Shorter stays in the ED thanks to COVID-19

Early last year the expected influx of patients with COVID-19 to emergency departments (ED) in New Zealand required rapid preparation.  Many questions needed answering quickly – such as, where will we put all the patients? How will we separate highly likely COVID-19 patients from less likely COVID-19 patients?  How will we allocate staff and keep them safe?

One of the two most common presentations to the ED are people who think they may be having a heart attack – chest pain patients.  I got the call to ask if there was a way we could speed up assessment of these patients and identify who could safely go home earlier.  This was a challenge, as Christchurch was already one of the leading hospitals globally for accelerated diagnostic pathways for possible heart attacks.

My job was to go back to our research data to try and extend and improve the identification of low-risk patients.  But I was only one cog in the wheel led by ED specialist Dr Martin Than.   He along with the Cardiologists were also exploring ways to minimise “double-handling” of patients by more than one speciality.  Finding ways to both reduce the time a patient spends in the ED and to reduce the number of nurses and physicians who come into contact with an individual patient would reduce the risk of cross infection, and could free staff time for other patients.  

The accelerated diagnostic pathway (ADP) for chest pain comprises three components, an electrocardiogram (measurement of the electrical activity of the heart), a risk assessment score (called EDACS) based on symptoms, demographics and medical history, and measurements of a blood biomarker called troponin.  Troponin is elevated in the blood when there is damage to the heart muscle.  Previously the diagnostic pathway (EDACS-ADP) comprised one or two troponin measurements.  If the first measurement was very very low (no heart muscle damage) AND the electrocardiogram was normal AND the risk score was low the patient may be reassured they are not having a heart attack and go home (unless the physician thinks otherwise of course).  For the others there needs to be a second troponin measurement two hours later before a decision can be made to send home or admit to hospital.

What we were able to do in March last year was to identify a second group of patients whom physicians could confidentially call low-risk and send home after a single troponin measurement in the ED (ultimately it was decided that they were to have a second troponin the next day at a community laboratory).  

It is one thing to analyse data and identify possible clinical improvement, it is quite another to actually make the change.  What was extraordinary in this case is that after we identified the possibility of a change the clinical, laboratory, managerial and administrative staff were able to implement the change in a matter of a few weeks so that on the 6th of May 2020 the new pathway went live with immediate effect (Figure 1).  Far fewer patients needed to have a second blood test in the ED, and so on average these chest pain patients were spending 30 minutes less each in the ED.  Additionally, there was a reduction in patients admitted to Cardiology who were ultimately diagnosed, not with a heart attack, but merely “Unspecified chest pain.”  In our publication of our experience in implementing this “COVID pathway” we identified a very large group of CDHB and University of Otago staff who were involved or whose earlier work made this transition possible.  For this work we were honoured to be recognised as both the Asia Pacific and one of three Top Global award winner by UNIVANTS of healthcare excellence (https://www.modernhealthcare.com/univants-healthcare-excellence, https://www.univantshce.com/int/en/2020-winners ).

Figure 1: Fewer patients needed two troponin measurements two hours apart in the ED after the implementation of the new ADP

While we were fortunate that the influx of Covid patients did not come, such was the efficiency gains and the ability to reassure more patients early that they are not having a heart attack, the new pathway has remained in place.  While this is a tale of a silver lining to the COVID-19 pandemic it is also a tale of the tip of the iceberg. This tale was possible because of 13 years of previous work.  It began when Dr Than decided there needed to be something done about the 93% hospital admission rate for chest pain patients despite only 10-15% having a heart attack.  Several rounds of research in collaboration with University of Otago’s Christchurch Heart Institute including two randomised controlled trials and development of the risk score called EDACS all meant that when a very rapid change was needed, we had the data, and we had the people in place who trusted each other to work together to make that change happen.  The extraordinary result is that now, despite a large increase in population, the numbers of patients being admitted with the ultimate diagnosis of “unspecified chest pain” is now what it was in the 1990s (Figure 2).

Figure 2: Since the implementation of the first ADP Christchurch hospital has managed to reduce the numbers of those hospitalised for chest pain but without ultimately being diagnosed with a heart attack back to levels not seen since the 1990s.

A divine visitor last Friday

On Friday night, 5 March 2021, the “God of Chaos” sped past our planet. The asteroid Apophis, or “God of Chaos” as it is known, made a close approach.  Bigger than the Sky Tower (about 370m diameter) and faster than a speeding bullet (4 km/s or about 14,000 km/h) it would surely be a spectacular sight if it hadn’t been so far away (16,000,000 km).  Fortunately, to keep you all safe, I was watching with a new kind of amateur telescope, the Unistellar eVscope (enhanced Vision scope).*  The thin streak in the attached photo shows the movement of Apophis relative to the background stars. 

Apophis on a trip to down-town Auckland

Apophis will come around again in April 2029, but much much closer.  Skimming the Earth under 3 Earth diameters away (~36,000 km), possibly disrupting satellites, and being visible to the naked eye. The exact trajectory of the asteroid is not known, though the “hitting the earth” scenario has been all but ruled out (chance is less than 1 in 40,000).  Yet, with all measurement there is some uncertainty.  The gravitational pull of the sun, planets, other asteroids and even the solar wind can all alter the orbital mechanics.  So, with a massive object bearing down on earth we want to limit uncertainty as possible!  Being able to observe these events from earth is vital to our safety and well-being, as well as a source of excitement and wonder.  The sort of wonder that has encouraged thousands to pursue science for its benefit and for its beauty.  

Apophis at 9:45 pm on Friday 5 March as seen from Christchurch, New Zealand. If your socks haven’t been blown off, you have failed the test to see if you still have the capacity of wonder and amazement. This is a 350m diameter ONLY rock some 16 MILLION kilometres away moving at 14,000 km/h! For the astronomer its magnitude was 15.9 (8.6 million times fainter than the brightest star) & it subtended an angle of 0.2 millionths of a degree.

Apophis’s shadow from a distant star passed over parts of the US on 22nd February and again on 7 March (from a different star).  These are called asteroid occultation events.  eVscope users were there to observe the event.  These citizen scientists are part of Unistellar’s Planetary Defence team (I’ll join as soon as a scheduled occultation occurs near Christchurch).  Their measurements of the very very subtle dimming of the star as the asteroid passes in front of it are automatically uploaded to SETI (https://www.seti.org/unistellar?page=1) where the professionals work on the data to help define the orbit of Apophis and its shape more precisely.  

Sadly, our views of the heavens are being shut out – shut out by the light pollution caused by excess lights, badly shielded lights, and high temperature LED light bulbs.  In Christchurch the city’s new street lighting has resulted in a huge increase in light pollution. Where once Matariki was easy to find, now it is possible only to the experienced amateur astronomer or the country dweller.  This need not be with only a little effort – all outside lighting must be limited, dim, well shielded pointing down to where the light only needs to be, and if it is LED light bulbs be 3000K or less (because the 4000K+ bluer light is more highly scattered as well as being damaging to insects and birds).  We can all participate in keeping our skies dark by turning off lights and insisting on dark sky friendly lighting.  For those more interested in the issues,  Blue Light Aotearoa from the Royal Society of New Zealand (https://www.royalsociety.org.nz/major-issues-and-projects/blue-light-aotearoa/), and the The International Dark Sky association (https://www.darksky.org) are places to start. 

Ps.  Some have ascribed the earthquake and tsunami to divine intervention because of bad press received earlier in the week.  “The God of Chaos” has messaged me to say he takes no responsibility.

*I have no financial interest in Unistellar beyond having backed the eVscope on Kickstarter.

End of life – it isn’t so easy

In a few weeks New Zealanders will make a choice whether we implement into law the End of Life Choice Act 2019.  My scientific expertise includes developing and validating methods to predict future events of ill people including death.  There is one section of the Act that concerns me deeply.

Section 5(1)c of the End of Life Choice Act 2019 states that one of the criteria of eligibility for assisted dying is that a person “suffers from a terminal illness that is likely to end the person’s life within 6 months”. 

Concern 1: How likely is likely?

What does “likely” mean?  Does it mean a 51% chance of dying or a 99% chance?  The Act does not define it.  This means that the decision as to what “likely” means is left to the individual physicians’ involved in the decision whether to grant the person’s request for assisted dying.  It is inevitable, therefore, that should this Bill be enacted that there would be inconsistency in application of this clause.  Some physician’s would be more liberal in their interpretation than others.  Physicians are human and subject to the subtle pressures and biases that affect decision making.  The tone of the voice or the story told may affect whether they rate someone as “likely” or not.  The physician’s own prior experience plus their familiarity with the literature around a particular disease will be a large part of the equation.  These too will vary considerably between physicians. Unsurprisingly, the literature suggests the accuracy of physician estimates of when a terminally ill patient will die are poor and varied (reference).

Concern 2: How accurate are physicians?

My day job is to help physicians make better decisions by providing them with objective assessments of risk of current or future events (eg risk of a heart attack or risk of dying).  Developing and validating these risk prediction models is difficult.  In the context of the End of Life Choice Act the statistic that is most relevant is called calibration.  A prediction model is said to be well calibrated when it gives a prediction of an event of say, 60%, to 100 people 60 of them go on to have that event.  Similarly if the prediction is 5%, 5 out of 100 with that prediction will have the event. Etc. The figure shows a calibration curve I produced of a model that performed very well.  Many models, though, may have very good calibration at low predictions, but poor at high ones. Others may be only averagely good across the range.  What it means at a particular part of the range when a model is inaccurate is that it systematically over or under-estimates the risk.  It is rare for a model to be accurate across the whole range of risks.

Example calibration figure where the Observed rate of a myocardial infarction (heart rate) is compared to the prediction made by the MI3 algorithm. This is an example of a particularly good calibration. It is published here.

The other statistic that is relevant is called discrimination.  A good prediction model discriminates between those who go on to have the event and those who do not have it by allocating to those who went on to have the event a high probability and to those who didn’t go on to have the event a low probability.  In the ideal model these probabilities would be 100% and 0% and the model would never predict a risk of 100% to people who didn’t have the event or 0% to those that did.   Of course, these ideals are never reached in medicine.  Humans are just too complex and we cannot accurately measure every variable that matters.  Psychological variables are particularly difficult to quantify, and these are incredibly important to a terminally ill person’s wellbeing and prognosis.  

My point is simple – event the best scientific techniques applied to estimate risk of death are not perfect and often far from perfect.  Should this bill be enacted, how much more inaccurate will the physicians’ estimates of risk be?  How many people who would have survived more than 6 months die prematurely?  I can’t predict this. For this reason I believe the End of Life Choice Act is an experiment.  If it had been submitted as a study protocol for review for a research grant or to a health ethics committee at the very very least a measure of the accuracy of the physician risk would need to be part of the protocol.  There would also need to have been stopping rules that if the physicians were proved to overestimate risk (as they do for a number of diseases) then the experiment would be halted.  Without these safeguards, if for no other reason, I believe the pre-cautionary principal should apply and the experiment that is the End of Life Choice Act 2019 should not go ahead.

A reflection on Rising from the Rubble

Nine years ago in my office above the main entrance to Christchurch hospital I was hiding under my desk pushing back at furniture on wheels (whoever thought that was a good idea in New Zealand!) and generally avoiding flying objects. This weekend we remember that February day in which an earthquake cost many their lives and many more their health, their homes and their livelihoods. All who live in Christchurch know that the events of that day and the days afterwards still are with us today. For many of us our homes are still broken. Sadly, many have had their mental health suffer too. When I extracted myself from the office and got down stairs, I watched as the first of the injured arrived in cars and on the back of the utes. As the staff of the health system geared up, I, merely a scientist, wandered off home avoiding the liquefaction and inspecting broken buildings on the way. Recently, I have had the pleasure of reading a book written by a scientist and an emergency physician that has opened my eyes more to the incredible system and people who work in it that we call the Canterbury’s health system.

Rising from the Rubble: A health system’s extraordinary response to the Canterbury earthquakes, has been written by Professor Michael Ardagh, an emergency physician, and Dr Joanne Deely, a scientist. It is published and available through Canterbury University Press.

Rising from the Rubble is indeed an extraordinary story.  It is written in a very engaging manner – purposely with anecdotes and quotes from interviews, but not without athe science and facts and figures to back them up.  The authors’ aimed for the book “to be not only a public record of the Canterbury health system’s response, but a celebration of it.”  They have certainly met that aim.

The book covers a very broad range of response, both in the immediate aftermath of the quakes, but also the ongoing dealing with the myriad issues the quakes threw up for the health system and those involved.  

You will read of courage and heroes (have your tissues ready): 

“We were scared … We were running out and we were hit by a wave of people running in to the building [Princess Margaret Hospital]! They were all staff.  I couldn’t believe it”

You will read of competence and professionalism: 

“speaking calmly and quietly she directed resources to where they were needed”

Dr David Tolley, President of the Royal College of Surgeons, Edinburgh who was in the ED on 22/2 speaking of Dr Jan Bone

You will read of what can be done when it has to be done:

“So we just gave him [a builder son of a GP] the work credit card and he got reticulated water going to about 12 or 13 of the practices in the east and south areas of Christchurch”

You will read of leadership

“He [Dr Nigel Miller, Chief Medical Officer] said, ‘ What are you going to do?’ And I said, “Well we already know what we are going to do.  We are going to get everybody [needing dialysis] out”

Dr David McGregor, Clinical Director, Nephrology Department

You will read of collaboration

“The friendships, connections and collaborations that were forged during the period of integration between Canterbury Health Laboratories and MedLab staff will remain.”

Kirsten Beynon, GM, Canterbury Health Laboratories

You will read of aroha:

“I asked the Maori community if we could include the Asian and migrant communities because they would be outside, to which I got an immediate agreement”

Sir Mark Solomon, Te Rununga o Ngai Tahu

Of course, there is much more.  We were truly blessed by a health system that was so well interconnected and replete with individuals prepared to do what it takes.  While, the consequences of the earthquake remain this book is a reminder of what was and what can be done by people with the right motives and skill.  Dr David Meates, CDHB Chief Executive speaks not of recovery, but of transformation.  This book will be a record of a time of rapid transformation as well as a tribute to all those involved. Do read it.

Cheesecake Files: Machine learning heart attacks

“Machine learning” rates very high on the buzz-word scale, right up there with “nano-technology” and “blockchain”. Like most buzz it is more noise than substance. However, every now and again it looks like there might be something in the noise that bites. This episode of the Cheesecake Files1 is about testing an algorithm (another buzz word) developed through a machine learning technique for the early detection of heart attacks (strictly – myocardial infarction).

The buzz

Before I begin my story in earnest a couple of words about the buzz words. When I say “algorithm” think “recipe”. In the context of emergency medicine this is simply a series of steps which assist the medical team in their decision making. For example – if the presenting complaint is “chest pain” the triage nurse will connect up a device (ECG) to measure the electrical activity of the heart and will draw some blood and send it off the the lab with specific instructions to measure the concentration of a molecule called troponin. Several years ago we introduced in all New Zealand emergency departments more detailed pathways (ie an algorithm) which included guidance on which other data to obtain from the patient, when to repeat blood measurements and how all the data goes together to risk stratify the patient. The principal aim being to ensure that as quickly as possible, and as safely as possible, physicians could rule out the presence of a heart attack. This is important because patients presenting with possible heart attacks are one of the most common presentations to the ED and so if they remain in the ED a long time this can affect the whole service. However, only about 10-15% (in NZ) are actually having a heart attack. Many of those who aren’t can now be reassured early that they are not. Please note – if you’ve sudden onset chest pain then the ED is the right place for you. Just because most who attend are not having a heart attack doesn’t mean that you might not be.

The other buzz word is “machine learning.” This term is usually used to mean a computational technique which involves giving a computer some data and some basic instructions how to look at it. Then asking the computer to make a prediction of an outcome (in our case, whether a patient is having a heart attack or not). The prediction is compared to the actual outcomes and information on how well the computer performs is feedback into the machine to tweak some of the algorithm. Think of this as tasting the soup and then adding a few more spices. The process is repeated many times until the soup is as good as it can possibly be. Some recipes we know and can follow ourselves. Some happen behind closed doors as a team of chefs puts together a meal. A characteristic of machine learning algorithms is that they are often not easily understood (a “black-box”), but the proof of the pudding is in the eating. This leads me to the story that is the current cheesecake.

The story

Nearly three years ago we were asked to test if an algorithm called MI3 works to risk stratify people who appear in the emergency department with symptoms suggestive of a heart attack. The algorithm had been developed by a US based diagnostic company called Abbott Diagnostics. We were given access to the black box and could input variables from real patients and observe the predicted outcome. In this case the algorithm was producing a number that very closely corresponded to the probability of a patient having a heart attack. There were very few variables required to make this prediction – sex, age, two measures of troponin and the time between the two measures. The latter is important because how troponin concentrations change over time informs us about the possible heart attack.

A collaboration of research groups from Scotland, Switzerland, Germany, United States of America, Australia and New Zealand came together to provide sufficient data to test MI3. This group was lead by Christchurch ED physician Dr Martin Than, and Scottish cardiologist, Prof Nicholas Mills. I was charged with pulling together all the data and conducting the statistical analysis of the performance of MI3.

There were about 8000 patients in our testing data set with 10.6% of them having a heart attack. Importantly, the first thing I noted is that the values output by the algorithm corresponded to the true rate of heart attacks. ie when the MI3 value was 5 about 5% of those with this value were having a heart attack, when it was 90 about 90% of people were having a heart attack. In other words, the algorithm was well calibrated – this can give physicians confidence. The second thing was to see if we could find MI3 values below which we could say that almost everyone is not having a heart attack (it’s impossible to be 100% certain – we aim for about 99% or better). We were able to find such a value and show that it identified an impressive 69% of people as low-risk. The full results are available in the cardiology journal Circulation – here.

The application

So, how may this be used? The difference with this algorithm compared with others is three-fold (i) it does not require blood samples to be taken an specific set intervals, (ii) it does not require information about patient history or detailed signs and symptoms to be gathered and incorporated, (iii) and the output is a probability rather than simply stratifying patients to a low, intermediate or high risk category. In other words, the inputs are simple and objective, and the output is easily interpretable. In practice, the physician may receive the MI3 value from the labs along with the troponin results. This may aid discussions with the patient through the use of icon arrays or similar (see the figure).

A concept of how a tool displaying the result of the algorithm may be used to display risk to physician and patient.

1 Once upon a time, a long long time ago, I received a cheesecake for every publication. Sadly, those days are gone now. But I live in hope.

Disclaimer: I have acted as a consultant statistician for Abbott Diagnostics. I have no shares or intellectual property associated with MI3. Abbott was not involved in the testing of the algorithm.

Dark space

Dark space, like green space, is essential for our well being. Dark space, like green space, is our past, our taonga, and our right. Dark space, unlike green space, is not prioritised in our city plans, is not part of our conversation about Te Tiriti, nor is it where we go for relaxation and inspiration. But once it was. Dark space is accessible to us all, if only. If only we turned off the lights and looked up. Dark space is the night sky, the moon, the planets, the stars and galaxies. Once visible to every child, now lost in the haze of light pollution; once the source of wonder and joy; once the inspiration and the starting point for the personal journeys of countless scientists and philosophers, religious leaders and poets.

The week is Matariki. The new year celebrated at the first appearance of the stars known as Matariki – the pleiades. A time for friends and family and celebration. Sadly, even tragically and certainly scandalously most tamariki in Aotearoa will struggle to see any of the stars of Matariki, let alone the nine that are supposedly naked eye objects. This is because we have not acted as kaitiaki of our night sky. We have not guarded it from our modern obsession of pretending night is just an extension of day rather than a time to rest, recuperate, and, yes, gaze on the heavens in awe-filled wonder.

Matariki (Source: http://deography.com/m45-the-pleiades-seven-sisters/)

All is not lost, though. The solutions are in our own hands. As we have preciously defended the green spaces in our city, now we must do the same for dark space. We must insist that our city ordinances are such that lighting that spills light into the dark space is not permitted and that the kinds of lights we employ are not of the most polluting and disrupting kinds. As individuals we can turn down or off our outside lights that blaze away even when we are not there. We must be guardians of the night – protecting not just the taonga that is the view, but the darkness that enables our night loving insects and birds to survive. Matariki is also a time to reflect on our health and wellbeing. Perhaps this year we may reflect on the fact that the blue light emitted by our phones and tablets which we have been warned is detrimental to our health, is the same kind of light emitted by the LED street lights, which cities are rushing to pollute dark space with.

Let us together, all who call Aotearoa New Zealand home, reclaim our kaitiakitanga of Matariki.

(Featured Image by cafuego https://www.flickr.com/photos/cafuego/32719827268)

Performance Based Research Fund: The numbers are up

8269 academics and their bosses have been alerted – the data is in, the numbers are crunched, PBRF scores are out. Who are the winners, who are the losers? Find out more with tec publication. But before you go there…

I predict that any minute now tertiary institutions throughout the land will be posting press releases detailing their successes, each one trying to say “we’re the best” – at least in some category, somehow, if you squeeze the numbers and look at them sideways… well, you get the picture. Speaking of which – here’s one I posted after the last lot of PBRF results were released in 2013.

http://www.scoop.co.nz/education 12 April 2013

Last year I stated that the PBRF quality evaluation was a net zero sum game, because irrespective of the outcomes of the quality evaluations of the 8269 staff there is no net increase in the overall funding (that being set by the Government of the day in the Budget). The total quality evaluation dollar amounts are $173,250,000 p.a. The 2018 PBRF process has shifted where this pie is divided up marginally.

The percentage changes are marginal, but they do translate to a loss or gain to individual institutions. In this round, based on these preliminary results, the “big winners” appear to be AUT, VUW and the non-university sector. The biggest loser appears to be UC, but the others seem to have lost a substantial amount. Of course, as I’ve argued before, these $ amounts must be considered in terms of the total $ costs to the institutions and the government to administer the PBRF, likely millions for each institution. The gains of VUW and AUT aren’t really as much as they look and the losses to the other institutions more.

Was this Quality Evaluation PBRF process worthwhile? – I think not. Will the results be celebrated? – I expect so. Will this post do more to expose the emperor’s new clothes and change the system? – one lives in hope.

Falling into a black hole

To honour Roy Kerr and the first picture of a Black Hole, I have used the wonderful “rayshader” package in R to render the photo into 3D. It’s a bit like an extraordinarily hot volcano. Don’t fall in.

Thanks NASA for the photo (https://www.nasa.gov/mission_pages/chandra/news/black-hole-image-makes-history).

Is my science ethical?

Am I doing what the public thinks is OK to do?  That was the question that came to mind as I heard Bioethics and Health Law expert Rochelle Style speak at a Health data workshop held at the University of Otago Christchurch this week.  Rochelle spoke with clarity and demonstrated a great deal of expertise.  I like the simplicity of Law – tells us what we can do, Ethics – tells us what we should do, and Social license – tells us what the publish think is OK to do.  In this post I want to focus on Social license because when I asked a question about it, it became evident that this was the least understood or investigated of the three legs to ethical decision.

First, an example.  It is legal for me to search twitter for posts about prostate cancer.  If I was to approach the NZ Health and Disabilities Ethics Communities saying that I was doing an observational study of publicly available social media comments on prostate cancer I may not even need to go for ethical review.  However, would it be OK – according to the NZ public – for me to do this?  What if in my report I anonymously quoted some of those twitter posts?  What if I quoted some of those twitter posts and identified the person who made the post?

What is your reaction?  Which is OK?

Now consider this, the print and online media use identified twitter quotes all the time.  They effectively take what is public in one domain to an audience that chooses to follow someone and make it public to another audience who has not made that choice. In addition, consider that twitter uses can block certain people from seeing their tweets.  This suggests to me that the “social license” employed by the media is quite broad. Is it OK?

What if when you visited your doctor you were told by the receptionist that the consultation may be recorded for training purposes.  There was no option to “opt out”.  Would this be OK?  Now, phone your bank or broadband provider.  Did you get an option to “opt out”?  They now have your voice recorded – a biometric that can be used to identify you – as well as details of the conversation.  How long will they keep it for?  Who can listen to it? How is the data stored? How can you ask for it to be deleted?

Hopefully, by now, you see that the concept of social license – what is OK and what isn’t – is not easy to tie down.  For health science, trying to understand what the public think is OK and what they think is not OK is very important, though not well articulated.  

Two organisations that are actively engaged in the discussion about the use of social license for health (and other) data are Te Mana Raraunga , the Māori Data Sovereignty Network and Data Futures Partnership.  Their websites are worth visiting.  Te Mana Raraunga talk of data as “taonga” (a “treasure” for a non-New Zealander reader). This really appeals to me and I think is a good place to start, whether as individuals or communities.  

If our personal data is taonga, what are our rights and responsibilities over this taonga?  How we answer this will depend on our own cultural, social, religious, and other backgrounds.  For me, my data is taonga and I have a responsibility to use this treasure for the greater good – the greater good of those around me, and for generations to come.  This is one reason I volunteer for health studies from time to time.

Regarding data as taonga belonging to individual patients helps me in my role as a scientist. It is not just about making sure I don’t inadvertently reveal something publicly about an individual, but that I utilise ethically and well the data that I have been given temporary guardianship over. For me, this means that I must be vigilant over the statistical techniques I use and cognisant of the most appropriate ways to interpret that data.  Unfortunately, this is a much-neglected area of scientific ethics.  The scientific literature, even in the most prestigious of journals, is full of error of interpretation and of statistical methodology. I regularly see even simple errors in journal articles I referee.  I know I have committed some errors of these myself.  What I believe is most important is that I take responsibility for improving the techniques I use and I improve how I interpret and communicate the results. This is not just about “better science” but about treating the data as taonga, about regarding myself as guardian, not owner, of the data, and recognising that I operate not merely under law and ethical guidelines, but under a social license.  That license may be fluid, ill-defined, and somehow based on diverse views, but nevertheless it exists and needs to be understood and honoured.

The physics of maiming a child (repost because of “those” scooters)

Dear Driver,

When you backed out of a driveway and did not even see how I swerved around behind your car to avoid T-boning you, how dare you have the temerity to tell me you were careful!  I was 7 feet tall, dressed in bright yellow and traveling at no more than 10 km/h.  Perhaps a simple lesson in physics will help you and your fellow “driveway backers” to realise how dangerous you are and to adopt safer driving practices.

In the diagram you can see a car backing out of a driveway.  Typically when you are at the edge of your property and have a fence (see photo below) blocking your view of the footpath you are able to see about 1.7 metres along the footpath.  Let us imagine that there is a child on a trike riding at 5 km/h just out of your line of sight.  How long  does it take them to travel that 1.67 metres?  The physics is quite easy.

5 km/h is 5000 metres in 60 x 60 seconds, ie about 1.4 m/s.  Putting this in the formula above means that it takes about 1.2 seconds for the child to travel that 1.67 metres. 

Now consider this. According to design guidelines for safe bicycle use 2.5 seconds must be allowed for someone to observe the danger, react, apply brakes and stop.  In other words, if you covered the distance from your driveway to the middle of the footpath, about 1 metre, in under 1.2 seconds you will almost certainly hit the child.  That is a speed of just 3 km/h!!!!!

Now consider who else is on the footpath, all legally:

  • Pedestrians 5 km/h
  • Joggers 5- 15 km/h
  • Kids on skateboards or scooters 10 km/h
  • Child on bicycle with small wheels, 10 km/h
  • Mobility scooter, 5-10 km/h
  • Me on my Trikke, 10 km/h
  • Postie on a bike 5-10 km/h.

For those going 10 km/h your speed needs to be just over 1.5 km/h to hit someone! That’s the legal people … but the Lime scooters at 20 km/h mean it all the more necessary to slow down.

So, before you do some damage here is what you can do:

  • Never back out of a driveway unless you really really must.  If you think you must because of the design of your driveway, change the design!
  • Cut back those hedges, remove some of that fence so that you can see further. [ City councils… please make a by-law to make this happen].
  • Always always always stop at the end of your driveway (BEFORE THE FOOTPATH) and toot a horn.  Then proceed very very slowly.

By the way, you are legally obliged to give way:

(1)
A driver entering or exiting a driveway must give way to a road user on a footpath, cycle path, or shared path (as described by clause 11.1A(1)).

Thank you for considering the physics of maiming a child, may you never find your self in such a terrible situation.

Regards,

Dr John Pickering

A typical driveway with almost non-existant visibility
 A typical driveway with almost non-existent visibility

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Feature Image: Intangible Arts https://www.flickr.com/photos/intangible/ under Creative Commons Attribution 2.0 licence.