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Posts Tagged ‘Pandemic’


The Inequality and Health Disparity seen with the COVID-19 Pandemic Is Similar to Past Pandemics

Curator: Stephen J. Williams, PhD

2019-nCoV-CDC-23311

It has become very evident, at least in during this pandemic within the United States, that African Americans and poorer communities have been disproportionately affected by the SARS-CoV2 outbreak . However, there are many other diseases such as diabetes, heart disease, and cancer in which these specific health disparities are evident as well :

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Disease like cancer have been shown to have wide disparities based on socioeconomic status, with higher incidence rates seen in poorer and less educated sub-populations, not just here but underdeveloped countries as well (see Opinion Articles from the Lancet: COVID-19 and Cancer Care in China and Africa) and graphics below)

 

 

 

 

 

 

 

 

 

 

In an article in Science by Lizzie Wade, these disparities separated on socioeconomic status, have occurred in many other pandemics throughout history, and is not unique to the current COVID19 outbreak.  The article, entitled “An Unequal Blow”, reveal how

in past pandemics, people on the margins suffered the most.

Source: https://science.sciencemag.org/content/368/6492/700.summary

Health Disparities during the Black Death Bubonic Plague Pandemic in the 14th Century (1347-1351)

During the mid 14th century, all of Europe was affected by a plague induced by the bacterium Yersinia pestis, and killed anywhere between 30 – 60% of the European population.  According to reports by the time the Black Death had reached London by January 1349 there had already been horrendous reports coming out of Florence Italy where the deadly disease ravished the population there in the summer of 1348 (more than half of the city’s population died). And by mid 1349 the Black Death had killed more than half of Londoners.  It appeared that no one was safe from the deadly pandemic, affecting the rich, the poor, the young, the old.

However, after careful and meticulous archaeological and historical analysis in England and other sites, revealed a distinct social and economic inequalities that predominated and most likely guided the pandemics course throughout Europe.   According to Dr. Gwen Robbins Schug, a bio-archaeologist at Appalachian State University,

Bio-archaeology and other social sciences have repeatedly demonstrated that these kinds of crises play out along the preexisting fault lines of each society.  The people at greatest risk were often those already marginalized- the poor and minorities who faced discrimination in ways that damaged their health or limited their access to medical care even in pandemic times.

At the start of the Black Death, Europe had already gone under a climactic change with erratic weather.  As a result, a Great Famine struck Europe between 1315-17.  Wages fell and more people fell into poverty while the wealthiest expanded their riches, leading to an increased gap in wealth and social disparity.  In fact according to recordkeeping most of Englanders were living below the poverty line.

Author Lizzie Wade also interviewed Dr. Sharon, DeWitte, a biological anthropologist at University of South Carolina, who looks at skeletal remains of Black Death victims to get evidence on their health status, like evidence of malnutrition, osteoporosis, etc.   And it appears that most of the victims may have had preexisting health conditions indicative of poorer status.  And other evidence show that wealthy landowners had a lower mortality rate than poorer inner city dwellers.

1918 Spanish Flu

Socioeconomic and demographic studies have shown that both Native American Indians and African Americans on the lower end of the socioeconomic status were disproportionately affected by the 1918 Spanish flu pandemic.  According to census records, the poorest had a 50% higher mortality rate than wealthy areas in the city of Oslo.  In the US, minors and factory workers died at the highest rates.  In the US African Americans had already had bouts with preexisting issues like tuberculosis and may have contributed to the higher mortality.  In addition Jim Crow laws in the South, responsible for widespread discrimination, also impacted the ability of African Americans to seek proper medical care.

From the Atlantic

Source: https://www.theatlantic.com/politics/archive/2016/05/americas-health-segregation-problem/483219/

America’s Health Segregation Problem

Has the country done enough to overcome its Jim Crow health care history?

VANN R. NEWKIRK II

MAY 18, 2016

Like other forms of segregation, health-care segregation was originally a function of explicitly racist black codes and Jim Crow laws. Many hospitals, clinics, and doctor’s offices were totally segregated by race, and many more maintained separate wings or staff that could never intermingle under threat of law. The deficit of trained black medical professionals (itself caused by a number of factors including education segregation) meant that no matter where black people received health-care services, they would find their care to be subpar compared to that of whites. While there were some deaths that were directly attributable to being denied emergency service, most of the damage was done in establishing the same cumulative health disparities that plague black people today as a societal fate. The descendants of enslaved people lived much more dangerous and unhealthy lives than white counterparts, on disease-ridden and degraded environments. Within the confines of a segregated health-care system, these factors became poor health outcomes that shaped black America as if they were its genetic material.

 

https://twitter.com/time4equity/status/1175080469425266688?s=20

 

R.A.HahnaB.I.TrumanbD.R.Williamsc.Civil rights as determinants of public health and racial and ethnic health equity: Health care, education, employment, and housing in the United States.

SSM – Population Health: Volume 4, April 2018, Pages 17-24

Highlights

  • Civil rights are characterized as social determinants of health.
  • Four domains in civil rights history since 1950 are explored in—health care, education, employment, and housing.
  • Health care, education, employment show substantial benefits when civil rights are enforced.
  • Housing shows an overall failure to enforce existing civil rights and persistent discrimination.
  • Civil rights and their enforcement may be considered a powerful arena for public health theorizing, research, policy, and action.

 

For more articles on COVID-19 Please go to our Coronovirus Portal

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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6/30/2020

Chasing change: Innovation and patent
activity during COVID-19

A report on the pandemic’s impact on the global
R&D community and innovation lifecycle

Reporters: Aviva Lev-Ari, Ph.D., RN & Gail S. Thornton, M.A.

Published by Clarivate Derwent

In just a few short months, COVID-19 swept through the world. While many aspects of everyday life have altered as the pandemic has gripped the globe, society at large has been and remains remarkably resilient.

To understand the impact of COVID-19 on the world’s innovators, we asked organizations from a cross-section of industries globally about how the pandemic has affected their organizations’ innovation strategies.

We are pleased to share the results with you in the report, “Chasing change: Innovation and patent activity during COVID-19.”

SOURCE:

https://clarivate.com/derwent/wp-content/uploads/sites/3/dlm_uploads/2020/06/DW507408683-COVID-19-Report_FINAL.pdf?utm_campaign=EM1_Report_Derwent_COVID_19_Survey_Apr_IPS_Global_2020_ClientsProspects

Other related article published in this Open Access Online Scientific Journal include the following:

Corticosteroid, Dexamethasone Improves Survival in COVID-19: Deaths reduction by 1/3 in ventilated patients and by 1/5 in other patients receiving oxygen only

Reporter: Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2020/06/27/corticosteroid-dexamethasone-improves-survival-in-covid-19-deaths-reduction-by-1-3-in-ventilated-patients-and-by-1-5-in-other-patients-receiving-oxygen-only/

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

 

Curator: Stephen J. Williams, Ph.D.

 

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse.  In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries.  China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

 

 

 

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging.  For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality.  Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data.  This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

 

 

 

 

 

 

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

 

 

 

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data.  Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

 

 

 

 

 

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal.  In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data.  In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020).  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.  The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

  • The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
  • More data was curated from news media, television and includes time-stamped information on COVID-19 cases
  • These reports are publicly available, de-identified patient data
  • No patient consent was needed and no ethics approval was required
  • Data was collected between January 20, 2020 and January 31,2020
  • Sex, age, province of identification, travel history, dates of symptom development was collected
  • Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
  • All patients in database had laboratory confirmation of infection

 

Results

  • 507 patient data was collected with 153 visited and 152 resident of Wuhan
  • Reported cases were skewed toward males however the overall population curve is skewed toward males in China
  • Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
  • Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
  • Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
  • Median reporting delay in the authors crowd-sourcing data was 5 days
  • Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

 

 

 

 

 

 

 

 

 

 

 

 

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

  1. Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
  2. Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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Powerful AI Tools Being Developed for the COVID-19 Fight

Curator: Stephen J. Williams, Ph.D.

 

Source: https://www.ibm.com/blogs/research/2020/04/ai-powered-technologies-accelerate-discovery-covid-19/

IBM Releases Novel AI-Powered Technologies to Help Health and Research Community Accelerate the Discovery of Medical Insights and Treatments for COVID-19

April 3, 2020 | Written by: 

IBM Research has been actively developing new cloud and AI-powered technologies that can help researchers across a variety of scientific disciplines accelerate the process of discovery. As the COVID-19 pandemic unfolds, we continue to ask how these technologies and our scientific knowledge can help in the global battle against coronavirus.

Today, we are making available multiple novel, free resources from across IBM to help healthcare researchers, doctors and scientists around the world accelerate COVID-19 drug discovery: from gathering insights, to applying the latest virus genomic information and identifying potential targets for treatments, to creating new drug molecule candidates.

Though some of the resources are still in exploratory stages, IBM is making them available to qualifying researchers at no charge to aid the international scientific investigation of COVID-19.

Today’s announcement follows our recent leadership in launching the U.S. COVID-19 High Performance Computing Consortium, which is harnessing massive computing power in the effort to help confront the coronavirus.

Streamlining the Search for Information

Healthcare agencies and governments around the world have quickly amassed medical and other relevant data about the pandemic. And, there are already vast troves of medical research that could prove relevant to COVID-19. Yet, as with any large volume of disparate data sources, it is difficult to efficiently aggregate and analyze that data in ways that can yield scientific insights.

To help researchers access structured and unstructured data quickly, we are offering a cloud-based AI research resource that has been trained on a corpus of thousands of scientific papers contained in the COVID-19 Open Research Dataset (CORD-19), prepared by the White House and a coalition of research groups, and licensed databases from the DrugBankClinicaltrials.gov and GenBank. This tool uses our advanced AI and allows researchers to pose specific queries to the collections of papers and to extract critical COVID-19 knowledge quickly. Please note, access to this resource will be granted only to qualified researchers. To learn more and request access, please click here.

Aiding the Hunt for Treatments

The traditional drug discovery pipeline relies on a library of compounds that are screened, improved, and tested to determine safety and efficacy. In dealing with new pathogens such as SARS-CoV-2, there is the potential to enhance the compound libraries with additional novel compounds. To help address this need, IBM Research has recently created a new, AI-generative framework which can rapidly identify novel peptides, proteins, drug candidates and materials.

We have applied this AI technology against three COVID-19 targets to identify 3,000 new small molecules as potential COVID-19 therapeutic candidates. IBM is releasing these molecules under an open license, and researchers can study them via a new interactive molecular explorer tool to understand their characteristics and relationship to COVID-19 and identify candidates that might have desirable properties to be further pursued in drug development.

To streamline efforts to identify new treatments for COVID-19, we are also making the IBM Functional Genomics Platform available for free for the duration of the pandemic. Built to discover the molecular features in viral and bacterial genomes, this cloud-based repository and research tool includes genes, proteins and other molecular targets from sequenced viral and bacterial organisms in one place with connections pre-computed to help accelerate discovery of molecular targets required for drug design, test development and treatment.

Select IBM collaborators from government agencies, academic institutions and other organizations already use this platform for bacterial genomic study. And now, those working on COVID-19 can request the IBM Functional Genomics Platform interface to explore the genomic features of the virus. Access to the IBM Functional Genomics Platform will be prioritized for those conducting COVID-19 research. To learn more and request access, please click here.

Drug and Disease Information

Clinicians and healthcare professionals on the frontlines of care will also have free access to hundreds of pieces of evidence-based, curated COVID-19 and infectious disease content from IBM Micromedex and EBSCO DynaMed. Using these two rich decision support solutions, users will have access to drug and disease information in a single and comprehensive search. Clinicians can also provide patients with consumer-friendly patient education handouts with relevant, actionable medical information. IBM Micromedex is one of the largest online reference databases for medication information and is used by more than 4,500 hospitals and health systems worldwide. EBSCO DynaMed provides peer-reviewed clinical content, including systematic literature reviews in 28 specialties for comprehensive disease topics, health conditions and abnormal findings, to highly focused topics on evaluation, differential diagnosis and management.

The scientific community is working hard to make important new discoveries relevant to the treatment of COVID-19, and we’re hopeful that releasing these novel tools will help accelerate this global effort. This work also outlines our long-term vision for the future of accelerated discovery, where multi-disciplinary scientists and clinicians work together to rapidly and effectively create next generation therapeutics, aided by novel AI-powered technologies.

Learn more about IBM’s response to COVID-19: IBM.com/COVID19.

Source: https://www.ibm.com/blogs/research/2020/04/ai-powered-technologies-accelerate-discovery-covid-19/

DiA Imaging Analysis Receives Grant to Accelerate Global Access to its AI Ultrasound Solutions in the Fight Against COVID-19

Source: https://www.grantnews.com/news-articles/?rkey=20200512UN05506&filter=12337

Grant will allow company to accelerate access to its AI solutions and use of ultrasound in COVID-19 emergency settings

TEL AVIV, IsraelMay 12, 2020 /PRNewswire-PRWeb/ — DiA Imaging Analysis, a leading provider of AI based ultrasound analysis solutions, today announced that it has received a government grant from the Israel Innovation Authority (IIA) to develop solutions for ultrasound imaging analysis of COVID-19 patients using Artificial Intelligence (AI).Using ultrasound in point of care emergency settings has gained momentum since the outbreak of COVID-19 pandemic. In these settings, which include makeshift hospital COVID-19 departments and triage “tents,” portable ultrasound offers clinicians diagnostic decision support, with the added advantage of being easier to disinfect and eliminating the need to transport patients from one room to another.However, analyzing ultrasound images is a process that it is still mostly done visually, leading to a growing market need for automated solutions and decision support.As the leading provider of AI solutions for ultrasound analysis and backed by Connecticut Innovations, DiA makes ultrasound analysis smarter and accessible to both new and expert ultrasound users with various levels of experience. The company’s flagship LVivo Cardio Toolbox for AI-based cardiac ultrasound analysis enables clinicians to automatically generate objective clinical analysis, with increased accuracy and efficiency to support decisions about patient treatment and care.

The IIA grant provides a budget of millions NIS to increase access to DiA’s solutions for users in Israel and globally, and accelerate R&D with a focus on new AI solutions for COVID-19 patient management. DiA solutions are vendor-neutral and platform agnostic, as well as powered to run in low processing, mobile environments like handheld ultrasound.Recent data highlights the importance of looking at the heart during the progression of COVID-19, with one study citing 20% of patients hospitalized with COVID-19 showing signs of heart damage and increased mortality rates in those patients. DiA’s LVivo cardiac analysis solutions automatically generate objective, quantified cardiac ultrasound results to enable point-of-care clinicians to assess cardiac function on the spot, near patients’ bedside.

According to Dr. Ami Applebaum, the Chairman of the Board of the IIA, “The purpose of IIA’s call was to bring solutions to global markets for fighting COVID-19, with an emphasis on relevancy, fast time to market and collaborations promising continuity of the Israeli economy. DiA meets these requirements with AI innovation for ultrasound.”DiA has received several FDA/CE clearances and established distribution partnerships with industry leading companies including GE Healthcare, IBM Watson and Konica Minolta, currently serving thousands of end users worldwide.”We see growing use of ultrasound in point of care settings, and an urgent need for automated, objective solutions that provide decision support in real time,” said Hila Goldman-Aslan, CEO and Co-founder of DiA Imaging Analysis, “Our AI solutions meet this need by immediately helping clinicians on the frontlines to quickly and easily assess COVID-19 patients’ hearts to help guide care delivery.”

About DiA Imaging Analysis:
DiA Imaging Analysis provides advanced AI-based ultrasound analysis technology that makes ultrasound accessible to all. DiA’s automated tools deliver fast and accurate clinical indications to support the decision-making process and offer better patient care. DiA’s AI-based technology uses advanced pattern recognition and machine-learning algorithms to automatically imitate the way the human eye detects image borders and identifies motion. Using DiA’s tools provides automated and objective AI tools, helps reduce variability among users, and increases efficiency. It allows clinicians with various levels of experience to quickly and easily analyze ultrasound images.

For additional information, please visit http://www.dia-analysis.com.

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Opinion Articles from the Lancet: COVID-19 and Cancer Care in China and Africa

Reporter: Stephen J. Williams, PhD

Cancer Patients in SARS-CoV-2 infection: a nationwide analysis in China

Wenhua Liang, Weijie Guan, Ruchong Chen, Wei Wang, Jianfu Li, Ke Xu, Caichen Li, Qing Ai, Weixiang Lu, Hengrui Liang, Shiyue Li, Jianxing He

Lancet Oncol. 2020 Mar; 21(3): 335–337. Published online 2020 Feb 14. doi: 10.1016/S1470-2045(20)30096-6

PMCID: PMC7159000

 

The National Clinical Research Center for Respiratory Disease and the National Health Commission of the People’s Republic of China collaborated to establish a prospective cohort to monitor COVID-19 cases in China.  As on Jan31, 20202007 cases have been collected and analyzed with confirmed COVID-19 infection in these cohorts.

Results: 18 or 1% of COVID-19 cases had a history of cancer (the overall average cancer incidence in the overall China population is 0.29%) {2015 statistics}.  It appeared that cancer patients had an observable higher risk of COVID related complications upon hospitalization. However, this was a higher risk compared with the general population.  There was no comparison between cancer patients not diagnosed with COVID-19 and an assessment of their risk of infection.  Interestingly those who were also cancer survivors showed an increased incidence of COVID related severe complications compared to the no cancer group.

Although this study could have compared the risk within a cancer group, the authors still felt the results warranted precautions when dealing with cancer patients and issued recommendations including:

  1. Postponing of adjuvant chemotherapy or elective surgery for stable cancer should be considered
  2. Stronger personal protection for cancer patients
  3. More intensive surveillance or treatment should be considered when patients with cancer are infected, especially in older patients

Further studies will need to address the risk added by specific types of chemotherapy: cytolytic versus immunotherapy e.g.

 

Preparedness for COVID-19 in the oncology community in Africa

Lancet Oncology, Verna Vanderpuye, Moawia Mohammed,Ali Elhassan

Hannah Simonds: Published:April 03, 2020DOI:https://doi.org/10.1016/S1470-2045(20)30220-5

Africa has a heterogeneity of cultures, economies and disease patterns however fortunately it is one of the last countries to be hit by the COVID-19 pandemic, which allows some time for preparation by the African nations.  The authors note that with Africa’s previous experiences with epidemics, namely ebola and cholera, Africa should be prepared for this pandemic.

However, as a result of poor economic discipline, weak health systems, and poor health-seeking behaviors across the continent, outcomes could be dismal. Poverty, low health literacy rates, and cultural practices that negatively affect cancer outcomes will result in poor assimilation of COVID-19 containment strategies in Africa.”

In general African oncologists are following COVID-19 guidelines from other high-income countries, but as this writer acknowledges in previous posts, there was a significant lag from first cases in the United States to the concrete formulation of guidelines for both oncologists and patients with regard to this pandemic.  African oncologist are delaying the start of adjuvant therapies and switching more to oral therapies and rethink palliative care.

However the authors still have many more questions than answers, however even among countries that have dealt with this pandemic before Africa (like Italy and US), oncologists across the globe still have not been able to answer questions like: what if my patient develops a fever, what do I do during a period of neutropenia, to their satisfaction or the satisfaction of the patient.  These are questions even oncologists who are dealing in COVID hotspots are still trying to answer including what constitutes a necessary surgical procedure? As I have highlighted in recent posts, oncologists in New York have all but shut down all surgical procedures and relying on liquid biopsies taken in the at-home setting. But does Africa have this capability of access to at home liquid biopsy procedures?

In addition, as I had just highlighted in a recent posting, there exists extreme cancer health disparities across the African continent, as well as the COVID responses. In West Africa, COVID-19 protocols are defined at individual institutions.  This is more like the American system where even NCI designated centers were left to fashion some of their own guidelines initially, although individual oncologists had banded together to do impromptu meetings to discuss best practices. However this is fine for big institutions, but as in the US, there is a large rural population on the African continent with geographical barriers to these big centers. Elective procedures have been cancelled and small number of patients are seen by day.  This remote strategy actually may be well suited for African versus more developed nations, as highlighted in a post I did about mobile health app use in oncology, as this telemedicine strategy is rather new among US oncologists (reference my posts with the Town Hall meetings).

The situation is more complicated in South Africa where they are dealing with an HIV epidemic, where about 8 million are infected with HIV. Oncology services here are still expecting to run at full capacity as the local hospitals deal with the first signs of the COVID outbreak. In Sudan, despite low COVID numbers, cancer centers have developed contingency plans. and are deferring new referrals except for emergency cases.  Training sessions for staff have been developed.

For more articles in this online open access journal on Cancer and COVID-19 please see our

Coronovirus Portal
Responses to the #COVID-19 outbreak from Oncologists, Cancer Societies and the NCI: Important information for cancer patients

 

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Worldwide trial uses AI to quickly identify ideal Covid-19 treatments

Reporter : Irina Robu, PhD

The novel coronavirus, SARS-CoV-2 that has been spreading around the world can cause a respiratory illness that can be severe. The disease, COVID-19 appears to have a fatality rate of less than 2 percent and forcing doctors to choose between two equally revolting options: try an unproven therapy and anticipate that it works or treat patients with standard supportive care for severe respiratory disease until a vaccine is developed.

Currently, randomized controlled trials have started in dozens of hospitals around the world by fusing two approaches together, using artificial intelligence to home in or using the most effective treatments for respiratory infections. The randomized trials, also known as an adaptive trial, in which scientists adjust the treatment protocols and/or statistical procedures based on the outcomes of participants. The trials are seen as a way to detect promising treatments and brand trials more flexible than traditional randomized trials and force patients, trial sponsors to wait for an outcome that often turns out to be disappointing. The inadequacies of the randomized approach have been taken into sharp relief during the pandemic, as thousands of patients can’t wait for gold-standard science to play out as they lay dying in intensive care of units.

However, analyzing data from more than 50 hospitals, researchers hope to supply quick answers to pressing questions such as the fact that the antimalarial drug hydroxychloroquine is an effective therapy and, if so, for which types of patients. The trial will also allow the researchers to test multiple therapies at once. Since the approach seems reasonable to give answers during a pandemic, it still has a lot of challenges, plus the necessity to rapidly assemble and analyze data from several hospitals with various record-keeping systems on three continents. Then update the protocols in accord during a crisis that is draining clinical resources.

Since several treatments are being tested, carrying out these trials is predominantly complicated. But progress in computing resources required to share data and analyze it swiftly using artificial intelligence have started to make these designs more practical.

The World Health Organization and the U.S. Food and Drug Administration, along with groups like the Gates Foundation, have offered increasing support for adaptive trial designs in recent years, particularly as a way to evaluate therapies during epidemics.

Nonetheless that doesn’t mean this specific effort is going to yield results in time to save the first wave of extremely ill patients. Once a promising treatment is recognized more patients will be allocated to receive it during each successive round of therapy. So far, about 130 ICU patients with Covid-19 have been enrolled, furthermore to hundreds of other hospitalized patients.

The goal in the REMAP-CAP trial, once all the trial sites are up and running, is to analyze results and change treatments on a weekly basis.

SOURCE

International trial uses AI to rapidly identify optimal Covid-19 treatments

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Chinese Hospitals Deploy AI to Help Diagnose Covid-19

Software that reads CT lung scans had been used primarily to detect cancer. Now it’s retooled to look for signs of pneumonia caused by coronavirus.

https://www.wired.com/story/chinese-hospitals-deploy-ai-help-diagnose-covid-19/

 

Artificial Intelligence against COVID-19: An Early Review

AI has not yet made an impact, but data scientists have taken up the challenge

https://towardsdatascience.com/artificial-intelligence-against-covid-19-an-early-review-92a8360edaba

 

Can AI Find a Cure for COVID-19?

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https://www.datanami.com/2020/03/23/can-ai-find-a-cure-for-covid-19/

 

Scientists are racing to find the best drugs to treat COVID-19

The WHO is launching a multicountry trial to collect good data

By Nicole Wetsman Mar 23, 2020, 9:21am EDT

https://www.theverge.com/2020/3/23/21188167/coronavirus-treatment-clinical-trials-drugs-remdesivir-chloroquine-covid

 

Data Scientists Use Machine Learning to Discover COVID-19 Treatments

Researchers are using machine learning algorithms capable of generating millions of therapeutic antibodies to quickly find treatments for COVID-19.

https://healthitanalytics.com/news/data-scientists-use-machine-learning-to-discover-covid-19-treatments

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