Showing posts with label Health and Medicine. Show all posts
Showing posts with label Health and Medicine. Show all posts

19/06/2015

A Two-Step Model of Class-typical Behaviour

Let's start with the example: In the U.S., high-SES people used to smoke more than low-SES people until about 1965. Then the lines crossed, once, and they never crossed again. These days, there are many high-SES people that you don't have to tell about health risks: to them, smoking is prole. And who wants to be prole?

More generally, there are many behaviours that low-SES people show more frequently than low-SES people, and vice-versa. Why? Let me propose a two-step model. First, there is some initial reason why a certain behaviour is shown more often by low-SES people. Then, the behaviour becomes associated with being low SES. Then, the behaviour is reduced even more by high-SES people. 

Smoking is, I think, a good example. Initially, high-SES people may have had access to better information, or have been better at processing the information, or had more self-control, or have put a higher value on health, or what have you, or all of the above. This created an initial smoking gap. This helped associate smoking with being prole. This, in turn, caused people who don't want to be seen as prole to smoke less.

In some cases, the reason for the initial reason could simply be chance.

The model implies that SES differences in smoking were easier to explain in terms of the psychological factors mentioned above (more self-control, etc.) in 1970 than today. Generalizing this is left as an exercise to the reader.

23/04/2014

Why Is Quitting Smoking So Hard?

Quitting smoking is generally considered very hard. Anecdotally, most attempts seem to fail. Malcolm X is said to have said that quitting Heroin is not easy, but smoking is the real challenge.

I can't speak to the biochemistry of it all, but from a purely anecdotal-behavioral perspective, it is not obvious why quitting smoking should be particularly hard. I can see one big argument for why it should be easy and one for why it should be hard.

1. Should be easy. Beyond the addiction itself, there's little reason to smoke. It is not hard to see why people would consume alcohol or heroin: They're psychoactive, in ways that are often experienced as extremely pleasant. Strictly speaking, nicotine's psychoactive, too, but, really, that's negligible. So, in that sense, smoking is the stupidest addiction there is: You don't even get a high out of it. Why not stop altogether?

2. Should be hard. Being addicted to heroin or alcohol fucks up your life. You probably won't be able to hold down a job, and mess up your personal relationships as well. (Yes, there are high-functioning alcoholics. This is one of those cases in which the existence of the term tells you that it's been invented to describe an exception. Nobody would come up with the term "low-functioning alcoholics" because that's just, you know, regular alcoholics.) In contrast, the near-term consequences of smoking are minor (smell, yellow teeth, shortness of breath), while the biggie (lung cancer) is far into the future, and you, like everyone else, are a time discounter.

So, no major reasons to continue, but not that many reasons to stop either. Should be a wash in those terms, right? And yet quitting smoking is considered unusually hard. Let me suggest that this is a variant of the phenomenon "bad is stonger than good" (low quality pdf). That is, other drugs give you a better reason to continue (good), but they also give you a better reason to quit (bad). Bad gets a higher weight than Good, so if both are stronger, people feel less of a motivation to kick the low-bad, low-good addiction than the one that's high on both. Why go through all the trouble when you're not hurting yourself all that much?

07/02/2014

Around the Blogs, Vol. 106


2. "Nonshared environment" might best be conceived of as noise, not environment, says Kevin Mitchell.

3. External validity alert: Are patients in medical trials selected for large treatment effects? (Andrew Gelman/Paul Alper)

4. Chris Bertram makes a surprisingly good case for the argument "Squeezing the rich is good: even when it raises no money".

5. "Is there no racial bias precisely because it seems like there is?" Ole Rogeberg takes us into the mind of the microeconomist.



8. 50 great book covers from 2013, collected by Dan Wagstaff (via)

9. The low-hanging fruit of immigration: Bryan Caplan offers another metaphor.



12. What's it like to hear voices that aren't there? (Christian Jarrett/L. Holt and A. Tickle)

20/06/2013

Taubes on the Limits of Epidemiology

Here's a very interesting 2007 article by Gary Taubes, on the limits of epidemiology. One interesting bit:
The subjects were some 8,500 middle-aged men with established heart problems. Two-thirds of them were randomly assigned to take one of the five drugs and the other third a placebo. Because one of the drugs, clofibrate, lowered cholesterol levels, the researchers had high hopes that it would ward off heart disease. But when the results were tabulated after five years, clofibrate showed no beneficial effect. The researchers then considered the possibility that clofibrate appeared to fail only because the subjects failed to faithfully take their prescriptions.

As it turned out, those men who said they took more than 80 percent of the pills prescribed fared substantially better than those who didn’t. Only 15 percent of these faithful “adherers” died, compared with almost 25 percent of what the project researchers called “poor adherers.” This might have been taken as reason to believe that clofibrate actually did cut heart-disease deaths almost by half, but then the researchers looked at those men who faithfully took their placebos. And those men, too, seemed to benefit from adhering closely to their prescription: only 15 percent of them died compared with 28 percent who were less conscientious. “So faithfully taking the placebo cuts the death rate by a factor of two,” says David Freedman, a professor of statistics at the University of California, Berkeley. “How can this be? Well, people who take their placebo regularly are just different than the others. The rest is a little speculative. Maybe they take better care of themselves in general. But this compliance effect is quite a big effect.”
And another:
Indeed, if you ask the more skeptical epidemiologists in the field what diet and lifestyle factors have been convincingly established as causes of common chronic diseases based on observational studies without clinical trials, you’ll get a very short list: smoking as a cause of lung cancer and cardiovascular disease, sun exposure for skin cancer, sexual activity to spread the papilloma virus that causes cervical cancer and perhaps alcohol for a few different cancers as well.
Note that it says "without clinical trials", though. He's also got answers to the question, "So how should we respond the next time we’re asked to believe that an association implies a cause and effect" that seem reasonable. Recommended for anyone interested in health and/or research methods and causality (but keep in mind I'm not an expert on medicine).

17/06/2013

Estimating the Effect of Helmet Laws on Cycling-related Injuries: You Can't Do It Like That

In some places there are laws that require people to wear helmets when cycling. One may wonder what effects these regulations have on injuries. That's a question a paper (open access) by Jessica Dennis, Tim Ramsay, Alexis F. Turgeon and Ryan Zarychanski is trying to answer. They use data from the Canadian provinces, some of which introduced helmet legislation for minors only, while in other provinces the laws apply to people of all ages, and yet others introduced no such legislation. Have a look at the basic data:


Red lines are for adults, blue lines for minors. The dotted lines indicate when the legislation was introduced. You'll note that the provinces differ in when they introduced the laws. There are no clear breaks in the trends when the laws are introduced. On the other hand:
The rate of hospital admissions for cycling related head injuries in Canada among young people decreased from 17.0 to 4.9 per 100 000 person years between 1994 and 2008 (fig 1⇓). In provinces that implemented helmet legislation, the rate decreased steeply between 1994 and 2003, the time over which legislation was implemented, from 15.9 to 7.3 per 100 000 person years, corresponding to a 54.0% (95% confidence interval 48.2% to 59.8%) reduction. In provinces and territories that did not implement helmet legislation, the rate of admissions for cycling related head injuries also decreased between 1994 and 2003, but to a lesser degree. The reduction in provinces without legislation was 33.2% (23.3% to 43.0%), corresponding to a decrease from 19.1 to 12.9 per 100 000 person years. Among adults, the rate of admissions for cycling related head injuries was low in all provinces and across all study years. Between 1994 and 2003, the rate of head injuries in adults in provinces with helmet legislation decreased by 26.2% (16.0% to 36.3%), a reduction from 3.0 to 2.2 per 100 000 person years, compared with a negligible increase in rates in provinces and territories with no legislation, from 2.7 to 2.8 per 100 000 person years.
That's the authors' preliminary, narrative analysis. They point out that other cycling-related injuries also decreased. The authors then make some data analysis decisions which I would describe as suboptimal. First, they run an interrupted time series regression for each province separately, adjusting for trends. Second, they do not differentiate between provinces in which the laws apply only to minors and those where they apply to all, on the basis that some other study found spillover effects of legislation aimed at young people on helmet use in adults. Third, they take as their dependent variable hospital admissions for cycling-related head injuries as a ratio of hospital admissions for all cycling-related injuries.

The authors estimate no significant effects and conclude that "the incremental contribution of provincial helmet legislation to reduce the number of hospital admissions for head injuries is uncertain to some extent, but seems to have been minimal."

But you cannot conclude that from their analysis. First, recall that the provinces introduced their laws in different years. Dennis et al. throw that variation away and hence cannot control for time effects. Just pool the data and run a regression controlling for both province and year fixed effects! I guess that's almost all you need for identification, but one might consider controlling for differences in weather, which surely must have some effect on cycling.

Second, why not differentiate between laws applicable to all cyclists and minors only, respectively? Just use two different dummies. If the minors-only laws have effects on adults, that's information you want to explicate.

Third, and most importantly, you really, really do not want to adjust for all cycling-related injuries. The authors state that they do this in order to adjust for changes in cycling. But this makes no sense, and doubly so. (i) You automatically adjust-out any differences that the laws might make by reducing cycling. I believe there are studies suggesting such an effect, but I have not seen them. It would certainly make sense: Forcing people to wear a helmet makes cycling less attractive to some. (ii) There is a large literature on the topic of the consumption of risk (Peltzman effect). The idea is that when safety measures are put into place, people are going to consume some of that risk by adjusting their behaviour. For example, cyclists might cycle faster. So some of the effect of the law should be on cycling-related injuries not to the head.

In other words, this is an ideal design to find no effects even if there are some. I'm not saying that's deliberate - maybe it is more appropriate to say that this reflects disciplinary differences. For a medical researcher, it's probably natural to ask how much a helmet helps once there is an accident, which is roughly what the adjust-for-all-injuries strategy does. But if you measure that, you're not measuring the full effect of the law, which is the authors' stated aim. The concept of consumption of risk is standard knowledge in economics, and also known in other social sciences. And any undergraduate who has taken in, say, Wooldridge's Introductory Econometrics, should be able to suggest the design I outlined above, especially given the yummy data structure. Maybe that's just not obvious if your training was in medicine.

In this case, and as a noneconomist, I'll say it's the (hypothetical) economists who get it right. Oh, and I don't think you should use significance tests with this data.

22/03/2013

Around the Blogs, Vol. 93

1. Why social science research is so hard: The case of guns and violence. Part one, part two. (Maggie Koerth-Baker)

2. Important questions that are easy to not ask dept.: Which type of growth trajectory are you on? (Scott H. Young) And are there sudden jumps? (Ben Casnocha)


4. Self-reports underestimate BMIs in Ireland, too. (Economic Logician/David Madden)

5. Placebo effects in priming. (Christian Jarrett/Ulrich W. Weger & Stephen Loughnan) Will it replicate?

15/03/2013

Rational Choice of the BMI

A 2011 paper by Thomas Klein, called "Durch Dick und Dünn: Zum Einfluss von Partnerschaft und Partnermarkt auf das Körpergewicht", studies the intersection of mating and health-related outcomes, namely the BMI. Here's the English-language abstract to the paper which is both written in German and gated for maximum inaccessibility:
This article analyzes how body weight is associated with the existence of an intimate partner and with the sex ratio in the marriage market. The data rely on a representative sample of the 16–55 years old population in Germany, carried out in 2009 (Partner Market Survey 2009). In this data set, individuals’ mating opportunities for the first time are measured by their integration in a network of friends as well as in foci of activity as conceptualized by Scott Feld. Results confirm a weight increase after an intimate relationship has started (negative protection) and they also confirm a mating disadvantage corresponding to high weight (selection). Further results lead to the discovery that the weight difference between individuals with and without a partner varies according to the sex ratio in the marriage market: higher competition in the marriage market obviously corresponds to relatively lower weight of individuals without partner. Moreover, similar BMI of partners is not a result of adaption between partners over time but solely is a result of assortative mating. Consequently, mating patterns with respect to obesity have no effect on the individuals’ weight.
So, there is a number of results; I'll highlight two. Perhaps the most convincing one gives an answer to a question many people will have wondered about (and that, according to the author, no previous study has addressed): How come partners are similar in BMI? According to Klein's results, this is solely a selection effect; treatment - measured as the coefficient yielded by an interaction between the partner's BMI and the length of the relationship - seems to play practically no role.

Another key finding is that single people (but not others) appear to react to the sex ratio in their social circles: When there are more potential partners and fewer competitiors, they exhibit higher BMIs (controlling for other stuff). It's as though people don't try as hard when there's little competition. I have a few quibbles with these analyses, however. It's unclear exactly how the sex ratio measure was operationalized and it is never explained why it was logged rather than used in its original (linear) form, which would seem the most plausible functional form a priori. Further, Klein asserts, but does not show, that only the sex ratio in a person's social circles counts - I would have liked to see the local sex ratio as an additional independent variable. I also bet the size of the local market has an influence. More generally, none of the regressions presents a particular identification strategy beyond controlling for confounds.

Nonetheless, these are interesting results. There are (at least!) two views on when rational choice explanations will not work so well. One holds that rational choice will work poorly when decisions involve strong emotions. Another is that rational choice cannot contribute much to explaining decisions when the stakes are low, but will be powerful when they are high. The continuing flow of results showing that rational choice has a lot to contribute to the study of mating is evidence in against the former view, and in favour of the latter.

29/12/2012

Operation Blank Slate, 2012 Edition

As usual, my end-of-the-year dump of stuff I meant to use for blogging but didn't. Shorter than usual because I changed machines mid-year and, much in the spirit of this series, didn't export bookmarks.

And coming on Jan 1st: The best blog posts of 2013.

06/10/2012

Around the Blogs, Vol. 86

Long time no Around the Blogs, lots of links collected:


The exploit/explore tradeoff (Seth Roberts)

3. "Disclosing hospital quality works" (Economic Logician/Lapo Filistrucchi and Fatih Cemil Ozbugdayand)



6. Anonymity may decrease the accuracy of questionnaire responses. (Christian Jarrett/Yphtach Lelkes et al.)

7. Just in case you had Malcolm X down as some kind of civil rights hero, Bryan Caplan reminds you what a vile racist he was.





31/07/2012

Poverty and Life Outcomes: A Theory of the Changing Relative Importance of Resources and Personality

Why do the poor experience low-quality outcomes in areas such as health, education, and crime? Bryan Caplan has recently been arguing "that social scientists need to search for factors that cause both poverty and irresponsible behavior. Such as? Low IQ, low conscientiousness, low patience, and plain irrationality" (also here and here; critical comment here). The position he is disputing is what you might call the Trading Places interpretation: low-quality outcomes are the consequence of the social situation called poverty. According to this view, there's a big fat causal arrow running from poverty to outcomes such as those mentioned above. In Caplan's model, there are two big fat causal arrows from a summary variable you could call "personality" (which encompasses low IQ, low conscientiousness, etc.) to both poverty and other outcomes of interest (a textbook "third variable" explanation).

I'm ambivalent about Caplan's position. On the one hand, his actual line of argument is deficient bordering on the ridiculous - among other things, it suggests he's never heard of opportunity costs, which would be kinda surprising for a professor of economics. On the other, I agree with the conclusion that both social scientists and laypeople are much too quick to interpret a correlation between poverty and some problem outcome as a poverty effect, and that much more weight should be put on personality as an alternative explanation. Sociological work is perhaps particularly bad in this respect.


My own model is a bit more nuanced. Let's start by imagining we had found a way to partial out those bits of the poverty-problem correlation that are due to poverty as a consequence (e.g., you become unemployed, and hence poor, due to bad health). It seems to me that 100% of the remaining correlation can be explained by "personality" (Caplan's focus) plus the structural restrictions brought about by poverty (standard sociological focus). It follows logically that the portion of the variance explained by one of those two factors must go up when the proportion of the variance explained by the other goes down, and vice versa.

My model applies to societies that today are relatively affluent and meritocratic, such as the USA, Germany or Japan. Back in the day - let's say 150 years ago - the common explanation for the poverty-problems correlation used to be in terms of "personality", but actually much of the reason for its existence was structural. Then structural explanations became more popular, and with it efforts to tear down the structural impediments to better life outcomes. These efforts were effective to a large degree: opportunities actually became more equal. This is another way of saying that the portion of the correlation explained by structural restrictions went down, which, in turn, means that the effect of personality must have gone up.

If true, this implies a neat "perverse" effect (in the sense in which the word is used in sociology): the more one type of explanation for the poverty-problems correlation is believed, the less true it becomes. 

So far my argument may have brought to mind programmes that allow young people from low-income families access to universities (and hence better-paying jobs), for example. But there's more to it, namely intergenerational transmission and the shadow of meritocracy.

In order to demonstrate the logic of the argument, let's first point to the fact that children tend to resemble their parents (their personalities are positively correlated). Let's also note that societies can vary in the degree to which they are meritocratic. One could say - non-normatively - that some people "belong" at the top (the intelligent, industrious, etc.), while others "belong" at the bottom (the dull, lazy, etc.). "Belong" here means simply that we would expect these people at certain positions in society, and these expectations should be the stronger the more meritocratic a society is. But we cannot predict the degree of "fit" we observe simply by looking at how meritocratic a society is now. We also need to look at the past: If a society has been highly meritocratic for a long time, children will tend to already start out in the place of the social structure where they "belong". This is due to the positive correlation between children's and their parents' personalities.

This view, if true, has a number of implications.

(i) You cannot judge the degree to which a society is currently meritocratic by looking at the social mobility which it exhibits. Holding current meritocracy (and parent-child resemblance) constant, we would expect societies with a long tradition of meritocracy to exhibit less mobility. Hardly anybody who talks about this topic understands this.


(ii) We should expect a lower "fit" between personality and social position in those groups who can't have benefited much from past meritocracy, such as recent immigrant groups.


(iii) If you make a society more meritocratic, you should expect the biggest movements right away. For example, programmes that allow children of low-income parents access to univerity will be most effective shortly after their introduction. After a few generations, there won't be so many many highly talented kids left among the potential beneficiaries.


(iv) Caplan's model is more true today than it would have been 150 years ago, because it became unfashionable.


The model also explains why the left (as in "people who favour lots of social mobility") used to like IQ tests, now don't.


An illustration: the social gradient in health
In the 19th century, when these things were first measured, it became apparent that the poor experienced worse health than the rich. In the middle of the last century, it was expected that this poverty-health correlation would soon disappear in affluent western countries due to improvements such as clean drinking water for everybody and universal healthcare (doesn't apply to all nations). Dirty drinking water-type illnesses did indeed disappear, but the social gradient stuck around. These days the leading causes of morbidity and mortality are too much food, alcohol and nicotine. According to some (standard citation), the continuing significance of socioeconomic status as a predictor of health is due to high socioeconomic status allowing access to a multitude of varied resources, such as money, networks and knowledge; access to such resources will be beneficial no matter what the specific causal channels from status to health are. Others contend that individual behavioural choices are important, and according to Linda Gottfredson's theory, the actual fundamental cause is not socioeconomic status, but general intelligence, which correlates positively with the former. My view is that Gottfredson's focus on intelligence is too narrow; that you must also include factors such as high time discounting and preferences for risk taking and that these personality factors are more important now than they used to be. Note how nicely this is in line with the change in the leading causes of poor health: dirty water then, too much to drink now.


Odds and ends
(i) Very careful readers may have noted that my dynamic general model of meritocracy and intergenerational mobility made an unspoken assumption; namely, that the personality traits that were beneficial for parents will also be beneficial for children. The less true this is, the more intergenerational mobility we should expect, holding all else equal.


(ii) I made a clean distinction between "structural" and "personality" influences. But the former may influence the latter. For example, Daly and Wilson (scroll down to p. 1271) hypothesize that growing up in a neighbourhood where life expectancies are low causes people to discount future consequences of actions more (they also present some evidence, but it's low-quality). If this type of explanation is true, it is not crystal-clear whether to model time discounting simply as a personality variable or a consequence of structural disadvantage. Which solution you choose will depend on the exact question you're asking, but take this as a note of caution to give it some thought.


Where credit's due
With a bit of inspiration from Thilo Sarrazin and Steven Pinker not otherwise acknowledged.

13/02/2012

Can You Fight Cancer?

I've been surprised to find that I've followed the US primaries much more closely than I thought I would, given my boredom with day-to-day politics and the fact that I don't live in, nor am a citizen of, the United States. I guess I like the whole politics-as-a-contact-sport setup, coupled with the fact that we now have a cast of four easily distinguished characters, as though this were a boyband: Slick Type You'd Cast As President in a Hollywood Movie, Kranky Old Man with Krazy Ideas, Flyover Beta Who Tries to Play Tough and Used Carpet Salesman. I've even watched one of the TV showdowns, or whatever they call them. When the host asked the four contenders to make the argument that their wives would make good first ladies, I naturally rolled my eyes, but it turned out that the answers were less uninteresting than I'd anticipated [the following quotes are from memory]. Gingrich's answer has kinda faded in my memory, though I seem to remember he loves his wife. Paul said his wife had written a cookbook, "The Ron Paul Cookbook". Santorum pointed out that he and his wife had brought up seven kids, "so we know children aren't good by nature". Romney said something strange. His wife, he said, is a fighter, as evidenced by the fact that she survived cancer.

Although I find it strange, you hear this kind of thing a lot. I've seen two cases of cancer in my immediate family. Both survived, but there wasn't a lot of fighting involved. They both got diagnosed, had an operation, and it turned out that the cancer hadn't spread. Competent surgery? Yes. Luck? Yes. Fighting? No.

I am aware, of course, that many people go through chemotherapy, and that this isn't exactly a fun experience. But still, suffering does not equal fighting. The closest thing to fighting in this context I can think is visualization. As you probably know, there is an idea that you're supposed to imagine little Pac-Man-type creatures moving through your body, eating up cancer cells. If that works, I'd like to see a solid study on it - I'm envisioning a large randomized controlled trial analyzed using IV techniques. Even if it does work, visualization doesn't strike me as particularly fight-like.

You might say that even if it's nonsense, it is nice to think of people who have been through hard times, often due to no fault of their own, and came through, as strong, and admire them for it. O.k. But there's a flipside to that. It suggests that those who didn't pull through sorta had themselves to blame - they just weren't tough enough. That seems kinda wrong.

03/02/2012

Pebbles, Vol. 36

Your weekend reading:

1. In praise of economic inequality (Paul Graham)

2. Harald Martenstein über das Sammeln von Büchern

3. Killian Fox explains pretty well why 2001 is the best film ever made.

4. "Unfortunately, bypassing the need to articulate the conditions and assumptions on which validity of the construct rests, may lead to bypassing consideration of whether these conditions and assumptions legitimately apply." The Deceibo Effect (Beatrice Golomb)

5. Are false rape accusations widespread? (Wendy McElroy)

6. Carreer advice: How to selectively report procedures and findings for publishability in psychology journals. Short, accessible academic paper (pdf) by Joseph P. Simmons, Leif D. Nelson and Uri Simonsohn.

7. What is it like to have an understanding of very advanced mathematics? (via)

8. Environmental catastrophe forecasts that didn't pan out. (Maxim Lott)

9. Insanity as rational choice given unusual preferences. Academic paper (pdf) by Bryan Caplan; of philosophical interest.

30/12/2011

Operation Blank Slate, 2011 Edition

As usual around this time of year, a list of links I was going to comment on here but didn't (in the order in which I bookmarked them). Lots of interesting stuff, as well as lots of stuff I hardly remember.