Risk attributable to drunk drivers

Drunk driving is dangerous, but…

Drunk driving and distracted driving are dangerous, and particularly dangerous for young and inexperienced drivers.

However, the reality of road safety today is that that most of the public risk of driving that we incur is due to driving sober, not drunk.  Driving drunk is more dangerous than driving sober, but since most people are not driving drunk these days, fatalities that do occur are more often caused by sober drivers.

Here is a table from a study done a long time ago on the subject

Source Evans, L. (1990). The fraction of traffic fatalities attributable to alcohol. Accident Analysis & Prevention, 22(6), 587-602.

What this table shows is that drunk driving is responsible for the majority of one vehicle fatalities, probably involving the death of the drunk driver.  However, the majority of multi-vehicle collisions involving a fatality are caused by sober drivers.

Driving drunk is dangerous (this can’t be said often enough), however, even as far back as 1990 (when drunk driving was more common) it did not make up for the majority of fatalities on the road, particularly when multiple vehicles are involved.  The majority of fatalities were caused by sober drivers.

What this means

Advocacy against drunk driving is important and makes our roads safer, but should not distract from the a fundamental reality: driving is dangerous.  Our decisions to drive, and to build cities focused on motorized transport have an impact beyond the decisions of individual drivers, and we are all participants in this system.

We need to remember that the risks of driving are systemic, and part of our collective decision to live in a motor-vehicle centred world.  A drunk driver is legally culpable for the consequences of driving drunk, but we are all at least somewhat morally culpable for this system of transportation that causes death.  If we are uncomfortable with traffic fatalities, we need to rethink our transportation system as a whole.  Perhaps autonomous vehicles can help?  Or a rethink on private motor vehicles altogether?  But what is clear is that we can’t pretend that all the consequences are entirely the result of poor decisions made by a small subset of individuals.  We all have a share in the blame.

No simple answers in health science

One of the critiques of modern medicine is that medical research is not sufficiently focused on developing cures for disease, and instead, puts too much focus on symptom management and treatment.  The reasoning behind this critique, most often applied to the pharmaceutical industry, is that disease cures are wilfully hidden because it is more profitable to manage symptoms long term with expensive drugs than to cure them with a single pill or treatment protocol.  There might be a small hint of truth in these accusations, but they distract from a less conspiratorial and more consequential reality: the golden age of modern health care, medicine, medical technology, and medical research is probably behind us.

During the last epidemiological transition, the world benefited from tremendous successes of medicine and medical research.  Understanding germ theory led to immunization and water treatment, which probably account for the majority of the increase in life expectancy and quality of life in Western Europe and North America in the last century.  Surgical techniques have advanced by leaps and bounds over the last 100 years—with improvements in hygiene probably saving thousands millions of lives alone.  Treatments for cancer, diabetes and other diseases have increased the length and quality of many lives.  The scientific advancement of medical science research also helped eliminate a host of ineffective and harmful ‘cures’ of the pre-science era.

The biggest achievements over this period were gained through the understanding, treatment and prevention of diseases caused by pathogenic micro-organisms–like viruses and bacteria. By the late 20th century, many of the most serious infectious conditions were under either partial or full control.  There remain infectious diseases that still have a significant impact on global public health (e.g., AIDS, malaria and schistosomiasis) but even many of these diseases are preventable or treatable based on the knowledge that the science of germ theory has granted us.  Indeed, their continued burden on global health is mostly a reflection of the vulnerability of populations living in poverty, failed political and social institutions, unemployment and a lack of education, not a lack of medical understanding.

Causal simplicity

What explains our past success over infectious diseases?  It probably boils down to their causal simplicity.  Infectious diseases have at least one necessary cause—a disease causing micro-organism.  When we discovered ways to deal with pathogens—through immunization, the modification of our environments and the modification of our behaviour—we could target the one necessary cause of infectious diseases.  We reduced our exposure to pathogens in the environment by treating water and sewage.  We enhanced our natural defences for fighting off infection through immunization.  We changed our contact with pathogens by altering our behaviour.  Importantly, we targeted these strategies directly at the most immediate and proximate cause of disease–the pathogen–and it’s because the pathogen is a known necessary cause that we were so successful.

On the other hand, the causes of heart disease, cancer, Alzheimer’s and many other major modern causes of disease and mortality are multifaceted—part of a complex epidemiological web.  For these conditions, causality has been harder to pin down to one or even a handful of necessary risk factors.  Many of the main causes of illness and death today are explain by a mixture of genetic, behaviour and environmental factors.  Without a single evident necessary cause, it is very difficult to develop simple and effective cures for most of these diseases.

Diminishing Returns

The evidence of this change can be seen in the life expectancy curves of wealthy countries.  In Canada, we are still making gains in life expectancy, but the returns are diminishing, and may very well be heading towards a natural limit.  Based on the figure below, we can see that people who live to be 90 have about 5 years of life remaining, on average, and it’s been that way for the last 100 years.  It seems that much of medical science involves helping more and more people live a meaningful life to this limit, rather than increasing our life spans, as was accomplished by past medical innovations.



The investment in increasing the quality of life as well as distributing long life to more people is admirable, and worthwhile.  However, we have a cultural memory of finding ‘cures’ of disease that originates from a time when medical researchers had the relative easy task of finding and killing the bugs that made us sick.  Now that the causes of disease are more complex, there are probably not as many more simple cures to be found, but rather, an assortment of partly effective treatments and interventions that hopefully improve lives.  This is not a flaw of medical research, but the reality of our fragile existence on this planet.  Nevertheless, the apparent disconnect between our cultural memory of medical breakthroughs and the long list of uncured diseases today might be the inspiration for both  ‘alternative’ medicine and conspiracies about the pharmaceutical industry hiding cures.

Changing expectations

This problem of diminishing returns is more than just a pessimistic prediction of future health innovation, but has important implications on what we should expect from health care.  For one, we shouldn’t expect as much from health care practitioners or researchers as we do.  What we know now will, very likely, be more or less the foundational knowledge of future health care and medicine.  No new research is very likely to cure all cancer, heart disease or the other plagues of modern life in the way we cured infectious diseases of the past.

Second, while there will still be new research achievements in the future, the benefits will continue to get smaller and smaller over time, particularly with respect to how long we live.  Increasingly, medical dollars should probably be spent on improving the health of the worst-off, rather than pushing up against the ceiling of life expectancy.  We continue to live in a world where large numbers of people suffer and die from treatable diseases and malnutrition.  The massive investment in medical research and care in the wealthiest third of countries has a very small impact on health when compared to the potential impact of investing that same money in the health of people living in the poorest third of the world.

My conclusion

Modern medicine is a victim of its own success; past achievements in medical research have addressed the easy-to-cure diseases, and the remaining diseases are harder to prevent and cure.  The slowing progress of medical research is not attributable to medical capitalism, but are due to the complexity of non-infectious diseases.  We need to accept that the gains of future medical research are likely to be very small, and that our resources may be better spent elsewhere.

There are some areas of medical research that may be fruitful in the coming years.  I suspect there is still considerable room to improve the efficacy of cancer treatment, for example.  However, if the health of humanity is of importance, more needs to be done to invest in the health of people that is going to have an impact, focusing our health care dollars based on the return on investment, rather than often unrealistic expectation to find cures to all that ails us.

Using Ngram to measure trends in spelling mistakes

Ngram is a database of words published in books.  It is a convenient and fascinating resource for all sorts of crazy stuff interesting to linguists, English professors and other word lovers.  You can search the database (or even download it) to see the patterns of word frequency in books published as far back as the 16th century.  Here is a reference to a scholarly article on the subject.

To see an example of what Ngram can do, I provide a trivial example.  When I was a kid, my friends and I often debated the correct colloquialism for underwear: ginch, gotch or gitch.  Using Ngram, I can see the frequency of usage in books published in English, and resolve the debate once and for all.  Gotch wins!

gotch ginch gitch

(Slightly) more seriously, I used Ngram to look at the changes in frequency of misspelled words in published books between 1800 and 2000.  In particular, I was interested to see if spelling improved between 1980 and 2000 — a period which covers the introduction of personal computing and the computerized spell-checkers in word processing software. You can view the data I compiled from Ngram here.

I focus on three common mispellings: occassionaly, recieve and beleive

Using the Ngram data, I calculated the ratio of the fractional use of the misspelled word to the fractional use of the correctly spelled word.  This ratio is an attempt to control for the secular changes in word use.  For example, maybe the word “believe” was more commonly used in books in the past than it is today.

I then graphed out the result:Spelling2017

We can see that up until the 1980s, the publication of these misspelled words is trending upwards, but by about 1980, there is a rapid decline.  Here is a close-up of the last few decades of data:


Over this period of time, misspellings of all three of these words declined in a way consistent with the (utterly unsurprising) hypothesis that computerized spellcheckers improve spelling.  However, it is worth noting (and is somewhat surprising) that the misspelled variants have not entirely disappeared from published books, and nor have they reached the relatively lower spelling error rates seen in the early 19th century.


Interpreting weak effects in large studies: is dementia associated with proximity to roads?

An Ontario study investigating the risk of dementia associated with living near major highways has been getting press attention from around the world recently.  The results report that living near a major highway is responsible for a 7% increase in risk of dementia, a fairly small effect compared to many epidemiological studies.  As an academic with a healthy dose of natural skepticism (bordering on an unhealthy dose of cynicism) I was immediately doubtful of the authors’ research findings, so I read the abstract and skimmed the paper.  I saw some study design weaknesses that when combined with the small effect size, suggest that the results should be interpreted with great caution, and reported with considerable qualification.  I will briefly comment one specific problem I see with the research, though there are several worth consideration.

What’s the problem?

The study design is a retrospective cohort, and very large; for this particular part of the study, there were over 2 million persons involved.  Large study sizes have some important advantages; for example, they mean results are likely to be more generalizable to the population as a whole. They also have more power to detect weak (though ‘statistically significant’) effects.  As I’ve noted other times on my blog, large data are better at detecting all effects–true and false.  For this reason, big data research has to be as rigorous as possible–one small systematic error can be enough to greatly affect the interpretation of the data, particularly when effects are small.

In this study, one important methodological shortcoming should cast some doubt on the observations the researchers make.  Specifically, the authors have not properly controlled for the confounding effect of income.

There is evidence that dementia has an association with income [1,2], and evidence that lower income is associated with living closer to major highways [3].  If the effect of income were not taken into account in this research, it could bias any association between dementia and living near a road–‘confounding’ our interpretation of the effect of interest.  In this particular case, the likely confounding is to produce a positive bias in the effect of interest, making the relationship between living near a busy highway and dementia seem stronger than it actually is.

To control for this, the authors did not use subject income, but rather, used neighbourhood income from the Canadian 2001 census.  Neighbourhood income is an imprecise measure of individual income, and therefore does not fully resolve the confounding problem.  Indeed, what remains is residual confounding, the effect of which is (in this situation) a probable bias in the estimated association between dementia and living near a busy highway.  This is true even if the error in neighbourhood income is random.  How much of a bias is unclear, but given the small size of the detected effect, could easily undermine the main conclusion of the paper.  You can see a simple example of this effect in this Google Sheet I prepared.

The media’s role

In spite of this, the research is probably still publication worthy.  The fundamental science is not unreasonable–which is to say, there is a plausible biological explanation for how exposure to air pollution could result in some effect on human systems–including the brain.  Furthermore, this study is building on other research in this area [4].  However, the modest observed effect combined with the methodological shortcomings (specifically residual confounding of income) require a high degree of qualification on the part of the authors, as well as the media writing about it

Unfortunately, once research like this gets reported in the media (and pumped by the PR staff at the journal and the affiliated universities), qualifications are often lost–especially in newspaper headlines.  As of today (January 6, 2017), here are some headline examples:

ex6 ex5 ex4 ex3 ex2 ex1

Perhaps most readers will be thoughtfully suspicious about the results of this research, or follow up with a critical analysis of the original article, but I doubt it.  It is quite likely that many people will file these headlines into their memories as evidence of something substantive–perhaps that highways are causing dementia, or that academics have a nefarious agenda to attack motor-vehicle culture.  In either case, promoting this particular study as an important contribution to our understanding of the environmental risk factors for dementia is problematic since it lacks the rigour to justify influencing public assessments of risk or our understanding of the world.

My conclusion

The apparently biggest strength of the study (the size of the cohort) is part of the problem, since it is the study size that makes the result seem important. Ceteris paribus, a large study with small biases is more likely to produce small but ‘statistically significant’ false effects than a small study with small biases.  For this reason, I think it is often good practice to interpret effect size and study size together, and that one should be especially suspicious of large studies with small apparent effects.  Large studies with methodological flaws are becoming more common in this era of big data, which means that researchers, policy makers and the public need to be more vigilant than ever, and take great care in their interpretation of findings.