15
Feb 12

An All-Too-Little-Known Fact About Quitting Smoking

Last Thursday was an important day for me, for personal reasons: it was the third anniversary of my quitting smoking.

After about 15 years of heavy smoking — a little over one, but sometimes up to two packs a day — I finally managed to quit in February 2009.

That last attempt at quitting smoking was my sixth or seventh attempt at quitting smoking. The first time I tried to quit smoking, when I was 19, I lasted a few hours. The penultimate time, I did not smoke for about six months.

This leads me to the following little-known fact about quitting smoking:

“Most people who quit smoking for good only do so at their fifth or sixth quitting attempt.”

I had no idea until I walked in my colleague Don Taylor‘s office one day after yet another failed attempt at quitting and said: “Screw it, I don’t think I’ll ever be able to quit smoking.” Don, who is a health policy scholar and who blogs over at the Incidental Economist, responded by quoting the stylized fact above, which is apparently well-known among health policy scholars.

It might be well-known among health policy scholars, but I feel like it deserves to be publicized widely. Every failed attempt at quitting smoking is very disheartening, as it brings a real sense of failure. How many people might have given up on the idea of quitting because of that sense of failure? How many people would have kept going had they been told that most people who quit smoking for good only do so at their fifth or sixth attempt?

If you know anyone who smokes and wants to quit smoking — and what smoker does not want to quit smoking? — the best thing you can do for them is to tell them about the stylized fact above, and not to give up. There is definitely a learning process. And if they want an actual source, they can check out the 1990 report of the surgeon general on smoking cessation.

 


10
Jan 12

Taubes on the Weakness of Observational Studies, and a Methodological Rant

One caveat is observational studies, where you identify a large cohort of people – say 80,000 people like in the Nurse’s Health Study – and you ask them what they eat. You give them diet and food frequency questionnaires that are almost impossible to fill out and you follow them for 20 years. If you look and see who is healthier, you’ll find out that people who were mostly vegetarians tend to live longer and have less cancer and diabetes than people who get most of their fat and protein from animal products. The assumption by the researchers is that this is causal – that the only difference between mostly vegetarians and mostly meat-eaters is how many vegetables and how much meat they eat.

I’ve argued that this assumption is naïve almost beyond belief. In this case, vegetarians or mostly vegetarian people are more health conscious. That’s why they’ve chosen to eat like this. They’re better educated than the mostly meat-eaters, they’re in a higher socioeconomic bracket, they have better doctors, they have better medical advice, they engage in other health conscious activities like walking, they smoke less. There’s a whole slew of things that goes with vegetarianism and leaning towards a vegetarian diet. You can’t use these observational studies to imply cause and effect. To me, it’s one of the most extreme examples of bad science in the nutrition field.

That’s Gary Taubes in a FiveBooks interview over at The Browser. Taubes is better known for his book Good Calories, Bad Calories, in which he argues that a diet rich in carbohydrates is what makes us fat and, eventually, sick, and in which he argues in favor of an alternative diet rich in fats.

I really don’t know what kind of diet is best for weight loss, but I do want to stress Taubes’ point about the weakness of observational studies, even longitudinal ones. It is not uncommon for social science researchers to say “Well, we’ve been following these people over time, so we can use fixed effects to control for unobserved heterogeneity.” That is, they control for what remains constant for each unit of observation over time, which is made possible because they have more than one observation for each unit of observation. I have certainly been guilty of that. Continue reading →


02
Nov 11

Assessing the Impacts of Telemedicine

My Sanford School colleague Manoj Mohanan talks about one of his current research projects, which aims about assessing the impacts of telemedicine in India:

YouTube Preview Image

 


25
Oct 11

Agricultural Policy and Malaria in the United States

From a recent working paper by Barreca et al.:

The Agricultural Adjustment Act (AAA) caused a population shift in the United States in the 1930s. Evaluating the effects of the AAA on the incidence of malaria can therefore offer important lessons regarding the broader consequences of demographic changes. Using a quasi-first difference model and a robust set of controls, we find a negative association between AAA expenditures and malaria death rates at the county level. Further, we find the AAA caused relatively low-income groups to migrate from counties with high-risk malaria ecologies. These results suggest that the AAA-induced migration played an important role in the reduction of malaria.

If you want to know more about the AAA, here is the Wikipedia page for it.


21
Oct 11

Health Insurance: Learning from Emerging Economies

Perhaps surprisingly, the most interesting incentives have been developed in an emerging economy: South Africa. The Discovery group, based in Johannesburg, has crafted a programme called Vitality that applies the “air miles” model to health care. You earn points by exercising, buying healthy food or hitting certain targets. You rise through various levels, from blue to gold, as you accumulate points (rewards are adjusted to your starting level of fitness to give everybody a chance of making progress). And you are given a mixture of short- and long-term rewards ranging from reduced premiums to exotic holidays. Continue reading →