Posts filed under Medical news (341)

The Dominion Post says “More Kiwis are winning the battle with cancer, with fewer people dying once they are diagnosed.”  Obviously that’s not literally true: the number of deaths per customer is pretty convincingly fixed at one.  What they seem to mean is that people with cancer are surviving longer. This might be true, but the underlying research, unfortunately, doesn’t really show it.

The Otago researchers studied survival time from diagnosis, and showed that it is increasing for many cancers.  That is, Kiwis are spending more time having cancer than they used to.   Survival from diagnosis involves two points.  Moving the time of death later will increase survival, and is a Good Thing, but moving the time of diagnosis earlier without changing the time of death will also increase survival, and doesn’t do anyone much good.

What’s actually happening will be different for different cancers. For example, in breast cancer we know from randomized trials that both phenomena are occuring.  Mammography makes diagnosis happen earlier for everyone, and this results in postponing death for many women.

For solid evidence of improved survival in cancer, you really need to look at something like mortality rates per 100,000 population per year, where there isn’t a problem of moving the goalposts.

The Herald has a very good story on sleeping pils and increased rates of death.  The story describes the study design and the findings, names the journal, and even gives a link to the paper, which is  in an open-access journal.

The study itself is interesting: they looked at at 10,000 people who took sleeping pills, and about 23,000 who didn’t, from a large US healthcare system (about the 2/3 the size of New Zealand).  After matching as well as they could on other health factors, the researchers still found a much higher rate of death, 3-5 times higher, in people who took sleeping pills.

It’s not easy to think of a sufficiently-strong confounding effect to explain this — you would need a factor that increases people’s chance of taking sleeping pills at least 3-5 times and also increases their rate of death at least that much. One possibility is sleep disturbance itself — it could be that needing sleeping pills is the risk factor, and the pills themselves are relatively innocent.

One factor that, I think, does cast a bit of doubt on the results is that the associations were about the same for all classes of sleeping pills:  the traditional benzodiazepines, the new short-acting drugs such as Ambien, and sedating antihistamines.   It’s entirely plausible that they all have adverse effects, but it’s a bit surprising that the effects would be so similar.

The Herald (and media sources worldwide) is covering a research paper on brain tumours and dental x-rays.  The paper asked roughly 1500 people with meningioma, and the same number of healthy people, about their histories of dental X-rays.  The people with meningiomas were more likely than the controls to report having X-rays at least annually, and the researchers estimated a relative risk of 1.5.

Now, meningioma is pretty rare, so this increase works out to an extra lifetime risk of maybe 5 cases for each 10,000 people.  Also, if you are going to have a brain tumour, meningioma is the one to have — some are not even diagnosed, and most diagnosed ones are treated successfully.  On the other hand, brain tumours are usually something you’d like to avoid, so is the risk real?

There are at least two issues that make the relative risk of 1.5 less plausible

• Self-report of risk factors for cancer is notoriously unreliable
• Since meningiomas can be relatively minor, the time of diagnosis varies, there might be some tendency for the sort of people who have regular dental x-rays to also be the sort of people who get earlier diagnoses, which would show up as a higher rate

The Science Media Centre also has a good summary, with quotes from experts.

It’s interesting to work out whether the risk increase is in the right ballpark given general knowledge about radiation.  A 1991 paper looked at the dose from different sorts of bite-wing dental X-ray setups, and found a range from 2 microSievert to 20 microSievert.   (XKCD shows what a microSievert means).   The same paper quotes an estimated risk of 0.73 health events including cancers per Sievert of dose to a population.   We don’t know what the population size was, but we can get a rough idea from the original paper.  They found 1500 meningiomas in 5 years, so at a rate of 3 per 100,000 people per year, that means about ten million people.  Roughly a third of the controls (and so roughly a third of the population) had at least yearly X-rays, so let’s suppose we are looking at 20 x-rays exposure on average for this third of people.Multiplying all the numbers together gives about 150 extra health events at 20 microSieverts per X-ray, or about 40 at the more-typical modern value of 5 microSieverts per X-ray.  The 1.5 relative risk that the researchers found is larger than this crude extrapolation would predict, but the order of magnitude is right.

So, there may well be a small increase in risk of a rare, mostly treatable brain tumour from having yearly dental x-rays.  It’s uncertain how big the risk is, and there are reasons to expect it might be less than a 1.5-fold increase, but that increase is at least of a plausible order of magnitude.   The radiation exposure from a dental x-ray is quite a bit less than from a trans-Tasman flight, and hugely less than from a CT scan, but it’s not zero.

Stuff is reporting on new drug tests being pushed by NZDDA

Hardy said hair testing was more accurate and effective method of detecting drug use, and it gave a history of drug or alcohol use over the previous 90 days….With urine tests more drugs were undetectable if urinalysis was carried out more than three days after use.

Since the advertised purpose of employee drug testing is to catch people who are impaired on the job, expanding the history from 3 days to 90 days surely makes the test less accurate, not more accurate.  It’s more accurate only if you don’t distinguish between on-the-job and off-the-job drug and alcohol use — like the drunk looking for his keys under the lamppost because he could see better there.

One of the key contributions of statistics to evidence-based medicine has been in forcing medical researchers to measure what they really want to affect, not what is convenient and plausibly related to it.  Drug use in the past 90 days is not the same as on-the-job impairment, and it’s probably a pretty lousy surrogate.

The Assistant Privacy Commissioner is quoted in the article as saying

“Employers should only use it where there is a genuine business need. For example, drug testing has been allowed where there are safety issues with operating machinery.

An interesting approach used by some US companies is drug testing after accidents.  A study from Princeton (PDF) found that this did reduce accidents by about 10%, though some of the reduction may have been due to under-reporting of accidents — an important tradeoff to consider.

There isn’t going to be a quick technological fix, however, and we do need some sort of regulation. As Stanford’s Keith Humphreys puts it

…we use public policy to pick the particular sort of drug problem society will have. For example, different policy environments can make it a human rights problem, an addiction problem, a crime problem, an AIDS problem, a public disorder problem etc., but no policy will produce a true ending of all of society’s problems with drugs. There are some policies that ameliorate multiple aspects of the problem, but in most cases we are faced with hard choices about what sort of problem we will have rather than a problem-free alternative.

This week, John Key announced a package of mental-health funding, including some new treatment initiatives.  For example, Whanau Ora will be piloting a whanau-based approach, initially on 40 Maori and Pacific young people.

It’s a pity that the opportunity wasn’t taken to get reliable evidence of whether the new approaches are beneficial, and by how much.  For example, there must be a lot more than 40 Maori and Pacific youth who could potentially benefit from Whanau Ora’s approach, if it is indeed better.  Rather than picking the 40 test patients by hand from the many potential participants, a lottery system would ensure that the 40 were initially comparable to those receiving the current treatment strategies.  If the youth in whanau-based care did better we would then know for sure that the approach worked, and could compare its cost and effectiveness, and decide how far to expand it.   Without a random allocation, we won’t ever be sure, and it will be a lot easier for future government cuts to remove expensive but genuinely useful programs, and leave ones that are cheaper but don’t actually work.

In some cases it’s hard to argue for randomisation, because it seems better at least to try to treat everyone.  But if we can’t treat everyone and have to ration a new treatment approach in some way, a fair and random selection is no worse than other rationing approaches and has the enormous benefit of telling us whether the treatment works.

Admittedly, statisticians are just as bad as everyone else on this issue.   As Andrew Gelman points out in the American Statistical Association’s magazine “Chance”, when we have good ideas about teaching we typically just start using them on an ad hoc selection of courses. We have, over fifty years, convinced the medical community that it is possible, and therefore important, to know whether things really work.  It would be nice if the idea spread a bit further.

The Herald has a story today about weight-loss surgery and Type II diabetes.  They say that two studies reported improvements in blood sugar control in people having the surgery, with complete remission of diabetes in some.    The story tells you where the research was published, and even has a comment from an independent researcher.  The only thing it misses is that it doesn’t come out and say that the studies were randomized: patients were randomly allocated to surgical or medical treatment, so the results are highly credible.

The two papers and a commentary are (currently) free-access at the New England Journal of Medicine, so you can even read the evidence for yourself.

A story in Stuff about student drinking at Otago illustrates an important problem with surveys.  According to the story, the students drank on average two nights a week, and consumed 7.2 drinks per night of drinking.  That gives an average of about two drinks per day, which is about the same as average for the whole country based on total alcohol sales.

I don’t know about you, but I find this a little hard to believe. The newspapers should find it even harder to believe, since it contradicts their usual line about Otago students.

My guess is that Otago students do drink a bit more than the average Kiwi, but that asking people to report their drinking leads to underreporting compared to looking at total sales.  There could be several reasons.  One is that people might not want to admit how much they drink, another is that there may be difficulties in converting actual glass sizes into standard doses, and a third is that people’s “average week” is usually different from their actual week.  Perhaps you `usually’ drink less, but this week was Sam’s birthday and you drank more.  And last week was the keg party. And before that was the start of semester. And so on.

That’s not the point of the story, though.  The point is that new research finds heavy drinking can make you feel unwell and have difficulty concentrating even the next day;  the effects seem to hang over and affect your daily activities. Perhaps someone could come up with a catchy name for this phenomenon…

A Campbell Live trailer just asked “Why is the disease that’s at epidemic levels about to be targeted by government cuts?”

In what is unfortunately an urban legend, Willie Sutton was supposedly asked “Why do you rob banks?”, and replied “Because that’s where the money is”.

There is no point in the NZ medical system spending too much time and effort on assessing cost-effectiveness in idiopathic pulmonary fibrosis, Fabry syndrome, or Hashimoto’s encephalitis.  Reducing the cost of diabetes treatment, on the other hand,  can free up a lot of money to spend on other illnesses.  There’s nothing surprising or shocking about targeting diabetes for cost savings, especially in a low-cost public health system like ours.

The real question is whether the savings in money come at too high a price in patient care, not why diabetes is one of the targets.

This week’s installment in the stream of stories that have found The Answer To Obesity says the problem isn’t just diet or exercise, it’s plastic bottles.   The Herald says

Man-made chemicals present in homes, schools, offices, cars and food are probably contributing to the sharp rise in obesity and diabetes in Western societies, according to a review of scientific literature published yesterday.

Until now lifestyle factors such as lack of exercise and poor diet were believed to be the primary causes of the increased incidence of both conditions, whose proliferation has strained global health budgets.

If you separate out diabetes, it’s not true that obesity has strained global health budgets.  The incidence of heart attacks, for example, continues to go down all over the world.  The rate of decline has slowed a bit, but increases are still theoretical and aren’t straining anything.  However, if we stipulate that obesity is bad, are bisphenol A, PCBS, and phthalates responsible?

All 240 studies they reviewed – whether in test-tubes, on animals or on humans – had been peer-reviewed and published in scientific journals.

We don’t get a lot of opportunities to say nice things about statistics in the press, but the Herald has a good example today in a report on young Maori health.  They report both the numbers of hospital admissions for Maori and non-Maori, and the relative risk.   It wouldn’t hurt if they had linked to a definition of bronchiectasis, though.