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Cambridge doctor raises dementia awareness in powerful charity campaign
Cambridge-based clinician and researcher, Prof James Rowe, takes the spotlight in an inspiring new charity film exploring the impact dementia has on the brain and the power of research to overcome it.
The film is part of Alzheimer's Research UK and Ricoh's joint campaign, Dementia Uncovered, which highlights the many physical diseases that cause dementia, as well as the scientists striving to bring about new treatments.
The campaign is launched as the charity also reveals that nearly half (46%) of UK adults are unable to name a single type of dementia. The survey carried out by YouGov survey shows that when prompted, most UK adults (92%) had heard of Alzheimer's disease, only 3% had heard of frontotemporal dementia (FTD), sometimes known as Pick's disease.
Prof James Rowe, from the University of Cambridge takes the leading role in a film, directed by Gemma Brady (24 Hours in A&E), revealing the complexities of FTD and related illnesses such as progressive supranuclear palsy, and the hope that lies in research.
FTD accounts for one in 20 of all dementia cases, but it often affects people at a much younger age than Alzheimer's disease.
During FTD, brain changes occur in distinct parts of the brain and can cause symptoms beyond memory problems. When the frontal lobe of the brain is damaged, it causes changes to personality, behaviour and movement and damage to the temporal lobe causes problems with learning and language.
Prof Rowe explores the impact of FTD and related illnesses on families at his clinic at Addenbrooke's Hospital in Cambridge. Sadly, current treatments developed for Alzheimer's disease do not work for people living with other types of dementia. Prof Rowe's research into FTD is at the forefront of medical science, providing hope for many people.
Prof James Rowe from the University of Cambridge, said:
"It is a privilege to be able to see and work with patients in the clinic. I get to see just how hard it can be for people with FTD and related illnesses, and their friends and families. I hope that this campaign will help raise awareness of the illness, which is often an aggressive form of dementia. Research here in Cambridge aims to unpick the causes of this disease and develop better treatments for those who need it."
Ian Wilson, Chief Executive of Alzheimer's Research UK, said:
"These eye-opening new survey results show that while many people recognise Alzheimer's disease as a cause of dementia and 92% associate memory loss with the condition, far fewer can name other forms of dementia or the wide range of symptoms they bring. It's important that people realise the condition is far more than just memory loss.
"We are delighted to be working with Ricoh on this important campaign once again. We're incredibly grateful to the commitment and dedication of the researchers like Prof Rowe whose work is so crucial. Dementia research is making progress and with support from the public and companies like Ricoh, we can make breakthroughs possible."
David Mills, Chief Executive Officer of Ricoh, added:
"Since the launch of our partnership in 2017, we have worked with Alzheimer's Research UK on a number of initiatives to help raise awareness of dementia. We are proud of this new campaign and hope that it will challenge common misconceptions around dementia, and provide a powerful insight into the lives of people with the condition and scientists striving to find ways to prevent, treat and cure these debilitating diseases."


juvenile localized scleroderma. rheumatic autoimmune disease. no cure.
i came across a blog the other day, whose premise/focus i really love. it's all about women encouraging other women in all of our messy, chaotic, imperfect, realness. it's about community-building, and connecting. and it sounds absolutely wonderful with just one caveat: every single woman centered and celebrated on this site so far, is white….


My family loves lasagna. I like this recipe because it's very easy to fix. You prepare, put in refrigerator, then pop in the oven.
Per Serving: 475 calories, 34 grams protein, 20 grams of fat, 38 grams of CHO, 4 grams of dietary fiber, 1031 mg sodium.
Brown beef in a large skillet over medium heat until no longer pink. Drain. Stir in pasta sauce. Spread the meat sauce into a 11 X 7 inch baking dish which has been sprayed with no-stick spray. Add ½ of uncooked lasagna noodles. Add ½ of ricotta cheese. Then ½ of the mozzarella cheese. Then repeat: meat sauce, lasagna noodles, ricotta cheese & mozzarella cheese. Slowly pour the ¼ cup hot water around the inside edge of the baking dish. Cover tightly with 2 layers of heavy duty aluminum foil. Bake at 375 degrees for 45 minutes. Uncover & bake for 10 more minutes. Let stand for 10 minutes before serving.
*Could substitute ground turkey, chicken or sausage.
**Could substitute tomato pasta sauce with white alfredo sauce.


Near the village Nor Karmiravan, Martakert province, Republic of Artsakh, 3 thousand years old anthropomorphic steles have been discovered.
They are considered one of the most significant finds belonging to the pre-Christian Armenian culture.
According to archaeologists, stone sculptures similar to these steles can be found only in Artsakh's region.
The steles are separated into three segments representing human body parts – head, torso, and lower limbs. On all steles arms are portrayed crossed on the chest of the figure, and some figures are holding daggers.
Such details might indicate that the purpose of the steles might have been connected with protection. Initially, one of the steles has been discovered by an inhabitant of Nor Karmiravan.
These monuments stand out with their gigantic three-meter height. Another aspect of their uniqueness is that they are well preserved, have distinguishable patterns.
The steles are of great interest not only in the fields of science and archaeology, but politics of Armenians of Republic of Artsakh as well.


text:
Infographic: The Story of MathCraft
Winter 1980: With a background in computer science, Imo Etuk leaves a prominent defense contractor to start a consulting business. After seeing the chaos surrounding the management of security clearances, MathCraft – named for the algebraic precision needed to design enterprise architecture – is born. Spring 1992: The MathCraft team is invited to a military…
How Insider Threats are Evolving in 2022
In the midst of a rising cybersecurity crisis, it's worth keeping one thing in mind: the best way to infiltrate an organization is from the inside. With experts estimating that two out of every three data breaches are caused by insiders, insider threats remain a major concern for cleared organizations, demanding constant vigilance and alertness from facility security…
The Log4Shell Exploit in Retrospect: 3 Takeaways for FSOs
The holiday seasons have become a perilous time for government agencies and cleared facilities: while everyone else is wrapping gifts, cyber actors are ramping up their malicious activities – and the 2021 holiday season was no exception. At the beginning of December 2021, the Apache Software Foundation disclosed a remote code execution (RCE) vulnerability in the popular…
What's New in 2022? Security Predictions for Cleared Facilities
2021 was an eventful year for cleared facilities, bringing new federal security legislation driven by changing trends in technology and the workforce. With rising cybersecurity threats and international conflict on the horizon, 2022 is shaping up to be just as disruptive, and FSOs must be prepared. Today, FSOs stand at the front line of America's…
Everything FSOs Need to Know About CMMC 2.0
In November 2021, the Department of Defense (DoD) introduced a major update to the Cybersecurity Maturity Model Certification (CMMC): CMMC 2.0. According to the original announcement, this update included significant changes from the previous version of CMMC (1.02), including reduced dependence on third-party assessment, and the elimination of security controls unique to CMMC. In December 2021,…
The History of DISS and the Future of Clearance Reform
After more than a decade of preparation, the Defense Counterintelligence and Security Agency (DCSA) finally rolled out the Defense Information System for Security (DISS) this March. Fulfilling a major goal of the Trusted Workforce 2.0 initiative, DISS has risen to replace the Joint Personnel Adjudication System (JPAS) as the Defense Department's security clearance and access database, solving…
Cybersecurity Awareness Month: Zero Trust Security and Insider Threats
Following the SolarWinds attack in December of 2020 – and the Colonial Pipeline attack 5 months later – the government has been working overtime to stem the tide of threats to America's national security. In May 2021, the White House issued Executive Order (EO) 14028, with a long list of reforms intended to strengthen cybersecurity across federal agencies. Now, some of…
How Cleared Organizations Should Prepare for Supply Chain Attacks
Following the SolarWinds attack in December of 2020, regulators have been pushing for stricter supply chain risk management (SCRM) practices across federal organizations, beginning with a list of recommendations from the Government Accountability Office (GAO). But since the publication of this list, it has become clear that agencies are struggling to comply in a timely way. In response, the…
How CMMC is Changing Facility Security in 2021
It's been nearly a year since the Cybersecurity Maturity Model Certification (CMMC) went into effect. Announced by the Department of Defense (DoD) in 2019, the program is an ambitious effort to consolidate security regulations across the Defense Industrial Base (DIB) and supplant the more familiar NIST SP 800-171. Now, implementation is well underway, and the certification…
FSO Tip: Managing Forms with Portal Commander
Today's Facility Security Officers (FSOs) have a tough job. Not only must they ensure that their facilities comply with increasingly strict security regulations, but they must also assist with employee training activities, protect classified information, conduct self-inspections, and much more. With so many things to do, FSOs don't have all day to spend in front…
Top 6 Cybersecurity Threats for 2023
MathCraft Celebrates Cybersecurity Awareness Month
Governance, Risk, and Compliance: How Access Commander is Effective for Both Security and Compliance
Transitioning from DISS to NBIS: Everything You Need to Know
What is Foreign Ownership, Control, or Influence (FOCI) & How Does it Impact Security Compliance?

redpajama_set_name: RedPajamaCommonCrawl


This is one of the best phone of 2003. I'm a first time Siemens user and I'm very happy with this phone. Aside from bluetooth, this small masterpiece has everything you need! I love it! SL55 and Siemens rules!
I have been very dissapointed with this product and the customer service staff in Jakarta-Indonesia.
I bought the phone about 2 and 1/2 weeks ago and I have been having problems with the phone. I was told by one of the customer service staff that it's a defect product, but they can't do anything.
Nice package in a small phone like this. 3 people in my family are using this phone. Keep it up Siemens!
Design and feature are super! =) I love this phone! The only thing missing is a MP3 player.. if Siemens can add the bluetooth to it.. it will be even better!
Great phone. This is a head turner! I love it's features and the design is so cool! SO far, so GOOD!
sl55 nothing without your touch!!!! kinda hopeless motto from siemens that worry noone would buy their small fone!!!!!
interested to buy one !!! but anyone know how is the screen,coz i see on my friend screen it was a bit fukked up !!! 101by 80 pixel only!!! anyone could tell me how is the camera !!!!!!answer a.s.a.p!!!!
tudor,the sl55 had the digicam it even had the same feature with the s55 digicam!!!!!!!
it is a shame it is without an mp3 player.in my opinion,that takes something away from it---it would have been a real winner cause everything else about it is just perfect!
not bad at all qite cute hay.
I just love it the features are so good.
Can't wait till the red one comes out!!!!
About Siemens. Siemens was the first with the slide keybord. Years ago siemens has an phone with the same idea. So Nokia has stole the idea from Siemens thats for sure.
does this telephone sl55 have downloadable ringtones?
fantastic phone. it does everything. i knew it was small but it shocked me to actually have it in my hand. the only small gripe i have is that the colour pixels are only 4096, not higher. The screen is very large given the smallness of the phone. I love the phone and a lot of people have complimented me on having it.
Well the siemens sl55 is a smart phone as call it and I was wandering if it has a digital camera? If anyboby knows pls. right me.
It is not a copy of the Nokia 7650, in fact Nokia stole the idea of this type of keyboard from Siemens... I suggest you take a look at the Siemens SL10, the first mobile phone with this concept.
this is the crapiest phone ever! a total copy of 7650! i think nokia should design for siemens instead of them designin.......real crap!
Total of 264 user reviews and opinions for Siemens SL55.


Lagoon catamaran is escalating its popularity. People have started owning lagoon 380 or lagoon 500 and so on to have a quality life and luxury. But if you consider buying them as easy charge, make sure you get rid of this notion. Getting a catamaran is not shopping any other luxurious stuff like your designer sofa or anything else; you need to have a fine understanding to make a good deal. Below is a couple of wise points which can assist you in getting a appropriate catamaran.
Carry your practical analysis of your requirements: People like the builders and architects have their own arguments and preferences, however, your requirements would be different. There are couples of boats which can be considered to be proper for summer cruising program, while others can be called as long term boats. These will have superior weight carrying capacity with correct sea keeping qualities or attributes. These choices take account of quiet myriad effects including the volume, tools, construction and performance. Ensure that you are not carried away by any lucrative ads rather be precise about your necessities. There is nothing called a wonderful boat, you have to go as per few considered concessions.
Focus additional on model rather on the equipment or any other factor: Having a amount of comfort equipments can make life easy, radar, watermarked, AC, communication systems and other safety equipments. However, you can add these equipments later which you see lacking in your boat. The thing which you will focus is the model since you cannot replace the hulls or the rig of any boat you procured. Hence it's imperative you count on the model first rather than considering the number of equipments on the boat.
Make sure you judge by visiting the boat rather than any preconception: You will find a quantity of fine appearing lagoon catamaran being sold having an enormous maintenance problem. Hence it's imperative that you visit the boat personally rather than relying on some preconception ideas. You may find the boat appearing in fine quality as far as the design and construction is concerned, however, they may be below the bench mark due to various other reasons. Unless you recognize these factors, superior let alone signing the deal. These things can render you more opportunity to negotiate. Therefore ensure you have a thorough survey in a accurate way even the boats are of recent models. This will also help you get a proper kind of insurance for the boat.
Negotiate using your head: The practice of negotiation in boat buying procedure is pretty common and called as normal. Though do not make this as your principle thing in life. Since you may sometime find the price quoted about the boat appropriately justified with no chance for any kind of negotiation. You may be simply interested in the costing, however, at the end up paying additional without considering the discount. However, if you find the boat has room for negotiations, make sure you do not lose this opportunity of doing it. Don't simply keep on negotiating; make a justified one as easily satisfactory by the seller. Don't forget there are other people too in the queue to make a deal.


text:
Yannick Le Bourdonnec, journaliste économique et écrivain, né le .

Originaire de Carhaix (Finistère), il est diplômé de l'Institut d'études politiques de Paris et titulaire d'une maîtrise de droit public. Journaliste, grand reporter et chef de différents services, notamment à Agra Presse, Les Échos, L'Expansion de 1979 à 1993, et Enjeux Les Échos à partir de 1994, il a également été directeur de la rédaction du Nouvel Économiste entre 2001 et juillet 2002.

Spécialiste des questions agricoles et de développement régional, il a collaboré en tant qu'éditorialiste au quotidien régional Le Télégramme. Écrivain, essayiste, il a également réalisé des documentaires pour la télévision, comme "Au nom du père", une saga sur Édouard et Michel-Édouard Leclerc.

En décembre 2002 il devient associé de l'agence de presse Tapas Presse avec Aymeric Mantoux, Luc Dubanchet et Marc Esquerré. Puis directeur de collection aux Éditions des Syrtes et fondateur de Poleco, une société de conseil en communication et image, il quitte le journalisme pour intégrer le Groupe Publicis, comme associé puis vice-président de Publicis Consultants. Il dirige la société Verbe à partir de . En 2009, il fonde Yannick Le Bourdonnec Conseil, « agence de conseil stratégique en communication corporate », dont il est le président.

Depuis 2005, Yannick Le Bourdonnec préside et anime l'Association des Dîners celtiques.
En , Yannick Le Bourdonnec crée la société YLB Édition et lance le magazine Celtissime. Ce trimestriel se positionne comme le magazine de la culture celtique, il identifie les traces de cet univers ancestral dans notre monde contemporain.

Publications
 Le maire et le développement économique, édité par le Crédit agricole.
 Des paysans heureux, éd. Flammarion, 1994.
 Le Miracle breton, postface de Joseph Le Bihan, éd. Calmann-Lévy 1996.
 Le Printemps des régions : la fin du désert français ?, éd. Calmann-Lévy, 2000.
 Du bruit dans le Landerneau, entretiens de Michel-Édouard Leclerc avec Yannick Le Bourdonnec, éd. Albin Michel, 2004.
 Que veulent donc les Bretons ?, Éditions des Syrtes, 2004.
 Le Paradoxe breton, Éditions des Syrtes, 2004.
 Réussir en toute franchise, Louis Le Duff avec Yannick Le Bourdonnec, éd. Albin Michel, 2006.

Notes et références

Liens externes
  Site officiel de Yannick Le Bourdonnec Conseil, société de conseil en communication stratégique créé en 2009 par Yannick Le Bourdonnec

  Site officiel de Celtissime le magazine trimestriel de la culture celtique

Journaliste français du XXe siècle
Journaliste français du XXIe siècle
Élève de l'Institut d'études politiques de Paris
Économie en Bretagne
Naissance en septembre 1953
Collaborateur des Échos

redpajama_set_name: RedPajamaWikipedia


. · forums · community | osu!
Last edited by Kleracen April 30, 2017 at 6:48:16 AM GMT , edited 1 times in total.
You can, as long as you have obtained permission from the respective authors of the skin elements you used.
Using too many elements from other skins is frowned upon regardless.


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Study looks at survival of coronaviruses in water and wastewater
Photo: Centers for Disease Control and Prevention (CDC)
Through cross-cutting innovations distinctive to the University of Arizona, we will expand the student experience through engagement, advance knowledge through innovations in creative inquiry and collaboration, and forge novel partnerships
Research and development |
Worldwide, seven different coronaviruses are known to infect people and cause illness. Four of the coronaviruses are quite common. Three others, including MERS-CoV (reported 2012), SARS-CoV (recognized 2002) and 2019-nCoV (recognized 2019), emerged in more recent years and are examples of viruses that once infected only animals but made the species jump to humans.
Human coronaviruses commonly cause mild to moderate illness, but a few – including MERS-CoV and SARS-CoV – have been known to frequently cause severe illness.
Most recently, the Centers for Disease Control and Prevention (CDC) reports that it is closely monitoring an outbreak of respiratory illness arising from a novel (new) coronavirus (2019-nCoV) first identified in Wuhan, Hubei Province, China. Chinese authorities identified the new coronavirus, noting that it has resulted in more than a thousand confirmed cases in China, including cases outside Wuhan City. Additional cases have been identified in a growing number of other international locations, including the United States.
Coronaviruses, named for the crown-like spikes on their surface, were first identified in the mid-1960s. In 2008, a study by WEST researchers measured the survival of coronaviruses in water and wastewater and found that: "Coronaviruses die off very rapidly in wastewater, with a 99.9% reduction in 2–3 days…. Survival of the coronaviruses in primary wastewater was only slightly longer than secondary wastewater, probably due to the higher level of suspended solids that offer protection from inactivation."
As a new strain of coronavirus is investigated, the UA study offers helpful information, demonstrating that transmission of coronaviruses tend to be limited in the aqueous environment due to the fact that they are rapidly inactivated in water and wastewater at ambient temperatures. To read the full UA study, click here.
How a forest's smell could help scientists sniff out drought stress
Freshwater habitats are fragile pockets of exceptional biodiversity, study finds
California's 'climate whiplash' has been worsening for 50 years and will continue
Rivers speeding up Arctic ice melt at alarming rate, experts say
Can forests become more resilient to extreme drought?
Jet stream changes could amplify weather extremes by 2060s
Mapping extreme snowmelt and its potential dangers
Longer, more frequent periods of drought plague western United States
Researchers propose climate-smart desert food production model for land and human health
03/04/2020 · Research and develop... · 607
Scientists track coronavirus through wastewater across US
As groundwater depletes, arid American West is moving east
Widespread droughts affect Southern California water sources six times a century
Precipitation extremes driving tree growth reductions
06/09/2019 · Governance · 46 2
Building water-efficient cities requires collaboration
Agrivoltaics proves mutually beneficial across food, water, energy nexus
Fracking has less impact on groundwater than traditional oil and gas production


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Prevention and Health Promotion AdministrationCurrently selected
Reportable Diseases
MDH Home
Environmental Health HomeCurrently selected
Environmental Public Health Tracking
MD Department of the Environment (MDE)
MD Department of Agriculture
MD State Department of Education (MSDE)
MD Department of Natural Resources

Maryland Occupational Health and Safety
What is occupational health and safety?
About 3 million people work in Maryland. Each year, roughly 3% of Maryland workers suffer a work-related injury or illness. Occupational injuries and illnesses are preventable. To protect Maryland's workers, the Maryland Occupational Health and Safety Surveillance Project (OHSSP), with funding from the National Institute for Occupational Safety and Health (NIOSH), does the following:
Reports statistics and indicators on occupational illness and injury rates in Maryland;
Collects reports from health care providers in Maryland who treat patients with reportable work-related illness and injury; and
Provides assistance to Maryland Occupational Safety and Health and other agencies concerned about possible work-related illness.
Occupational Health Indicators
Maryland tracks 20 Occupational Health Indicators to provide more information about the health and safety of workers in Maryland
An occupational health indicator is a measure of a work-related disease or injury, or a factor associated with occupational health, such as workplace exposures, hazards, or interventions, in a workforce population.
These indicators are a set of 19 measures that are used to monitor the health of Maryland's workforce. Each indicator highlights a risk factor or category of injury or illness that can be tracked over time and compared to other states and the nation to highlight both successes and areas that may need additional exploration to protect the safety and health of Maryland's workers.
What should I do if I have an occupational health concern?
Workers or their representatives can file a complaint with Maryland Occupational Safety and Health (MOSH) if they are concerned about workplace safety and health issues. Employers are prohibited from retaliating against workers who submit complaints.
Click here for instructions about filing a complaint with MOSH.
Concerned employees, union officials, or employers can also request a Health Hazard Evaluation (HHE) from NIOSH. NIOSH is the occupational safety research branch of the federal Centers for Disease Control and Prevention (CDC). The purpose of an HHE is to identify workplace health hazards and recommend ways to prevent work-related injuries and illnesses.
Click here for more information about the HHE program at NIOSH.
Information for clinicians
In Maryland, health care providers are required to report known or suspected cases of occupational diseases or health conditions aggravated by workplace exposures. Reports should be sent to the Environmental Health Coordination Program at MDH.
Click here to download the form that clinicians should use to report occupational illnesses and exposures.
Reportable conditions include occupational lung disease, chemical exposures, carpal tunnel syndrome, work-related injuries in persons less than 18 years of age, and work-related disease outbreaks. Detailed reporting guidelines can be found on page 2 of the above form.
Resources by topic in occupational health and safety
Surveillance and Maryland Indicators
Maryland Occupational Health Surveillance Report, 2000-2013
COVID-19
OSHA - Protecting Workers: Guidance on Mitigating and Preventing the Spread of COVID-19 in the Workplace
OSHA - COVID-19 Healthcare Emergency Temporary Standard
MOSH - COVID-19 Facemasks vs. Respirators: Understanding the Difference
Heat Stress
OSHA - Heat Illness Prevention
MOSH - In accordance with House Bill 722, the Maryland Occupational Safety and Health Administration (MOSH) will host four regional informational sessions prior to developing and adopting regulations to protect employees from heat-related stress in the workplace. Learn more.
Opioids
In November 2018 MDH hosted a workshop to address opioids in the workplace. You can find materials from that meeting here and the final report, Opioids in the Maryland Workplace: Challengs and Solutions (August 2019)
HHS -Guide for Clinicians on the Appropriate Dosage Reduction or Discontinuation of Long-Term Opioid
University of Maryland - Evaluation of an online training program focused on health care providers who treat work-related pain/injuries
Zika virus
OSHA - Interim Guidance for Protecting Workers from Occupational Exposure to Zika Virus
Asthma
Asthma in the Workplace: What Every Health Care Provider and Worker Should Know (2015)
Confidential Report of Occupational/Environmental Disease and Reporting Guidelines for Physicians
Baltimore City and DHMH final report on a building investigation (2013)
News
Heat Stress Regional Information Hearings
In accordance with House Bill 722, the Maryland Occupational Safety and Health Administration (MOSH) will host four regional informational sessions prior to developing and adopting regulations to protect employees from heat-related stress in the workplace. Learn more.  (September, 2021)
Resources & Partners
Maryland Occupational Safety and Health (MOSH)
MOSH Employer/Employee Resources
Occupational Safety and Health Administration
For more information, call the Environmental Health Helpline:
1-866-703-3266 or email mdh.envhealth@maryland.gov


I wrote my previous post (The mtDNA clock ticks out of time) on the reliability of the mitochondrial DNA "clock" after doing my research on the D4h3 haplogroup (hg) in America for my post on it [mtDNA D4h3a (Continued)].
At the time, the variability in the amount of mutations along each lineage drew my attention. How could all these living people who came from a common root have differing amounts of mutations? After all, if there was a clock ticking of mutations at a steady rate, then all these people would have the same quantity of mutations. Well they don't. So, I suspected the clock was not reliable.
Between the root at D4 and D1 there are 2 mutations (marked in red on the image as 2 m), and the time that has passed is 23.5 - 17.8 ky. That is: one mutation every 11.72 - 8.9 ky.
Between root D4 and D4h3 there are 7 mutations (7 m), so you would expect them to be 77 to 63 ky apart, but no, surprisinglythis branch is between 500 and 2,500 years YOUNGER than its root. What sort of a tree is this? branches are born before the root?
You could argue that #26 and #10 had accumulated mutations at almost the same speed (barely 10% difference between them) but #17 lineage has mutated twice as fast as the other two did.
Finally, the D4h3a haplogroup is dated at 18 - 14.3 kya, so based on the maximum and minimum mutations mentioned above (12 and 3) the clock can tick anywhere between: 1,191 and 6,000 years per mutation! (compare that to the 11,720 to 8,900 years calculated further up and you can see why the clock is pointless).
Despite these blatant violations of a mtDNA clock I thought that somehow I had misinterpreted the data or not understood how the mechanism works, so I did some research into the "clock" issue. The outcome was yesterday's post which substantiates (based on scholarly papers) that the clock ticks with variable rates.
The sad part is that time and time again, paper after paper I see dates defined for our Most Recent Common Ancestors (MRCA) and entry dates into America based on this non-existent clock.
Take the following example (Behar et al, 2014): "As the clock violation was observed only in a restricted number of specified cases, we applied the best available tools for estimating the ages of ancestral nodes." .
The "best available tools" may surely be the orthodox point of view: A very long (>100 ky) incubation period in Africa, a small failed entry into the Middle East where modern humans briefly occupied the region some 90 kya. A sudden migration "out of Africa" some 50 kya followed by a quick expansion across southern Asia, Europe and Australia. A slower march north across East Asia and Siberia and a very late (<25 ky) entry into America.
I now believe that the mtDNA clock is irrelevant, it is guesswork and adjusted "by hand" to fit the dates defined by the orthodox point of view.
With this in mind, I believe that it is very likely that a group of humans could have left Africa with L3 haplogroup (hg). anytime between the 200 - 80 kya (yes the "failed" migration that took place 90 kya, could have been earlier), and then mutated along the M hg and D hg branches in say, 20 ky, allowing an entry into America of Modern Humans anytimg between 180 and 60 kya.
And I specifically give a very ancient date of 200 kya for the appearance of modern humans based on a Y chromosome haplogroup (Mendez et al, 2013) named A00 which was dated to: "338 thousand years ago (kya) (95% confidence interval = 237-581 kya). Remarkably, this exceeds current estimates of the mtDNA TMRCA, as well as those of the age of the oldest anatomically modern human fossils. The extremely ancient age combined with the rarity of the A00 lineage, which we also find at very low frequency in central Africa, point to the importance of considering more complex models for the origin of Y chromosome diversity. These models include ancient population structure and the possibility of archaic introgression of Y chromosomes into anatomically modern humans. "
An early and rapid dispersion across Central and Eastern Asia could account for ancient Humans such as those found at Zhirendong, China with a minimum age of 100 - 113 kya (Wu et al, 2010). Fossils which prove that the "90 kya" migration into Asia was not a failure, the migrating humans settled in China.
Here we have a good explanation for the high proportion of Neanderthal genome in modern East Asians (Vernot and Akay, 2014) who have +20% more Neanderthal genes than Europeans.
It is a pity that Amerindians were not included in the study, it is likely that they have even more Neander genome than East Asians (based on other traits that they have inherited in a higher proportion than East Asians).
Have you noticed that Native Americans are rarely included in these studies?
Apparently 30% of Neanderthal's genome ended up in humans, of course no individual has more than 1 to 3%, but combine all those bits and pieces and it is one third of H. sapiens genome.
But getting to the point. Vernot and Akay suggest that Asians have more Neanderthal genes because there were two admixture events. One just after modern Humans left Africa and before they split into the Asian and European populations. And another, between Asians and Neanderthals after they split from the forebearers of modern Europeans. This double admixture gave them a higher dosage of Neanderthal DNA.
 Mendez et al., (2013). An African American paternal lineage adds an extremely ancient root to the human Y chromosome phylogenetic tree. Am J Hum Genet. 2013 Apr 4;92(4):637.


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Who should be prioritized for renal transplantation?: Analysis of key stakeholder preferences using discrete choice experiments
Michael D Clark1,
Dennis Leech2,
Anil Gumber3,
Domenico Moro4,
Ala Szczepura5,
Nick West6 &
Robert Higgins6
Policies for allocating deceased donor kidneys have recently shifted from allocation based on Human Leucocyte Antigen (HLA) tissue matching in the UK and USA. Newer allocation algorithms incorporate waiting time as a primary factor, and in the UK, young adults are also favoured. However, there is little contemporary UK research on the views of stakeholders in the transplant process to inform future allocation policy. This research project aimed to address this issue.
Discrete Choice Experiment (DCE) questionnaires were used to establish priorities for kidney transplantation among different stakeholder groups in the UK. Questionnaires were targeted at patients, carers, donors / relatives of deceased donors, and healthcare professionals. Attributes considered included: waiting time; donor-recipient HLA match; whether a recipient had dependents; diseases affecting life expectancy; and diseases affecting quality of life.
Responses were obtained from 908 patients (including 98 ethnic minorities); 41 carers; 48 donors / relatives of deceased donors; and 113 healthcare professionals. The patient group demonstrated statistically different preferences for every attribute (i.e. significantly different from zero) so implying that changes in given attributes affected preferences, except when prioritizing those with no rather than moderate diseases affecting quality of life. The attributes valued highly related to waiting time, tissue match, prioritizing those with dependents, and prioritizing those with moderate rather than severe diseases affecting life expectancy. Some preferences differed between healthcare professionals and patients, and ethnic minority and non-ethnic minority patients. Only non-ethnic minority patients and healthcare professionals clearly prioritized those with better tissue matches.
Our econometric results are broadly supportive of the 2006 shift in UK transplant policy which emphasized prioritizing the young and long waiters. However, our findings suggest the need for a further review in the light of observed differences in preferences amongst ethnic minorities, and also because those with dependents may be a further priority.
In the United Kingdom (UK) in January 2011 there were 6,610 patients awaiting renal transplantation (a figure which had risen by 8% annually since 2004). In the previous year (2009–10), only 1,482 patients received deceased donor transplants, and 1,038 received live donor transplants [1]. A growing imbalance between demand and supply led to the Organ Donation Taskforce Report in 2008 [2] outlining strategies to increase UK organ supply by 50% within 5 years. However, despite the resultant increase in organ supply, demand still continues to outstrip supply. So criteria remain necessary to allocate the limited supply of kidneys which are available for transplantation.
A transplant policy based on efficiency criteria would require that organs be transplanted to patients deriving greatest health benefit. Criteria to address equity of access may conflict with efficiency ones. Patients waiting a long time may be selected for transplant on equity grounds even if someone else, who has not waited as long, would obtain greater health benefits from transplantation. In 2006, UK transplant policy was re-appraised. The previous policy was thought to disadvantage those with less common tissue types and blood groups, especially ethnic minorities. This population is also at higher risk of kidney disease [3]. African Caribbeans and Asians have a 3–4 times greater risk of end stage renal disease [2] related to a higher prevalence of type 2 diabetes [4]. Increased risk of renal disease in these groups is also associated with increased risk of co-morbidities such as hypertension [5] and cardiovascular disease [6]. Moreover, ethnic minorities donate fewer organs [6], so individual patients are less likely to obtain closely matched transplants.
The 2006 re-appraisal led to reduced priority being attached to HLA matching in the UK, and allowed consideration of other criteria [7]. The resulting guidelines [8] suggested more priority should be given to long waiters and paediatric and younger adult recipients. Research from the USA and Australia had, indicated such changes would be acceptable to professionals and patients [9, 10]. Although there was some excellent UK research to inform prioritization [11], this research did not adopt DCE methodology, unlike ours.
In this analysis we have used Discrete Choice Experiments (DCEs) in order to establish respondent's valuation of different kidney transplant allocation criteria, and how they might trade-off gains in relation to one transplant allocation criterion, for losses in relation to another transplant allocation criterion. DCEs involve the application of a stated preference technique in order to establish a respondent's valuation of attributes or characteristics of a good or service or health state. DCEs are increasingly being used to address priority setting issues in healthcare, both in primary care [12], and secondary care [13, 14]. Some DCE research has been published on general transplantation issues, including assessing factors influencing willingness to donate body parts [15] and a DCE to establish UK priorities for liver transplantation [16, 17]. In renal transplantation, the first DCE findings internationally emanated from our study conducted in the UK [18]. This publication focused solely on assessing whether patient preferences varied by ethnicity and gender. More recently, DCE research has been undertaken in Canada relating to patient and healthcare professional preferences for chronic kidney disease (CKD) care more generally (although not specifically focused on kidney transplantation) [19].
The current paper provides more extensive evidence on the preferences of various stakeholder groups alongside those of patients than our earlier paper [18]. These groups include patients, renal healthcare professionals, renal carers, and live donors / relatives of deceased donors. Unlike the general public (who may lack personal experience of renal disease) all these 'expert' stakeholder groups will have a direct interest in priorities for kidney transplant allocation, either because they have renal disease themselves (patients) or care for those with such a disease (renal carers/renal healthcare professionals). Moreover, live donors or relatives of deceased donors are concerned to ensure kidneys are appropriately allocated. Therefore research to improve understanding of the preferences of these different stakeholder groups should help inform the policy debate about transplantation.
This Discrete Choice Experiment (DCE) study assumed that respondents' valuations of different kidney transplant allocation criteria can be decomposed into component parts known as attributes. The DCE involved respondents making repeated hypothetical pairwise choices in which they expressed their stated preferences about which of two transplant recipients (differing in these pre-defined characteristics or attributes) should receive a kidney. DCE respondents' trade-offs were established so that weightings given to different recipient characteristics (attributes) could be quantified. The pilot study for this research began in 2005; the main study started in 2006 with final data analysis completed during 2007–09. Ethical approval was obtained from Warwickshire Local Research Ethics Committee (reference number 05/Q2803/86).
Pilot exercise
Although some DCEs have been undertaken without undertaking a pilot exercise first, the benefits of initial piloting and analysis of pilot data econometrically are considerable. We therefore undertook a rigorous pilot exercise using both qualitative and quantitative methods. For full details see Additional file 1. Attributes tested in the pilot included waiting time, tissue match, employment status, number of dependents, recipient age and various diseases affecting recipient health. Attributes and level selection was mainly informed by discussions with clinicians. Given the number of attributes and levels selected, we needed to design a DCE questionnaire which used a limited number of different choice scenarios, which would be sufficient to infer choice information. However, we also invited pilot respondents to suggest other possible attributes and rank them in order of priority (alongside those already in the questionnaire). Attributes tested in the pilot included waiting time, tissue match, employment status, number of dependents, recipient age, patient compliance, whether illness could be regarded as 'self-inflicted' and various diseases (co-morbidities) affecting recipient health. Early during the pilot some respondents expressed disquiet about the employment attribute, arguing it represented unwarranted discrimination against the retired or those unemployed because of illness. We therefore asked all respondents whether this should be included. Most respondents said 'no', so this was omitted. We used the computer package SPEED (software designed to establish choice sets for DCEs [20]) for the pilot DCE to generate such an orthogonal main effects design. We paired choices generated by SPEED to minimize attribute overlap and level imbalance [21]. The pilot exercise analysed 60 responses to establish appropriate attributes and levels for use in the final DCE questionnaire.
Identifying attributes and levels for final DCE
The pilot exercise analyzed 60 responses to ascertain attributes and levels for the final DCE using Random Effects Probit. All the attributes (with the exception of the employment status attribute) proved significant at the 5% level (i.e. implying that the attribute would affect a respondent's choice of who to prioritize for kidney transplant). Final pilot findings from attribute rankings indicated that the employment status attribute was far from highly ranked and it was therefore removed. The following inclusions were warranted, with some limited changes. Respondents thought people with dependents ought to be prioritized, but most considered adult as well as child dependents should be included, so this attribute was amended to include adults. Age was considered relevant, but the recipient age ceiling was reduced to 65 because clinicians indicated that transplantation was less likely amongst over 65s. Although highly ranked separately, when combined the life expectancy and other recipient diseases (non-CKD co-morbidities) attributes resulted in unrealistic DCE scenarios. For example, one pairwise choice resulted in respondents choosing between a 70 year old with severe arthritis with 12 years life expectancy, and a 45 year old without co-morbidities with a shorter life expectancy. Such a comparison did not make sense since a 45 year old without co-morbidities would be expected to have a longer life expectancy. So, the life expectancy attribute was replaced with one indicating whether a potential recipient had diseases predominantly affecting life expectancy. This provided more realistic scenarios and improved DCE design.
Other attributes highly ranked in the pilot exercise included 'patient compliance' and whether illness was 'self-inflicted'. However, advice from medical professionals highlighted the fact that unlike 'liver transplantation' renal transplantation was rarely required because of alcohol or drug misuse, so this was not a particularly relevant consideration to underpin attributes and levels for the DCE. Also patients who are thought likely to abuse their bodies or be non-compliant would not be transplanted. On this basis, it was decided to exclude this attribute.
Table 1 lists the final attributes and levels selected for use in the DCE questionnaires. Copies of the questionnaires are provided in Additional file 2.
Table 1 Details of final attributes and levels used for Discrete Choice Experiment (DCE) questions
The pilot questionnaire contained an explanatory preamble which described the attributes and levels included. Transplant survival rates were presented from UK Transplant and it was explained that these were contingent upon donor/recipient tissue match. Although transplant survival rates were available for longer time horizons, we wanted to avoid information overload and therefore only presented 12 month survival rates. The pilot confirmed that explanatory information was easy to understand.
Final Discrete Choice Experiment (DCE)
We used a binary dependent variable model in order to force a choice. This is because, in reality, transplant decisions have to be made and medical professionals face a forced choice when allocating kidneys because of donor scarcity. Moreover, pilot interviews revealed that many respondents felt uncomfortable with deciding who to transplant. Therefore, it was judged that including a 'cannot decide' option might have triggered such a response from people who in reality were not indifferent. An alternative would have been to allow choices between more than 2 potential recipients using a multinomial model, or to have more attributes and levels; but this would have complicated decision making [22]. Moreover, since many renal patients suffer from fatigue we wanted to avoid complicated decisions, because when complexity increases there is evidence that respondents may be more inclined to use simplifying heuristics [23] compromising response reliability. Copies of the final DCE questionnaires are provided in Additional file 2.
The final DCE design was again an Orthogonal Main Effects Plan (OMEP) design involving independent valuation of attributes. To ensure a perfectly orthogonal design and improve efficiency we used an OMEP design supplied by leading DCE designers [24] rather than SPEED as used in the pilot exercise. We also blocked 18 choices into 2 blocks of 9 questions (versions A and B) to reduce respondent fatigue and limit the patient questionnaire to 10 pages. Respondents were asked to choose between transplanting patient A with particular attribute levels or patient B with different levels (see Additional file 2 for examples).
Questionnaire distribution
We included an information leaflet and freepost envelope in the National Kidney Federation's newsletter 'Kidney Life' (circulation c.20,000). The leaflet invited patients, carers, donors or healthcare professionals to request a DCE questionnaire. Individuals who replied were sent a questionnaire, along with a covering letter, consent form, and freepost reply envelope for the completed questionnaire. Individuals who did not return a questionnaire were not contacted again. Questionnaires were also posted directly to members of the British Organ Donor Society, healthcare professionals listed in the UK Transplant service directory, and to non-transplanting units with transplant coordinators or transplant physicians. Relatives of deceased donors were targeted via the British Organ Donation Society (BODY). To increase ethnic minority responses we provided questionnaires in a person's preferred language. A reputable translation organization was used to translate questionnaires in Punjabi, Hindi, Bengali, Gujarati and Urdu. A bilingual researcher administered the questionnaires upon non-English speaking patients and checked the questionnaire's translation accuracy. We obtained additional responses from Ealing NHS Trust, and from University Hospital, Coventry using translated questionnaires to increase levels of ethnic minority participation.
Assessing the representativeness of respondent sample
In order to assess representativeness, we used UK Renal Registry data (when available); figures were extracted for transplant success rates. Other relevant patient data were not available i.e. patients with failed transplant, awaiting a transplant, on dialysis, or with kidney disease not requiring dialysis. UK Renal Registry data also does not record detailed ethnicity information for some sub-groups of respondents, so detailed patient data is not available for all sub-groups.
Econometric / statistical analysis models
Two models were used for analysis (for full details refer to Additional file 3). Random Effects probit (model 1 and 2) was used to establish different stakeholder preferences. Model 1 used a series of dummy variables to ascertain whether preferences for specific attributes differed amongst carers, donors/relatives of deceased donors, and healthcare professionals compared with the patient respondents group.
Model 2 compared the preferences of ethnic minority and white majority patients. Again a series of dummy variables were used to establish whether preferences differed between ethnic minority and other patients for specific attributes.
Marginal Rate of Substitution (MRS)
Data were analysed in terms of a measure of marginal rate of substitution (MRS) which related differences in other attributes (potential transplant priority criteria) to waiting time for renal transplants. Analysis examined whether differences in MRS were statistically significant between stakeholder groups for specific attributes (using Wald tests). We also used the Delta method [25] with command 'nlcom' in STATA to estimate 95% confidence intervals. Full details of the approach used to derive MRS are provided in Additional file 4 (Table 1). Wald tests using 'testnl' in STATA were used to establish whether MRS differed significantly between groups; comparing patients with carers, donors and healthcare professionals (model 1); and ethnic minority patients vs. white majority patients (model 2). For example, to establish whether preferences for tissue matching differed between ethnic and non-ethnic minorities (model 2), the null hypothesis was that tissue match MRS was identical for both (p ≤ 0.05 indicated a difference at the 5% level).
Table 2 presents respondent characteristics. We obtained 908 patient responses; 18 additional responses from Ealing NHS Trust and 5 from University Hospital, Coventry were obtained using translated questionnaires. UK Renal Registry data [26, 27] was used to assess patient sample representativeness. 508/908 patient respondents (55.9%) were male, 397/908 (43.7%) were female, and 3/908 (0.3%) not reported. Renal Registry data [26] similarly indicates a slightly higher proportion of male than female patients across age groups. The average patient age in the sample was 54.9 years (median 57 years), coinciding with the Renal Registry data median age (57.3 years) [27]. Of the 895/ 908 patients indicating ethnicity, 799/895 (89.3%) were white British and 27/895 (3%) were other white background (e.g. Irish), giving a total of 92.3% white patients. UK data [26] indicates that 79.7% of renal patients are white. So white respondents are over-represented in our survey. Overall, 69/895 (7.7%) patients indicating ethnicity were non-white, compared with an expected 20.3% incidence rate [26], and 50/69 of the non-white patients were South Asians (5.6% of those indicating ethnicity) compared to an anticipated 10.5% [26].
Table 2 Details of characteristics of questionnaire respondent samples
In terms of transplant history, the patient sample comprised: 468/908 (51.5%) with successful transplants, 118/908 (13%) whose transplant had failed and 279/908 (30.7%) awaiting transplant (average wait 22.6 months). Some patients whose transplant had failed also reported they were 'awaiting transplantation'. Of the remainder, 237/908 (26.3%) were undergoing dialysis without transplantation and 57/908 (6.3%) had kidney disease not requiring dialysis. Renal Registry data [27] indicates 46.9% of patients have successful transplants (close to our figure). There are no data for the other patient characteristics. Amongst non-whites (including Asians) our sample included 18/69 patients (26%) with successful transplants and 10/69 (14.5%) whose transplant had failed; 35/69 patients (50.7%) were awaiting a trans-plant on dialysis (average wait: 21.45 months) and 3/69 (4.3%) had kidney disease not requiring dialysis. These statistics cannot be compared with UK Renal Registry data since ethnicity is not recorded for such sub-groups [27]. However, a lower percentage figure for transplant success and a higher figure for patients awaiting transplants might be expected (ethnic minorities donate fewer organs and transplants are less likely to be closely matched).
Of the 48 donor respondents, 21 were live donors and 27 relatives of deceased donors. Healthcare professionals comprised: 9 renal surgeons, 37 renal physicians, 17 transplant co-ordinators, 31 nurses, 9 clinical scientists, 1 GP, 1 dietician, 1 network manager, 1 transplant scientist, 1 medical student, 1 transplant immunologist, 1 tissue typer, 1 clinical audit manager, 1 renal technologist and a pathologist.
Table 3 presents model 1 results including MRS 95% confidence intervals (CIs). Variables are as listed in Table 1.
Table 3 Model 1: results and MRS (i.e. utility value of other attributes expressed in terms of trade-off with 1 year waiting time) for patients, carers, donors, and healthcare workers
Coefficients are as indicated in Table 1. MRS in Table 3 indicates indirect utility values for changes in attributes (for direction of change see Table 1) relative to values for prioritizing a recipient waiting an extra year for transplantation (Additional file 4: Table 1 presents more detailed MRS formulae). In effect MRS indicates the rate at which the respondent group in question is willing to trade-off gains in relation to one criterion against losses in relation to another (in this case the amount of time waiting). MRS in Table 4 is expressed in terms of 1 or 5 year waiting times. Measures of 'goodness of fit' for model 1 (Random Effects probit) show that 63% of actual values are predicted by the model, and McFadden's R2 = 0.1088. A likelihood ratio test for the joint significance of the dummy variables has 27 degrees of freedom with a critical value of 40.11, compared with λ=71.90, so the dummy variables are jointly significant. The tissue match coefficient (tiss) in Table 4 indicates the impact of a 1% difference in 12 month kidney survival. Difference in survival rate between a perfect vs. favourable match is 1%, so the MRS figure of 1.41 (Table 3) also appears in Table 4 for 'Prioritizing perfect not non-favourable tissue matches'. Table 4 indicates MRS for the 'Prioritizing someone with a favourable not non-favourable match' (1.41 × 3 [a 3% difference in kidney survival rate] = 4.23). It also shows how much respondents value other changes in attributes compared to a 1 year wait (column 2). Moreover 5 year MRS figures are presented in Table 4 (column 3). If waiting time increases 5 fold, MRS for a 5 year wait is 1/5th of 1 year MRS. 95% CIs for 5 year MRS are 1/5th of 1 year. The CIs define the range within which MRS figures must lie (to be 95% confident).
Table 4 Model 1: MRS (i.e. utility value of other attributes expressed in terms of trade-off with 1 year or 5 year waiting time) for patients, carers, donors and healthcare workers
When interpreting results it might be expected that, in general, transplant preferences would lie in certain directions. On efficiency grounds improvements in kidney survival should be positively valued, and therefore respondents should generally prefer transplants with the highest chance of success. But, some stakeholder groups might not exhibit this preference if there is a lack of organs closely matching their own requirements. We might expect respondents to prioritize those waiting longer for a transplant on equity grounds, and therefore would anticipate a positive coefficient on a one year reduction in waiting time. It might also be considered that recipients with more dependents should be prioritized because more people would benefit from a recipient's improved health. In contrast, all other things being equal, one might expect older patients to benefit less because they have a lower life expectancy, so the coefficient on reductions in recipient age would be expected to be positive. Finally, for efficiency reasons respondents might prioritize more highly those with fewer or no disease(s) affecting life expectancy over those with moderate diseases.
Patients' MRS figures (Table 4) suggest, all other things being equal (ceteris paribus), that patients would prioritize recipients with perfect over favourable tissue matches (tiss) more than those waiting an extra year (1 year MRS =1.41, exceeding indirect utility from avoiding a 1 year wait of 1.00). However, if a favourably matched patient were to wait 5 years longer, they would be a higher priority than the perfect match (MRS = 0.28) <1. Similarly, prioritizing someone with a favourable not non-favourable match (ceteris paribus) is valued more than prioritizing someone waiting for 1 year (MRS = 4.23) as it exceeds 1, the utility from a 1 year wait. But if a potential recipient waited 5 years longer, prioritizing the longest waiter is optimal (MRS = 0.85) < 1.
Patients also prioritized someone with an extra dependent more than waiting a year longer (MRS for 'dep'=1.43). However, if a potential recipient waited 5 years longer MRS = 0.29, for an extra dependent, so prioritizing an extra dependent is a lower priority. Prioritizing a recipient who is 1 year younger is valued less than a 1 year or 5 year reduction in waiting time ('age' 1 year MRS = 0.16, 5 year = 0.03). Patients would also not prioritize those with no vs. moderate diseases predominantly affecting life expectancy ('dis 1' is insignificant), but would prioritize (dis2) those with moderate rather than severe diseases predominantly affecting life expectancy highly (1 year MRS = 15.32; 5 year MRS = 3.06). Thus, someone with a moderate, not severe, disease predominantly affecting life expectancy is prioritized (MRS >1).
Paradoxically, for diseases predominantly affecting quality of life, rather than life expectancy, patients prioritized those with moderate not no disease ('ill1' has a 1 year waiting time MRS of −2.73), perhaps because many patients have moderate co-morbid diseases. However, 5 year MRS equals −0.55, so long waiters are a higher priority than those with moderate rather than no disease affecting quality of life. Finally, patients prioritized those with moderate rather than severe diseases predominantly affecting quality of life ('ill2' 1 year MRS = 4.18; 5 year MRS = 0.84). So, ceteris paribus, someone with moderate not severe disease would be a higher priority than someone waiting 1 year longer (MRS>1), but a lower priority than someone waiting 5 years longer (MRS < 1).
Carer results (Tables 3 and 4) were compromised to some extent by the smaller sample size (n=41), so MRS was only significant for 1 variable - prioritizing those with dependents (dep) (Table 3, column 5). The fact that other MRS figures are insignificant may partly be attributable to the size of sample. Wald test results (Table 3, column 7) indicate statistically significant differences in MRS between other stakeholder groups and patients (5% level). The results presented in Table 4 suggest MRS for prioritizing perfect over favourable tissue matches is lower amongst carers than patients (1 year MRS = insignificant vs. 1.41; 5 year MRS = insignificant vs. 0.28); and lower for prioritizing favourable over non-favourable matches (1 year MRS = insignificant vs 4.23; 5 year MRS = insignificant vs. 0.85). Moreover, Wald tests (Table 3, column 7) show carers' preference for prioritizing those with dependents is less than patients' (1 year MRS = insignificant vs. 1.43; 5 year MRS = insignificant vs. 0.29). Wald tests also show that carers prioritize those with moderate not severe diseases predominantly affecting life expectancy (dis2) more than do patients (1 year MRS = 16.79 vs. 15.32; 5 year MRS = 3.36 vs. 3.06).
Donor response analysis was also compromised by a smaller sample size (n=48). This may explain why MRS (MRS for 1 and 5 years in Table 4) is only significant for 2 variables ('dis2' and 'ill2'). Once again, Wald tests (Table 3, column 4) suggest that donors value tissue match (tiss) less than patients (1 year MRS = insignificant vs. 1.41; 5 year MRS = insignificant vs. 0.28) for perfect not favourable matches, and also value favourable not non-favourable matches less (1 year MRS = insignificant vs. 4.23; 5 year MRS = insignificant vs. 0.85). Similar to carers, Wald tests (Table 3, column 4) indicate donors value prioritizing dependents (dep) less than patients do (1 year MRS = insignificant vs. 1.43; 5 year MRS = insignificant vs. 0.29). They also suggest that donors value prioritizing the young (age) less than patients (1 year MRS = insignificant vs. 0.16; 5 year MRS = insignificant vs. 0.03). Donors, in contrast to carers, prioritize those with moderate rather than severe co-morbidities predominantly affecting life expectancy less than do patients (1 year MRS = 11.54 vs. 15.32; 5 years MRS = 2.31 vs. 3.06). Wald tests do not indicate other differences.
Analysis of healthcare professionals' responses indicates MRS is significant for 6/7 variables (Tables 3 and 4) and Wald tests (Table 3, column 7) suggest healthcare professionals' preferences differ from those of patients for 5/7 variables. Professionals value prioritizing those with better tissue matches 'tiss' less than do patients (1 year MRS = 1.27 vs. 1.41; 5 year MRS = 0.25 vs. 0.28) for perfect not favourable matches, and prioritize favourable vs. non-favourable matches less (1 year MRS = 3.82 vs. 4.23 ; 5 year MRS = 0.76 vs. 0.85). Wald tests also indicate healthcare professionals prioritize those with dependents (dep) more (1 year MRS = 1.56 vs. 1.43; 5 year MRS = 0.31 vs. 0.29), and younger recipients (age) more (1 year MRS = 0.48 vs. 016; 5 year MRS = 0.10 vs. 0.03). They would also prioritize (dis1) those with no vs. moderate diseases affecting life expectancy whereas patients would not (1 year MRS = 4.50 vs insignificant; 5 year MRS = 0.90 vs. insignificant). Similarly, healthcare professionals also prioritized (dis2) those with moderate rather than severe diseases affecting life expectancy more than patients did (1 year MRS = 19.42 vs. 15.32; 5 year MRS = 3.88 vs 3.06). However, there was no evidence that healthcare professionals would prioritize recipients with co-morbid diseases predominantly affecting quality of life differently from patients (Wald tests for ill1 and ill2 are insignificant). Importantly, the fact that healthcare professionals exhibit statistically significant differences to patients for 5/7 variables suggests that, if healthcare professionals' preferences were to prevail in transplant decision-making, this could result in transplant allocation decisions which inadequately reflect patient preferences.
Measures of 'goodness of fit' for model 2 indicate 62.09% actual values are predicted by the model, and McFadden's R2 = 0.133. A likelihood ratio test for the significance of the dummy variables has λ = 35.83, which compares with a critical value for 9 degrees of freedom of 16.92, so the dummy variables are jointly significant.
Table 5 compares ethnic minority and white majority patients (model 2). Coefficients are as defined in Table 1, and MRS specified in Additional file 4. Overall 3 dummy variables (tiss, dep and dis2) were significant, but Wald tests (Table 5, column 6) suggest more differences including the following. Ethnic minorities do not prioritize recipients with better tissue matches (tiss) but the majority population do (1 year MRS = insignificant vs. 1.54; 5 years MRS = insignificant vs. 0.31 for perfect rather than non-favourable matches). For favourable, rather than non-favourable, matches only white majority patients valued favourable matches significantly (1 year MRS = insignificant vs. 4.64; 5 years MRS = insignificant vs. 0.93). This is perhaps because ethnic minorities are disadvantaged if a close tissue match is required, due to a lack of ethnic minority donors. Wald test results indicate that MRS for prioritizing younger (age) rather than older recipients differs only marginally between ethnic minority and other patients (1 year MRS = 0.15 vs. 0.16; 5 year MRS = 0.03 vs. 0.03). Finally, Wald tests also suggest ethnic minority patients value prioritizing recipients with moderate vs. severe diseases (dis2) affecting life expectancy less than other patients (1 year MRS = 10.25 vs. 15.86; 5 year MRS = 2.05 vs 3.17). Once again, this is perhaps linked to the higher prevalence of severe diseases / co-morbidities predominantly affecting life expectancy amongst ethnic minorities. Wald tests did not indicate that valuation of other attributes varied by ethnicity.
Table 5 Model 2: Patient values vs. those of ethnic minorities (96 out of 908 are ethnic minorities)
This study is unique because, although DCEs have been used in relation to liver transplantation to identify public [17] and patient [16] preferences, this is the first application of DCEs exclusively relating to prioritizing renal transplants. Moreover, the detailed comparisons between stakeholder respondent groups are unprecedented.
Usually when DCEs are used to address healthcare issues they look at patient preferences. In contrast our study compares preferences across a range of different stakeholder groups, deploying a new approach that had not been deployed in kidney transplant research before this project. The DCE approach allows for comparison of preferences between groups, and assessment of whether differences are statistically significant. Importantly, our findings indicate when stakeholder groups' preferences differ. This means that DCE studies that only elicit preferences for one group may fail to take into account preference heterogeneity. Establishing whether preferences vary between stakeholder groups (especially patients and healthcare professionals) is important for policy and practice.
Although DCEs are increasingly used in health services research, one potential limitation can be the sensitivity of results to the choice of attributes presented, since it is only possible to indicate trade-offs in relation to the actual attributes selected. Therefore, it is essential to consult a wide range of opinion during the attribute selection process, including patients and professionals, before deciding upon which attributes to include. The present study included such a process. Constructing a robust DCE also requires that the choice of attributes has emerged from a thorough pilot exercise. In the present study, a great deal of time was invested in piloting the questionnaire to try to ensure that the range of attributes and levels identified for inclusion in our DCE questionnaires was appropriate.
Our analysis of patient responses showed that respondents valued prioritizing patients with closer tissue matches, but also valued other factors significantly including prioritizing: long waiters; those with child or adult dependents; and younger recipients. Furthermore, in terms of co-morbidities affecting life expectancy, individuals with moderate diseases were prioritized over those with severe diseases, but those with moderate diseases were not prioritized over those with no such disease. In terms of diseases predominantly affecting quality of life (rather than life expectancy) patients prioritized recipients with moderate rather than no disease, and those with moderate rather than severe disease. However, for ethnic minority patients our findings demonstrate that, unlike other patients, this group did not value tissue match significantly. They also valued prioritizing those with severe rather than moderate disease affecting life expectancy less than other patients did.
We are also able to report on the preferences of carers. Although the sample was small (n = 41), it was sufficient to establish some statistically significant differences when compared with patients' responses, but probably insufficient for all differences in preferences between carers and patients to be demonstrated in a statistically significant manner. The number of carer responses obtained via our request in the publication 'Kidney Life' was probably limited by the fact that this publication is read more by patients than those who care for renal patients. An alternative strategy would have been to ask patient respondents to supply the name and address of their carer (if applicable) to approach. However, despite the fact we only had 41 carer responses, this data was sufficient to establish that some carer preferences differ significantly from those of patients. In contrast to patients, carers did not value prioritizing those with better tissue matches or those with dependents. But, they did value prioritizing those with moderate not severe diseases affecting life expectancy more than patients. Whilst it is interesting that carer preferences differed from those of patients, patient preferences are clearly more important in terms of decisions on kidney transplant criteria.
In terms of donor preferences, the sample size (n = 48 donor families / live donors) was sufficient to discern that some preferences differed in a statistically significant manner compared to those of patients. Our findings indicate that donors, like ethnic minority patients, did not value prioritizing better tissue matches significantly. They also valued transplants to those with dependents, younger recipients, and those with moderate rather than severe disease affecting life expectancy more than patients did. Donor preferences are important to establish because without donors transplantation programmes cannot continue. It might have been possible to obtain a larger sample of this stakeholder group if we had targeted people on the organ donor register as well as actual donor families and live donors, this would have increased statistical power thereby potentially allowing us to establish other statistically significant differences in preferences.
The number of responses from healthcare professionals (n = 113) was more than adequate to discern preferences for the group as a whole. In terms of overall preferences, healthcare professionals' preferences differed from those of patients in that professionals valued prioritizing better tissue matches less than patients did, but valued prioritizing those with dependents more. They also prioritized those with no rather than moderate diseases predominantly affecting life expectancy whereas patients would not; and prioritised those with severe rather than moderate diseases affecting quality of life more than patients. Unfortunately, the sub-sample of renal physicians (as opposed to healthcare professionals more generally) was not large enough to establish how their preferences might differ from those of patients. Given that it is renal physicians who are involved in decisions about allocating kidneys, more detailed information on the preferences of this important group of healthcare professionals would have been useful.
The difference between patients and healthcare professionals in prioritizing recipients with diseases affecting quality of life may be rationalized if patient preferences are biased due to many individuals in the patient group having moderate disease. However, it is less clear why healthcare professionals place less emphasis upon closeness of donor–recipient tissue match. In this respect, our findings indicate that if transplant allocation decisions and policies are based solely on healthcare professionals' own preferences this may conflict with patient wishes.
In terms of relevance for transplant policy, our DCE study was not intended to identify specific individuals "who should be prioritized for renal transplantation". Rather, it aimed to identify certain potential characteristics of kidney recipients characteristics which different stakeholder groups consider should be prioritized, and therefore suggest potential transplant recipient groups who ought to be made a higher or lower priority for transplantation. It is reassuring that our findings are broadly supportive of the 2006 revisions to UK kidney transplant policy in terms of prioritizing long waiters and young adults. However, although our analysis shows that this can be justified, it also suggests that other criteria (i.e. prioritizing those with dependents) ought to be considered.
Our findings can be considered alongside a number of earlier non-DCE studies. An Australian-based renal study unlike ours adopted a general public perspective [10]. Respondents were found to prioritize long waiters and the young, but had a split verdict over whether to prioritize those with children. Similarly, renal research into African Americans' preferences [9] indicates that kidney allocation based upon HLA matching is considered unfair. However, at the same time, African Americans did not want to receive organs with lower survival rates; note since this paper was published (1997) graft survival for poorer matches has improved. More recently, a 2005 Glasgow renal study [11] has used a non-DCE scenario approach to consider allocation of deceased donor kidneys for transplantation. Interestingly, certain findings from this research conflict with our results (i.e. tissue matching was not a major allocation criterion) although, like our findings, the researchers reported that emphasis was placed on prioritizing long-waiters (albeit defined by time on dialysis, not on waiting lists). One DCE study, a 2010 Canadian article on patients with chronic kidney disease [19] has reported that respondents preferred to prioritize kidney transplants on the basis of a 'best match' rather than 'first come, first served.' However, in contrast to our study, this particular DCE considered a wide range of attributes relating to CKD in general (including organ procurement and the organization of care) and as such could provide only a very limited indication of preferences for kidney transplant allocation. The DCE included only one attribute relating to kidney transplants ("How should deceased donor kidneys for transplantation be allocated for transplantation") with just two possible levels 'best match', or 'first come, first served.' Moreover, unlike our DCE study which furnished respondents with information on the likelihood of kidney transplants being successful for non-favourable matches, it is unclear whether similar information was provided in the Canadian study to ensure fully informed responses.
Interestingly, a recent article which discusses new allocation concepts [28] emphasises efficiency criteria related to maximising health gains (i.e. getting the most life years from organs available for transplant). Whilst maximising life years or Quality Adjusted Life Years (QALYs) may be a legitimate transplant policy objective (and is supported by our findings in the sense that respondents value prioritizing younger patients in our DCE analysis), it is clear from our results that stakeholders also value equity considerations (i.e. avoiding patients having to face excessive waiting times). This is something which comes out strongly in our analysis of different stakeholders' priorities, but would be neglected in an approach which focuses upon maximising health gains. Furthermore, the findings reported here and in our earlier analysis [18] indicate that, although both time spent waiting and the quality of tissue match between donor and recipient are of importance to healthcare workers and patients, amongst ethnic minority patients closeness of tissue match is not a significant determinant of patient preferences.
The findings reported in this paper, and those reported in our earlier analysis [18], suggest that both time spent waiting and the quality of tissue match between donor and recipient are of importance to healthcare workers and to non-ethnic minority patients, but that amongst ethnic minority patients closeness of tissue match is not a significant determinant of patient preferences. As DCEs can be used in order to quantify key stakeholders' willingness to 'trade-off' between conflicting transplant allocation criteria, data from the present study could in principle be used to underpin kidney transplant allocation policy thereby increasing transparency [29]. For example, if the weightings obtained were to be used for informing organ allocation decisions then, rather like the MELD scores which underpin Liver transplant policy in the USA, this would increase transparency. However, such an approach would sideline other valid evidence [3, 9–11, 28, 30] resulting in a more mechanistic process. In our view therefore such a mechanistic approach is inappropriate.
The United Network for Organ Sharing (UNOS) policy takes into account the length of time spent on the waiting list; whether the potential organ candidate is a child; body size of both donor and candidate; tissue match between donor and candidate; blood type; and blood antibody levels. Although we wanted to avoid being too prescriptive about how either UK or USA transplant policy should be changed, in the light of our findings it is clear that changes to USA transplant policy in 2003, and UK transplant policy in 2006, have already led to a shift away from considerable reliance upon transplanting on the basis of a close HLA tissue match between donor and recipient. Changes to USA policy (7th May 2003) involved an elimination of HLA-B similarity as a transplant allocation criterion [30]. This was because improvements to medications used to prevent transplant rejection reduced the benefit that previously had been associated with HLA-B matching (which had discriminated [perhaps unintentionally] against ethnic minorities). The current US policy has been suitably characterized as one of "Equal opportunity supplemented by fair innings" [31] and reported to have "improved access to transplantation for all minority groups" in the USA [28]. The first 6 year follow-up reported that the 2003 change in policy "has improved access to transplantation for all minority groups and has not been associated with a decrease in 2 year graft survival" [30]. Decreased emphasis on close tissue matching and more emphasis on prioritizing long waiters is similarly reported to have reduced the extent to which ethnic minority groups are disadvantaged in the UK [18].
However, our finding that UK ethnic minority patients do not value prioritizing recipients on the basis of closeness of tissue match indicates that there may be scope in the USA and the UK to consider further reducing the reliance upon donor-recipient HLA matching when allocating kidneys, without triggering a reduction in overall rates of graft survival if rates of graft survival continue to improve anyway. Such a policy shift would mean that the preferences of ethnic minority patients are better accommodated by transplant policy, alongside the preferences of other patients. More recently in 2011 [32] a USA concept document has been launched relating to kidney transplantation which advocates prioritizing the young because they have greater capacity to benefit. A problem with the approach is that whilst it might help maximize overall life expectancy from available transplants it discriminates against the old [32]. However, children are normally treated as an absolute priority, and our DCE analysis shows nothing to suggest otherwise. Indeed our findings indicate that this preference extends to young adults, which is not a completely new finding but important to make clear.
A further step in extending the differentiation between patients on the transplant list would be to include social and medical factors as well as age. The issue of whether an allocation policy should treat people differently, either because they are felt to be more 'deserving' or because allocating organs to some people and not others will give longer graft survival overall, is part of the equality/efficiency debate [3, 18]. In this respect our study makes an important contribution. Our pilot study ruled out a preference for social factors such as prioritizing those in employment. Our main DCE findings did show a preference for allocation according to some co-morbidities affecting life expectancy or quality of life. Our research also indicates that a measure of 'social value' (whether recipients have dependents) was valued by UK respondents. So, when transplant policy is re-appraised, consideration might be given to this additional criterion, though it is equally possible that the transplant policy group might not wish to include it for practical or ethical reasons. In either case, the use of DCEs to define and quantify stakeholders' preferences can provide a valid structure for the decision making process.
The findings of this study raise significant issues around transplant allocation to those from ethnic minority groups (who unlike other patients do not favour allocation on the basis of a close tissue match between donor and recipient; and also value prioritizing those with severe rather than moderate disease affecting life expectancy less than other patients do). Our findings also highlight important differences in preferences between healthcare professionals and patients (healthcare professionals prioritize better tissue matches less than patients do, but value prioritizing those with dependents more). Professionals would also prioritize those with no rather than moderate co-morbidities affecting life expectancy (more than patients), and would prioritize severe rather than moderate co-morbidities affecting quality of life more than patients would). Whilst respondents in our study did not think employment status should be a factor in kidney allocation, having dependents was valued. These findings ought to be considered when UK renal transplant policy is next re-appraised. This research also adds to the growing international literature relating to transplant allocation policy.
British Organ Donation Society
CKD:
HLA:
Human Leucocyte Antigen
DCE:
Discrete Choice Experiment
LREC:
Local Research Ethics Committee
MRS:
Marginal Rate of Substitution
OMEP:
Orthogonal Main Effects Plan
QALY:
Quality adjusted life year
UNOS:
United Network for Organ Sharing
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We are very grateful to the Coventry Kidney Fund for providing the funding to enable this research to go ahead. We are also very grateful to Dr Ratcliffe and Dr Street and Dr Burgess, for their input into the design template used for the final DCE design. We would also like to thank the very many people who made this research possible by filling in a questionnaire either as part of the pilot exercise, or in response to our request for participants fill in a questionnaire for the final national survey. We also want to thank BODY, and Kidney Life for their help in relation to distributing questionnaires, and University Hospital, Coventry, and Ealing Hospital for allowing us to undertake the pilot questionnaire interviews.
Warwick Medical School, University of Warwick, CV4 7AL, Coventry, UK
Michael D Clark
Department of Economics, University of Warwick, Coventry, UK
Dennis Leech
Centre for Health and Social Care Research, Faculty of Health and Wellbeing, Sheffield Hallam University Collegiate Campus, 32 Colliegiate Crescent, Room 205, S10 2BP, Sheffield, UK
Anil Gumber
Third Sector Research Centre, University of Birmingham, Park House, 40, Egbaston Road, B15 2RT, Birmingham, UK
Domenico Moro
Warwick Medical School, University of Warwick, Coventry, UK
Ala Szczepura
Nephrology Department, University Hospital, CV2 2DX, Walsgrave, Coventry, UK
Nick West & Robert Higgins
Nick West
Robert Higgins
Correspondence to Michael D Clark.
There are no conflicts of interests between the independence of the authors' contributions and the source of funding. The study was funded by the Coventry Kidney Fund.
Mr MC (Senior Research Fellow – Health Economics), wrote the paper which was amended in the light of co-authors and peer reviewers feedback. He managed the research project, and conducted the final data analysis. Mr Clark designed the pilot DCE questionnaire using the computer package SPEED under the oversight of Dr Julie Ratcliffe (a DCE expert who had undertaken published DCE work relating to Liver transplantation) and conducted most of the pilot DCE research. He then worked on the final design of the DCE with the support of Dr Julie Ratcliffe, and leading statisticians in the field of DCE design (Dr Street , and Dr Burgess – see reference[24]) who provided the final DCE design template. He then undertook the UK national DCE survey, and analyzed the data. Professor DL (Department of Economics, University of Warwick) provided guidance in relation to applying appropriate econometric methods to underpin the Econometric methods deployed in this paper. In particular, Professor Leech suggested the use of the Delta method to establish the statistical significance or otherwise of Marginal Rates of Substitution (MRS), and suggested the use of Wald tests to establish whether there are statistically significant differences in MRS between different stakeholder groups. Dr AG (Honorary Fellow, Warwick Medical School), conducted pilot interviews upon some non-English speaking ethnic minority patients. He arranged for the questionnaires to be translated into other languages for non-English speaking respondents, and then checked translations before distributing questionnaires to non-English speaking survey respondents who requested a questionnaire. In the interests of boosting final responses from ethnic minority groups he also interviewed some non-English speaking respondents at University Hospital, Coventry, and Ealing Hospital who completed a final questionnaire. He also contributed to the final paper. Dr DM (Research Fellow, Third Sector Research Fellow, University of Birmingham), introduced Mr Clark to the use of STATA, and STATA do-files in order that Mr Clark could conduct the econometric analysis contained in this paper. He then contributed to the final draft of the paper. Professor AS (Co-Director of the Centre for Evidence in Health and Diversity [CHEED]), contributed to the writing of the paper and management of the research, and advised on ethnic minority / diversity issues. NW (Transplant co-ordinator, University Hospital, Coventry), oversaw the distribution of final questionnaires to healthcare professionals, and commented upon the draft of the paper prior to publication. Dr RH (Renal Consultant, University Hospital, Coventry) commissioned this research and recognised the potential of deploying discrete choice experiment analysis to analyse data relating to different stakeholder group preferences for renal transplantation. He has also suggested a range of changes to the paper and assisted in making it clinically relevant. All authors read and approved the final manuscript.
12882_2011_454_MOESM1_ESM.doc
Additional file 1: Further details on the pilot exercise. Description: This file provides a more comprehensive description of the pilot exercise undertaken. (DOC 24 KB)
Additional file 2: Copies of DCE questionnaires. Description: This file contains the questionnaires used in the DCE study. Copies of the following are provided: ●Questionnaire Version 4a: Patient version. ●Questionnaire Version 5b: Carer version. ●Questionnaire Version 6a: Healthcare professional version. ●Questionnaire Version 7b: Donor / relative of deceased donor version. (DOC 378 KB)
Additional file 3: Technical details about the econometric models (Model 1 and Model 2) used to establish stakeholder preferences 'Econometric / statistical analysis.' Description: This file provides a more detailed description of the econometric models used to underpin point 6 of the 'Methods' section. (DOC 32 KB)
Deriving Marginal Rate of Substitution (MRS) for attributes with respect to waiting time.
Additional file 4: Description: This file provides information on how marginal rates of substitution (point 7 of the 'Methods' section) were calculated using data obtained from econometric models 1 and 2 (outlined in Additional file 3). (DOC 50 KB)
Clark, M.D., Leech, D., Gumber, A. et al. Who should be prioritized for renal transplantation?: Analysis of key stakeholder preferences using discrete choice experiments. BMC Nephrol 13, 152 (2012). https://doi.org/10.1186/1471-2369-13-152
Received: 21 March 2011
Choice experiment