abused this way, Prefer says. "There are plenty of other browser features that are used in synchronization that could be misused in a similar way, but would require research to investigate," he says. As examples, he points to autofills, extensions, browser history, stored passwords, preferences, and themes, which can all be synchronized. "With a bit of research, it might turn out that they can also be abused," Prefer says.
Ullrich says Prefer's paper was inspired by earlier research that showed how browser extension syncing could be used for data exfiltration and command and control. With that method, however, a victim would have been required to install a malicious browser extension, he says.
Mitigating the Threat
Prefer says organizations can mitigate the risk of data exfiltration by disabling bookmark syncing using Group Policy. Another option would be to limit the number of email domains that are allowed to sign in for syncing, so attackers would not be able to use their own account to do it.
"[Data loss protection] DLP monitoring that an organization already performs can be applied here as well," he says.
Bookmark syncing would not work very well if the syncing happened at a slower speed, Ullrich says. "But being able to sync 200,000+ bookmarks, and only seeing some speed throttling after 20,000 or 30,000 bookmarks makes this [very] valuable," he says.
Thus, browser makers can make things harder for attackers for instance by dynamically throttling bookmark syncing based on factors like the age of an account or logins from a new geographic location. Similarly, bookmarks that contain base64 encoding could be prevented from syncing, as well as bookmarks with excessive names and URLs, Prefer says.
If you've finally decided to finish that novel you've had in mind, there's an event beginning from today (July 1) that could help you to finally finish the idea you've most likely had for years, alongside some helpful apps on Windows, macOS and iOS.
Camp NaNoWriMo (opens in new tab) is an event held in April and July every year, where budding writers can start and finish a project in one month. And the best thing for this type of NaNoWriMo event, is that there's no set word count - you set it.
You can announce your project and then join in with certain writer groups (opens in new tab) to brainstorm ideas about it - from plot points to characters.
Then the crucial point is - writing it! However, it may be overwhelming to pick the best apps to get it done. With this in mind, we've put together a bunch of apps that can best help you put that project over the finish line by the time July ends.
Scrivener
As we've rated it one of the best writing apps out there, it's no surprise that Scrivener is a great app to use for NaNoWriMo.
Created by Literature and Latte, it's been designed for writers in mind, with ways to lay out your chapters, your research, and jot down any notes if extra inspiration comes to mind across the month.
However, the only feature that lets Scrivener down is that you can't use iCloud to sync your novel between Apple devices. The only way is to use Dropbox (opens in new tab), which could be a pain if you don't have an account. Fortunately, creating one is free, so if you want to manage your novel between your Mac and iPad, this will be the only way to do it.
Scrivener is available for $49.99 / £47.99 / AU$51.99 for a one-off fee on macOS and Windows but there is a free trial you can download. There are also iPad and iPhone versions at $19.99 / £19.99 / AU$21.99 (opens in new tab). It's an all-in-one suite where you don't just plan out what you're going to write, but how you're going to write it.
Ulysses
For iPadOS users, Ulysses has been the go-to in writing novels on the tablet, even though it's also available on macOS and iOS as a monthly or yearly subscription, with prices starting at $5.99 / £4.99 / AU$6.49 (opens in new tab).
If you only have an iPad though, this app is going to be crucial in completing NaNoWriMo. You can set goals for each chapter that can be split into word or timed goals, and there's a great export option that can easily allow you to convert it into an ePub or PDF format.
However, we have a great guide from a published author who uses Ulysses for his novels that can give you some helpful pointers for your project.
Author
While this may not be to everyone's taste, Author by Frode Hegland (opens in new tab) is still the existing entry in our best writing apps guide, and there's good reason as to why, especially for NaNoWriMo.
Available for $29.99 / £29.99 / AU$31.99 (opens in new tab), Author features a great mind-mapping tool (opens in new tab) that can help you visualize and lay down all your ideas for the novel throughout the month, and you can get back to them for when you reach certain chapters or plot points for your characters.
There is a free version of Author (opens in new tab) so you can see if it fits your workflow, but it won't export your project.
Granted, it's only available on macOS, but if you're struggling to lay down how certain cliffhangers will work on a rainy day, Author will be able to help out with this massively.
Things 3
Regardless of which writing app you've picked, it could be a challenge to keep track of your characters, your scenes, and your chapters. This is where a task management app can come in, and Things 3 leads the way in this category.
Available on the iPhone for $9.99 / £9.99 / AU$ 11.99, iPad for $19.99 / £19.99 / AU$24.99, and Mac that’s priced at $49.99 / £29.99 / AU$ 59.99, you'll be able to use the app on whichever device you think will be best to plan out your book.
You can create projects with certain reminders, where they can show as a widget on your iPad, or as a notification on your macOS dock. If there's something you plan to do later in the month but not sure when, you can throw it into the 'Someday' option, and it'll remind you toward the end of the month, but Things will decide when.
The design of it is super simple, made up of a sidebar and a sheet, so even if you just want to jot down some quick reminders, Things 3 can do this.
Dr. Theo Roth completed his medical scientist training at the University of California, San Francisco. During this time, Roth developed a novel method that enables the large-scale genomic engineering of primary immune cells without the need for complex viral vectors – non-viral genome targeting. Roth also further refined targeted pooled knock-in screening to rewire immune cell genomes.
In a latest Teach Me in 10 episode, Roth discussed what targeted pooled knock-in screens are, how he is working to accelerate the discovery of cell-based therapies and how this process could reduce the time it takes to transition these therapies from the lab to the clinic.
“What we try to do in the lab is develop new tools that can accelerate the process of discovering new drugs, new medicines for patients,” ‒ Roth.
Cell therapies
Roth’s research focuses on developing cell therapies, more specifically, modifying the genome of human immune cells to establish if these changes Improve their ability to kill cancer cells when they are reintroduced into the patient.
Cell therapy is defined as the introduction of intact live cells into a patient to help lessen or cure a disease. Immune cell therapies for cancer rely on the generation of antigen-specific T cells that can be used to direct an immune attack on a tumor. Despite many clinical successes, there are some limitations to these cell therapies. For example, CAR T cells are not effective against solid tumors, as they often become dysfunctional when faced with the immunosuppressive tumor microenvironment.
To develop cell therapies with increased therapeutic potential, the genomes of the cells can be modified, but testing the effect of inserted modifications can be very time-consuming, (for example, if only one modification is tested at one time) and there is no ensure that the desired therapeutic outcome will be achieved.
“There’s a huge pipeline of different steps that are required to develop a new drug,” notes Roth. His latest work, which was published in Cell, focuses on the early phases of the drug development pipeline, specifically which type of drug to take forward.
Targeted pooled knock-in screens
The process developed by Roth, targeted pooled knock-in screens, attempts to not only solve the challenge of determining the most promising cell therapy to progress to clinical testing, but can also expedite the testing of various modifications. With pooled knock-in screening, many modifications are made to the genomes of cultured cells ‒ including the introduction of a T-cell receptor that allows the cell to target the cancer cell antigen ‒ and each cell receives a gene that potentially improves cell function. These modified cells make up a cell library, in which every T cell has a different modification. The library can then be used in functional assays to determine the cells’ tumor-killing functionality.
“We start with a healthy human donor. In a therapeutic setting this would be a cancer patient. From that donor we isolate human T cells, which we are able to culture in a dish for about a week or two while we modify those cells to potentially become a therapeutic medicine,” explains Roth. “Instead of testing one cell-based therapy at a time, we can test dozens.”
CRISPR gene editing
As explained by CRISPR Cas9 co-developer and Nobel prize winner Professor Jennifer Doudna, “CRISPR allows scientists to alter the genetic information of any organism including human cells with unprecedented accuracy and efficiency. It is a breakthrough technology that holds huge potential as a strategy to treat and cure diseases, create sustainable biofuels and develop more robust crops.”
Originally discovered as a viral defense system used by bacteria, the CRISPR/Cas9 system is now used for targeted gene editing. Cas9 and guide RNA are used to identify and bind to a specific section of DNA, producing a double-strand DNA break, initiating repair using the new DNA template from the library. This means that during repair, the desired modification is introduced into the DNA.
Roth explains that CRISPR allows for the modification of specific sites within primary immune cell genomes, and is incredibly important as the integration of the new sequences “would only work if we put [the modification] in this one particular site”.
To watch the full Teach Me in 10 video, click here.
Unisexual reproduction lacks meiotic recombination, resulting in the accumulation of deleterious mutations and hindering the creation of genetic diversity. Thus, unisexual taxa are commonly considered an evolutionary dead end.
Intriguingly, some unisexual fishes and reptiles have outlived their predicted extinction time and exhibit high genetic diversity and strong environmental adaptation, yet evolutionary mechanisms underlying unisexual reproductive success generally remain unclear in these vertebrates.
Researchers led by Professor Gui Jianfang from the Institute of Hydrobiology (IHB) of the Chinese Academy of Sciences and their collaborators recently revealed the evolutionary puzzle of unisexual reproductive success in hexaploid gynogenetic gibel carp (Carassius gibelio).
This study was published in Nature Ecology & Evolution.
In this study, the research team sequenced the genomes of sexual tetraploid crucian carp (Carassius auratus) and unisexual hexaploid gibel carp (C. gibelio) and assembled their haplotypes to the chromosome level. Both haplotypes have 50 chromosomes, which comprise two subgenomes (25 chromosomes in subgenome A and 25 chromosomes in subgenome B).
Sequencing coverage analysis reveals that tetraploid C. auratus is an amphidiploid (AABB) with two diploid sets of chromosomes and hexaploid C. gibelio is an amphitriploid (AAABBB) with two triploid sets of chromosomes.
Based on the resequencing data from different strains of C. auratus and C. gibelio, the research team demonstrated that amphitriploid C. gibelio originated from ancestral amphidiploid C. auratus via autotriploidy about 0.82–0.96 million years ago. The amphitriploid C. gibelio overcame the meiotic obstacle caused by three homologous chromosomes through unisexual gynogenesis.
Through comparative genomics, the researchers identified intensive expansion and alterations of meiotic cell cycle-related genes and an oocyte-specific histone variant in C. gibelio. They found that most of these expanded genes showed high expression in ovaries and oocytes.
In addition, the research team found that C. gibelio produces unreduced oocytes by suppressing meiosis I. However, sporadic homologous recombination and a high rate of gene conversion were revealed during oogenesis of C. gibelio. These genomic changes might have facilitated purging deleterious mutations and maintaining genomic stability in amphitriploid C. gibelio.
- This press release was originally published on the Chinese Academy of Sciences Headquarters website
Laboratory
Solid State Chemistry Laboratory
RIKEN Cluster for Pioneering Research
(Chief Scientist: Genki Kobayashi)
Research Field and Overview
We work on the development of inorganic functional materials and novel electrochemical devices. Specifically, we aim to create outstanding ionics materials, including electrodes, solid electrolytes, and catalysts, and to develop energy storage/conversion and material conversion devices based on new operating principles.
Open Positions
Open Positions
Research Scientist: 1 position
Job description
We are seeking highly motivated scientists who can engage in research on ionics materials for the creation of new electrochemical devices. The successful candidate will carry out original research relating to the subjects mentioned above based on their expertise in one or more of the following research areas: solid state chemistry, electrochemistry, condensed matter physics, catalytic chemistry, and device engineering. Any research method is acceptable, including synthesis, measurement, and theory.
The successful candidate is expected to conduct research and development on subjects specific to the laboratory and to contribute to the management of the laboratory.
Qualification
Doctoral degree (or equivalent research ability and background) in the relevant research field.
Initial work location
Branch and address
RIKEN Wako Campus (2-1 Hirosawa, Wako, Saitama 351-0198)
Conditions, Salary and Benefits
Type of employment
The post is an indefinite-term employment position, subject to RIKEN's mandatory retirement age of 60. There is a Special Temporary Employment program to hire individuals from age 60 to age 65. This position does NOT offer a retirement allowance.
As an exception, depending on screening results, the successful candidate may be initially employed on a fixed-term employment contract up to a maximum of 5 years with the possibility of being promoted to indefinite-term employment after a separate review process that will take place, as a general rule, at the end of the first 3 years of employment. Remuneration and work conditions for fixed-term employment are based on the same conditions as those for indefinite-term employment.
Work conditions
Full time. This position falls under the specialized duties discretionary work system; one working day will be calculated as 7 hours and 30 minutes.
Remuneration and other provisions
In principle, the first two months of employment is considered a trial period.
Salary will be an annual salary based on experience, ability, and performance, and will consist of a base salary and a variable salary. The variable salary will be determined each fiscal year based on experience, ability, and performance. The monthly base salary is 417,300 JPY (based on FY 2022 actuals). The monthly base salary is subject to change due to amendments to RIKEN regulations. Discretionary work, commuting and housing allowances will be provided. Social insurance will be applied. Bonuses or retirement benefits will not be paid.
(Approximate annual salary at the time of employment)
The monthly salary at the time of employment, in the case that the employee has experience, ability, and achievement of approximately 5 years after completion of his/her doctoral degree, will be approximately 560,000 JPY.
Mandatory membership in the RIKEN Mutual Benefit Society (RIKEN Kyosaikai).
Days off include public holidays, New Year's holidays (Dec. 29 - Jan 3), and RIKEN Foundation Day.
Paid leave includes annual paid leave (up to 20 days depending on the month of employment), special paid leave (childcare, family nursing care, etc.), and work life balance holidays (up to 7 days depending on the month of employment).
There are also additional leave systems such as maternity leave, childcare leave, and family nursing care leave.
RIKEN has introduced telework as a temporary and special measure to prevent the spread of COVID-19. You may be asked to work from home according to the instructions of the institute. You can also choose to work from home with the permission of your supervisor.
Smoking is not permitted on site except in designated smoking areas. Smoking will be entirely prohibited on site from April 2024.
These and other provisions are in accordance with RIKEN regulations.
Other
Eligible to apply for Grants-in-Aid for Scientific Research (Kakenhi).
Eligible for an exemption from repayment for category 1 scholarship loans provided by the Japan Student Services Organization before fiscal year 2003.
RIKEN may instruct changes of workplace or assignment due to closure of the laboratory or other management decisions.
Application and required documents
Procedures
- A) Applicant registration
To register, access the website noted below, and enter the required basic information.
Solid State Chemistry Laboratory,CPR Research Scientist - B) After you have registered online you will automatically be sent an email from kjk-desk [at] spdr-fpr.com with your user ID, registration number, and password. Go to the website indicated in the email and upload your application documents (1) — (6).
Note:
You may revise your basic information and upload revised documents later if necessary. Please double check, however, that you have successfully uploaded your final application documents by the deadline.
All files must first be converted to PDF format before you can upload them. Word files cannot be uploaded.
Application documents
Note:
All the application documents including letters of recommendation should be prepared in English or Japanese, and uploaded in PDF format. Documents may be in black and white or color.
About letters of recommendation
Letters of recommendation should be directly sent to RIKEN by the referee. A request for a letter of recommendation will be automatically sent to the email addresses for references that you list when you register to apply. Please check if your referees have received the email request automatically sent from kjk-desk [at] spdr-fpr.com. You can also check on the status of your letters of recommendation through the online application system.
If none of your current supervisors or academic advisors are able to prepare a letter of recommendation, a letter from a third person will be accepted.
There is no set format for the letters of recommendation, but they must meet the following conditions: they must be addressed to the President of RIKEN, Makoto Gonokami, and include the date of issue. Please specify the referee’s contact information (Name, affiliation, job title, phone number, and email address).
Deadline
For applicant registration:
Noon, September 14, 2022 (Japan time).
For revising or uploading application documents and letters of recommendation:
Noon, September 20, 2022 (Japan time).
Handling of personal information
All private data sent to RIKEN in applications for employment are handled in strict confidentiality and will not be used for any other purpose or disclosed to any third party.
Selection process
After application documents are screened, only the successful candidates will be asked to have an interview. There will be final interviews with RIKEN executive directors as part of the hiring process.
Start of Employment
RIKEN expects that the successful applicant will be able to take up this position at the earliest opportunity after April 1, 2023
Notes
- No information can be released regarding application status or evaluation details prior to, during, or after the evaluation process.
- Application documents will not be returned.
- For further details on application or research activities at RIKEN, see the RIKEN website:
- RIKEN is actively undertaking initiatives to promote gender equality and diversity, and moving forward with the establishment of a diverse and vibrant research environment. If multiple candidates are found to have the same aptitude following a fair assessment, active efforts will be made to recruit female candidates.
Contact Information
For inquiries concerning the research
Dr. Genki Kobayashi (Chief Scientist)
Email: genki.kobayashi [at] riken.jp
For other inquiries
Research Personnel Affairs Section
Human Resources Division, RIKEN
Email: kjk-desk [at] riken.jp
Contact by phone is not available.
The UK has become the first country in the world to pay drug companies a fixed fee of £10mn per year for supplying new antibiotics1. This subscription model of payment, which values the new antibiotics according to patients’ quality of life over time, stands in stark contrast to more conventional reimbursement systems based on sales volumes. Antimicrobial resistance is one example where traditional approaches of introducing medicines are failing to meet the needs of patients. As medicine changes, fresh approaches are needed to accelerate patient access, and Improve the sustainability of healthcare systems.
How European countries should value and pay for ever more innovative therapies is becoming an increasingly pressing concern and one that Europe’s pharmaceutical trade association, the European Federation of Pharmaceutical Industries and Associations (EFPIA), is hurry to bring stakeholders together to address.
“New therapies that revolutionise the care model require a paradigm shift in the way they are assessed, valued and financed”
EFPIA
New treatments and vaccines can Improve and transform healthcare – if we are ready to embrace them. In 2020, around 5,000 clinical trials were initiated, funded by the pharmaceutical industry, and there are currently over 8,000 medicines in development. The UK subscription model is just one of many examples of more flexible and targeted payment models that might be employed to introduce new therapies into health systems.
In January 2022, the EFPIA published a white paper to investigate the potential impact of innovative therapies such as advanced therapy medicinal products (ATMPs) and the subsequent need for new models for pricing and payment2. It states: “ATMPs are at the forefront of global scientific innovation in healthcare. These pioneering treatments, which include gene therapies, somatic cell therapies and tissue engineered products, have the potential to transform the lives of patients and our healthcare systems. New therapies that revolutionise the care model require a paradigm shift in the way they are assessed, valued and financed.”
The pharmaceutical giant Roche is currently investing $15bn a year in drug research and development, according to Padraic Ward, Head of Pharma International at the Swiss firm. Ward compares the level of innovation in personalised drug therapies such as ATMPs to being able to match the disease fingerprint of patients in a way that is much more effective than those that treat the whole “hand”.
“The solution is to find a way of paying for the therapy over the amount of time that the patient benefits from it”
Padraic Ward, Head of Pharma International, Roche
“Such personalisation creates challenges because drug pricing and reimbursement systems across Europe are currently set up to compare a new therapy’s effectiveness against older and less targeted treatments to determine their value, a process that relies upon data from vast clinical trials,” Ward explains. “Large-scale trials are not possible for targeted “fingerprint” treatments such as ATMPs even though the benefits in trials with small cohorts of patients are demonstrably clear.”
This disconnect has been evident for some time and, as Ward explains, the situation for healthcare providers to give EU citizens and patients the best available treatments is only likely to become more complex:
“We're on the cusp of a new era of gene therapies and, while there's a long way to go and a lot of risks involved, they could be brilliant for patients with diseases that are very difficult to treat.”
“Think about it,” says Ward. “You essentially administer a gene therapy once and then the benefit lasts over a long period of time, but current healthcare systems – even if they could value them properly – would have to bear the cost in a single year.
“The solution, I think, is to find a way of paying for the therapy over the amount of time that the patient benefits from it.”
According to the EFPIA, a possible answer might lie in novel pricing models such as the subscription model recently signed by the UK, or a payment system based on treatment outcomes, payments made over time or a combination method.
A new report by the EFPIA3 looking at expenditure on innovative and generic medicines in 15 European countries shows that, on average, countries spent 15 per cent of their healthcare budgets on medication in 2018. Across Europe, the spend on medicines dispensed by pharmacies and hospitals has actually remained stable as a proportion of overall healthcare expenditure. Within this there is considerable divergence, however; countries such as the Czech Republic spent around 24 per cent of their healthcare budget on drugs in 2018, while Norway spent just eight per cent in the same year4.
Another challenge impacting the accessibility of innovative medicines is how the price of drugs is referenced, both formally and informally, across the EU – despite the fact that the ability of different European countries to invest in public health and pay for drug therapies differs widely.
One solution, says the EFPIA, is to implement an equity-based tiered pricing system, which can effectively address the widely diverging ability of the EU countries to fund innovative medicines. Ward agrees with the EFPIA’s fix for an urgent problem:
“Let's recognise that some countries are richer than others, and therefore, those places should pay more, and let's break that [price] referencing that takes place between different countries,” he says.
“That's the basis of the proposal that we're making: an equity-based, tiered pricing framework, in which we say that the highest price that poorer countries pay will be no more than the lowest price the wealthier countries pay,” Ward adds. “I think that's a nettle we have to grasp immediately, because otherwise it becomes very difficult to sort out anything else.”
In a latest study posted to the bioRxiv* preprint server, researchers presented a novel genomic sequence data-based mathematical framework for rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VoIs) and variants of concern (VoCs) in a viral multiple sequence alignment (MSA).
Background
Genomic surveillance plays a vital role in combat against more immune-evasive and virulent ribonucleic acid (RNA) virus strains and is particularly necessary for effective coronavirus disease 2019 (COVID-19) mitigation. Efforts for genomic sequencing have facilitated virological, epidemiological surveillance near real-time; however, efficient identification of variants within SARS-CoV-2 sequences that could be potential threats remains challenging. Existing correlation analysis methods are not amenable to co-evolutionary analysis.
About the study
In the present study, researchers presented a novel framework for rapidly detecting SARS-CoV-2 VoIs and VoCs with an MSA times series for SARS-CoV-2 genomes. The building blocks of the framework were pairs of co-evolving motifs identified by co-evolutionary signals in the MSA.
The method was applied to SARS-CoV-2, considering pairwise relations for analyzing alerts issued between November 2020 and August 2021 with a weekly resolution for England, the United States of America, South America, and India. The aim was to identify maximal co-evolving motif pairs within the MSA instead of considering the emergence of specific mutations.
The team constructed highly dimensional simplices based on the co-evolutionary pair coupling distances. The team developed and analyzed alerts and clusters with substantial fractions of newly emerging motifs triggered alerts. Subsequently, the alerts were tested, and the global initiative on sharing all influenza data (GISAID) database was used to obtain data on SARS-CoV-2 sequences retrospectively.
The framework issued alerts without a priori assumptions, with the exception of the Wuhan-Hu-1 (reference) sequence on which MSAs were built. The alerts were related to established SARS-CoV-2 variants and a posteriori knowledge of VoI/VoC strains was used for evaluating the issued alerts’ accuracy.
The main purpose of the alerts was to facilitate rapid biological analysis for a few critical sites. A substantial fraction of the included clusters comprised newly active sites that represented large additive fitness components of the underlying clusters and could indicate the emergence of a functional block or a mutational event. P-distance and J-distance with k-means and HCS-clustering were used for the analysis.
Results
An alert in England marked the rise of Alpha in a timely fashion, >4 weeks earlier than the WHO and Pango designations. The 28 Alpha-defining mutations were split into three blocks. Out of seven alerts in India, one alert corresponded to a cluster of 14 motifs, of which 13 were active sites that were confirmed to be among the 20 characteristic Delta VoC mutations organized into four co-evolutionary blocks that did not emerge at once; however, the 13 positions became inactive after 21 days.
In the USA, seven alerts were observed, three of which marked Delta AY.3 emergence and exhibited 31 characteristic mutations, of which 11 belonged exclusively to the AY.3. In South America, 23 alerts were observed, of which five alerts confirmed the emergence of a new variant. The prevalence of the Lambda variant in March 2021 was low and had not been assigned a Pango lineage; however, distinguished co-evolution signals mapped to Lambda were identified six weeks ahead of time.
Five alerts were triggered in the first week of December, which were: (i) a newly emerging cluster of 13 sites not mapped to any VoI/VoC, (ii) a cluster of 20 sites, of which 17 were real or (+) sites mapped to Alpha, (iii) a cluster of 15 sites, of which nine were real sites, (iv) a cluster of 16 sites, of which 15 were real sites mapped to Delta, and (v) an entirely new emerging cluster of 12 real sites, mapped to the Delta AY.3 sub-lineage.
In the third week of December, a cluster of 23 real sites emerged, mapped to the Gamma variant. The framework did not issue alerts of Mu variant emergence, and the characteristic Mu mutations were recombination of mutations present in Alpha, Delta, and Gamma. The mutational sites were already active in the general population; thus, they were not considered real sites. The matrices were highly symmetrical, indicating that HCS-clustering and k-means clustering produced similar results for P- and J-distances and that both metrics captured SARS-CoV-2 co-evolution.
Conclusions
Overall, the study findings showed how motifs could provide insights into the organization of characteristic mutations of a VoI/VoC, organizing them as co-evolving blocks. The extraction of co-evolutionary signals using MSA analysis could Improve understanding of the significance of SARS-CoV-2 mutations and could enable prompt detection of emerging SARS-CoV-2 variants.
The framework required no a priori phylogenetic knowledge or any biological impact analysis and could be considered a guidance system that alerted not only sites where the biological analysis should be performed but also quantified the co-evolution rate. The framework could provide important data to biologists since co-evolution relation identification could provide clues for the underlying biological mechanisms.
*Important notice
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
