Posts from February 2012 (42)

There were two stories on avoidable medical errors last night when I was watching the news.  The obvious one was on statistics from the District Health Boards on errors made in hospitals. These were up last year, though we don’t know whether it was better reporting or more errors.  On this topic, the best person to read is US surgeon Atul Gawande, especially his book The Checklist Manifesto.  As he points out, doctors do the wrong thing a lot more than they did a century ago, because there are so many more things they have to do right — medicine is getting more and more complicated.   It’s also helpful context to remember that avoidable errors in driving cause about 300 deaths and 6000 serious injuries per year in New Zealand.

The other story was on avoidable public health errors: the rate of infectious disease in New Zealand and its relationship to poverty and ethnicity. There are a lot of different diseases involved, but the most dramatic example is rheumatic fever.   Rheumatic fever is a nasty and almost completely preventable complication of  streptococcal throat infections, and in industrialized nations with modern healthcare systems it just shouldn’t be happening.   In the US, rheumatic fever is sufficiently obscure that it showed up in an episode of House, and even then it was just due to the patient not taking prescribed antibiotics properly.   There are major NZ initiatives to reduce rheumatic fever,  but this is the sort of medical statistic that should be in the news, because public awareness and public pressure on government is helpful in getting the problem fixed (rather than just getting it hidden, as is the risk with hospital errors).

What do you think are the biggest media/public misunderstandings around statistics? We know that some statistical concepts can be quite hard to understand (and a bit of a challenge to teach); we’d like to compile a list of the top stats misunderstandings so we can accurately focus some media education projects we are planning ….

Some examples that have already been raised:

• Misunderstanding correlation and causality: All too often causality will be assigned where a study has merely shown a link between two variables.
• Abuse/misuse of the term “potentially fatal”: While many activities/diseases could possibly result in death, the odds should be considered in the context of a developed country with reasonable health-care.
• How to know when something is statistically significant and when not.
•  How to know when you are looking at  “junk” statistics …

Please share your ideas below …

There’s still time to add your nominations for the Stat of the Summer competition, and here’s the most recent three nominations:

Child abuse statistics

Simon Moyes nominated a statistic from the Oamaru Mail on child abuse statistics.

Mondayising holidays

Simon also nominated the $400 million figure quoted in the NZ Herald.

Displaced / Causation

Sakshi Kalani nominated the NZ Herald for its article on workforce statistics:

Old people have displaced more than 40,000 teenagers from jobs in the past five years as more choose to stay on in the workforce and employers shun youth for experience.

Please add your nominations too!

This morning’s Radio New Zealand show Media Watch (MP3) covers Thomas’ post on “Tip of the icecube” about the “hundreds of unfit teachers”.

The teachers story starts at 29:47 into the podcast.

Stuff is reporting on a new paper in the BMJ (or as Stuff calls it, only 24 years after the name change, the British Medical Journal) saying that smoking dope doubles your chance of  a serious or fatal car accident.  Regular readers of StatsChat posts on drug journalism will be pleasantly surprised to hear that this is actually what the research paper says, and that the researchers had moderately good evidence for this conclusion.

The paper compiled the results of previous studies, restricting their attention to studies that had a control group, and that measured  actual THC concentration in the blood, rather than the inactive metabolites that can be detected days later in blood or urine.   Some of the studies compared drivers in crashes to drivers not in crashes, others compared the driver responsible for the crash to other drivers involved in the crash.

The summary of the findings looking at various ways of breaking up the data is:

That’s a forest plot, with the dots showing the estimated risk ratio [technically odds ratio, but it doesn’t matter in this context] and the lines showing the 95% margin of uncertainty.   Though some of the lines include the null value 1.0 and others don’t, there’s pretty clear agreement across the analyses that cannabis increases crash risk, by a factor of two or so.

After getting it right so far, Stuff unfortunately went further and said “The research found cannabis significantly impaired the psychomotor response, or muscle activity linked to mental processes.”  That isn’t actually what they found. That’s in the Introduction section of the paper, because it’s telling us what was already known — dope makes you slower and clumsier.  The BMJ isn’t publishing research telling us that, any more than they publish conclusions on the spatial distribution of ursine excretory activities, or the religious affiliation of the Pope.

They finish up by saying “The Land Transport Amendment Act 2009 will be reviewed this year and police are investigating the possibility of introducing more effective roadside drug tests.” This paper doesn’t really lend much support to changes in current roadside drug tests, which have the advantage of testing for actual impairment and not being specific to a single drug.

The NZ Herald is reporting that more people died in NZ last year than in any previous year. Since

• the population is expanding
• the average age of the population is increasing

we’d expect record numbers of deaths to be the rule rather than the exception.  And that’s what Statistics New Zealand shows us:

There has been a lot of fuss recently in the West Island around a paper by Dr Alan Barclay on trends in sugar consumption and obesity.  When read as a reaction to a rather extreme US paper in the prominent journal Nature claiming ‘sugar is as toxic as alcohol‘ it is a reasonable piece of analysis, but it is being overinterpreted.

The New Zealand Science Media Centre (who are typically sensible and well-informed*) and their Australian counterparts have commentary on the ‘evil sugar’ paper from three experts, who agree that specific effects of sugar beyond its calorie content are not that strong, and, of course, that excess sugar consumption doesn’t directly hurt anyone but you: it doesn’t make you get into fights or crash cars.  On the other hand, there is the calorie content.

(more…)

Researchers asked 30 University of Pennsylvania undergraduate to listen to a randomly-assigned piece of music, and then to record their birth dates.

According to their birth dates, people were nearly a year-and-a-half younger
after listening to “When I’m Sixty-Four” (adjusted M = 20.1
years) rather than to “Kalimba” (adjusted M = 21.5 years),
F(1, 17) = 4.92, p = .040.

This is a randomized experiment, not just an observational study, so we can conclude that listening to the Beatles actually changes your date of birth.

The point of the paper was to show that various sorts of sloppy design and modestly dodgy reporting of statistical analyses, especially in small data sets, can lead to finding pretty much anything you want.  You can then issue a press release about it and end up in the newspapers.

Some fields of science already know about this problem and have at least attempted to introduce safeguards. Medical researchers and statisticians have put together reporting guidelines that the better medical journals insist on following. The  CONSORT guidelines for randomized trials are pretty widely accepted.  More recently, STROBE addresses observational studies, and  PRISMA (formerly QUOROM) covers systematic reviews.

This table is from a US Census Bureau report  Who’s Minding the Kids? Child Care Arrangements:Spring 2005/Summer 2006.  The report in general is boring and informative, but Table 1 stands out: why are nearly four times as many pre-schoolers cared for by fathers vs mothers!?

It turns out that there is an explanation, but it shows how interpretation of statistical output depends on exactly what you ask.  The Survey of Income and Program Participation (SIPP) samples a ‘designated parent’ for each child, and if both mother and father are available they always choose the mother.  The child-care arrangement data are for child care by someone other than the designated parent, so ‘care by the mother’ means ‘care by the mother when the child lives with the father’.  As you would expect, some fathers don’t like this, and neither do some mothers, and it does seem a bit twentieth-century.

In partial defense of the Census Bureau, they have good reasons for being very reluctant to change their questions, because they are often interested in trends over time.  And Table 2 of the report does break down care arrangments by who the child lives with, and by employment information, and other factors.

Nominations for the Stat of the Summer are still open, and here’s the most recent three nominations:

What does relatively few mean?

Cam Slater nominated a quote from the NZ Herald:

“Auckland has become a city of extremes, containing more than half of the country’s total students in both the richest (decile 10) and the poorest (decile 1) schools, with relatively few in between.”

His comment was:

“Really…with with more than half in decile one and and decile 10 that means nearly half are in between not “relatively few”.”

How many?

Stephanie Mills nominated a quote from the Dominion Post:

Hundreds of teachers have criminal convictions and many are not fit to teach

Her comment included:

Given the Teachers Council says there are 100,000 teachers, and (say) 25 teachers were de-registered in each year, this can hardly lead to the conclusion that “many” are unfit to teach.

Thomas Lumley picked up on this and blogged yesterday on the topic, giving some context to the statistics.

Confusion

Sammie Jia nominated a quote from the NZ Herald:

Departing students owe more than those who stay

His comment included:

I am confused by the title and the content here. The article showed some comparisons but i dont understand the author’s purpose.Graduates with more loan tend to go overseas to earn more to repay or just avoid to repay. The title makes me think as long as graduates leave NZ they will owe more.

Go add your nominations, or if you would like to discuss these, add your thoughts below.