Higher speed limits

Today, I received my copy of The Nation's Health, a newspaper put out by the American Public Health Association.  In one section of the "paper", articles from the American Journal of Public Health are highlighted.  A recent article focused on the changes in speed limits that date back to 1995.  In that year, the federal regulations that had kept urban speed limits at 55 MPH and rural  speed limits at 65 MPH were lifted and states were allowed to set their own rates.  There are some states with rural area speed limits that are now 70 or 75 MPH.

Why is this of interest and why was it studied?  The article in The Nation's Health suggests that 12,500 deaths and 36,600 injuries were associated with the higher speed limits.  The deaths alone have been assigned a dollar value of $12 billion.  That translates into just under $1 million per death.

This brings us to the economic questions of the day.

• What is a life worth? 
• What does accepting $12 billion in lost life for faster speeds mean about our society.  

The value of life is often estimated at more than $1 million.  So, it is interesting that the researchers used such a conservative figure.  This is based in part on lifetime earnings and in part on estimates of how society has made tradeoffs between money and life in the past.  Interestingly, while the estimates are often over $1 million, the range of estimates is wide.

As for the second question, to the degree that we understand the $12 billion loss and agree that that is a reasonable valuation of lives lost, it means that we accept this in order to gain something from getting  where we need to go more quickly.  The study did not present an estimate of the value of getting places more quickly.  It is not clear that most people understand what $12 billion means or think that their own behavior will affect this.  The assumption is that society feels that the value of faster transportation outweighs the $12 billion loss (as well as the higher gasoline consumption associated with higher speeds).  It is quite possible that this is a rational choice at a societal level--even though for each family with a member who dies as a result of faster speeds it is an awful experience.

The researchers or the original article stated that "policy decisions that appear harmless can have long-term repercussions" and recommended reducing speed limits again.  I am not convinced that this policy was expected to be harmless in the first place or that society as a whole is not willing to accept the tradeoffs.  We cannot reduce the risk to zero.  We must decide what risk at what cost in return for what convenience is appropriate.

More on H1N1

The economics of H1N1 and influenza in general have been things that I have commented on multiple times.  Today on the Marc Steiner show, I heard a physician/vaccination researcher being interviewed who made a really good comment.  The H1N1 infection does seem to be any worse for complications than the general flu--in other words of every 10000 people who are infected the same number will have cases that are bad enough to land them in the hospital and/or die.  The key is that more people are getting the flu (the guest described the "attack rate"  as four times higher) because no one under age 50 has been exposed to anything like this version of the flu virus before.

How does this relate to economics?  Well, consider someone who had not gotten an influenza vaccination in past years.  If this person recognizes that hospital beds are likely to be more crowded with H1N1 cases, the person might be more likely to get a seasonal influenza vaccination.  In addition, an otherwise healthy young to early middle age adult might reach the conclusion that the risk of H1N1 is sufficiently high to warrant getting a vaccination even though he or she had not in the past.  Again, this is the overall risk of complications and death which is made up of the two parts and the one part (getting influenza) has changed considerably.

In summary, a lot more people will probably get flu shots this year.  Even me.  Is it because of public health officials' exhortations?  Perhaps in part.  But it is also a simple economic reaction to a change in risks.    

Kids' reactions to medications

In today's Washington Post, there is an article about kids reactions to medications.  The article's headline is "bad drug reactions".  I have chosen to use the term medications to emphasize that we are talking about legal prescriptions rather than "drugs" in the sense of illegal drugs. 

The article describes a professional manuscript published in the journal Pediatrics that indicates:

• "It's [the results are] based on national statistics on patients' visits to clinics and emergency rooms between 1995 and 2005. The number of children treated for bad drug reactions each year was mostly stable during that time, averaging 585,922"

What does this mean? It may mean that physicians and pharmacists don't communicate well with parents. It may mean that parents don't implement well drug administration, regardless of what has been communicated to them. It is not meant to say that the pharmaceutical companies purposefully put dangerous products on the market. It is worth thinking about the economics.


Why is there not better communication? It may be a matter of time and what the physicians and pharmacists are paid for (and ARE NOT paid for).

Why do parents not administer better? It may be a matter of rushed time, also an economic issue.

As 5% of the children needed hospitalizations, this represents over 25,000 hospitalizations per year that result from giving things to try to keep our kids healthy. Given that this is 25,000 out of millions of children, the chance that this will happen to any one child is small. However, it is worth thinking about how much could be spent on better research, better incentives for communication, and better incentives for parents to understand the communication and monitor their children closely when they are given medications. Even at only $4000 per admission for a two day admission, avoiding just the hospitalized cases could save $100 million. This is a drop in the bucket in terms of overall health care spending, but may be well worth the investment to help our children avoid the side effects of trying to keep them healthy.

Incentives for better food

Today's New York Times contains an article about incentives for full service supermarkets in neighborhoods characterized by little fresh produce, high rates of obesity, and accompanying high rates of chronic disease.  The solution--new zoning laws and tax incentives.  Will it work?  Maybe.

Why don't consumers eat healthy foods?  Price is one reason.  Time required to get the healthy foods is another.  If the supermarkets that locate in the targeted neighborhoods offer healthy foods (including fresh produce) at a reasonable price this will take care of the two issue mentioned--price and access.  However, I think there are still other things.

Healthier foods often take longer to prepare.  Does the proposed solution solve that problem?  No.

Healthier foods may not be preferred for taste.  Does the proposed solution solve that problem?  No.

Are there ways to solve those problems.  Of course, but that takes more investment by the stores.  They need to spend money to market healthy foods.

So the question of whether this policy will improve health in New York City (and other locales that may try it) comes down to a two part question:

(1) Are the incentives strong enough with existing demand in the neighborhoods  such that the businesses can be profitable if they just set themselves up there with a little help on incentives?

(2) If the answer to #1 is no, is it profitable to locate in new neighborhoods and market healthy foods?  In other words, can the demand for the foods that full service markets carry that corner stores don't be shifted enough with inexpensive marketing efforts to make the stores profitable.

I don't know.  It will be interesting to see how well this works.  Perhaps the stores can work with local community leaders so that the local communities "invest in themselves" and use some of their own effort to encourage community members to purchase and use healthier foods.  In short, I'm a little skeptical of the "build it and they will come" approach to providing only supply side incentives.

Grants from the NIH

It is interesting that as I prepare for the end of a meeting at NIH, the Johns Hopkins Bloomberg School of Public Health newsfeed yesterday highlighted an article on how the NIH has been awarding grants  The key message from this article is that 19 percent of grants awarded were 'not in rank order' assigned by the scientific review committee and that half of the exceptions to the ordering that would be suggested by the scientific review committee were made for "new investigators".

What does this mean?  The NIH has finite funds for research.  These have to be allocated in some way.  Scientists write proposals for research.  Review committees of experts in the field review proposals and assign scores. The scores are not like in school where if you get a 90 or above you have an A.  The scores are all relative.  With grants, it is still the case that a 99 is better than a 98, but the key is how the studies "line up" rather than having an "absolute cutoff" for funding.  So, a 90 or higher would not guarantee funding.  Some years you'd need a 92--other years an 88.  It depends on the budget the NIH has, the budget for each grant, and the number of grants submitted.

The public policy question is whether all grants should be awarded based on their rank order, assuming that rank order implies how meritorious the proposals are, or whether there should be room for exceptions.  Then, if there is room for exceptions, should there be rules for making those exceptions?

There are few rules for the exceptions at the moment, other than the notion that the grants should still represent good science.  The question is whether the scores that are given really tell us anything about the science.  Having served on a scientific review committee, I can say that the scores are only partly about the science.  They also reflect the quality of the research team and that is inherently subjective.  A new investigator could "work the system" by making sure that he or she knows likely reviewers and knows those within NIH to be considered more favorably.  In some ways that is just human nature on the part of the reviewers and decision makers within NIH.  The question is whether human nature or something more objective should guide resource allocation.  Even if we'd desire something "more objective" it is not clear how we'd arrange that.

Questioning assumptions

The hotel at which I am staying for a 1 1/2 day NIH meeting provided either USA Today or the Wall Street Journal as a complementary paper. I chose the WSJ. Probably no surprise there. So, today I had the opportunity to see not only the news feed from the Johns Hopkins Bloomberg School of Public Health, but all the articles in the paper. One caught my attention (as I am a "winter baby"): "New Light on the Plight of Winter Babies" by Justin Lahart.

What is the issue here? Past studies have consistently found that, on average, babies born in January have lower educational attainment and earnings than babies born in other months. This puzzled economists for a while. Almost two decades ago, a pair of economists put forward the hypothesis that because of when it is legal for kids to drop out of school, being born early in the year made it possible to drop out at an earlier grade. As a result, the average January baby would earn less, although many January babies do not drop out. Other economists have suggested other reasons, but the best known is the early drop out presumption.

The reason that this blog entry is titled "questioning assumptions" is that the assumption throughout is that the babies born in different months are otherwise similar. In other words, birth month is relatively random and all mothers are just as likely to have a baby in any month. A recent working paper has suggested that the mothers of babies born in different months are quite different. Each year, mothers of children born in January were less likely to be married, more likely to be teenagers, and had a lower average level of education. In contrast, mothers of babies born in May had the opposite characteristics. What this suggests is that the babies (or at least the families of the babies who may have a large role in determining educational attainment) are not all the same. As a result, we can't say whether the effect of month is actually a time effect or is an effect of family background.

I imagine that economists will debate for some time how important this alternative explanation is, but the fact is that a basic assumption driving the old analysis is now questioned.  Economists will also have to explain why there are differences in mothers associated with the month of their child's birth.

How could this be applied to analyses in health care? There are two examples I thin of immediately:

(1) Not all babies who are breastfed have mothers who are the same. Thus, any findings about the effects of breastfeeding are shaped by the differences in the babies' families.

(2) Not all infants are expected to be as healthy by their mothers when they are fetuses. The expected health of the baby may affect the mother's choice of when to initiate prenatal care and how much to get.

The key is that when we are not using a randomized study design, we have to carefully consider all our assumptions. Sometimes we have no way of assessing the validity of our assumptions. However, when we are given an opportunity to do so, we should take it and reassess conclusions as necessary. That way we can make more informed (and hopefully better) policy.

Unintended Consequences

I think I have mentioned before that one of my graduate school mentors (Richard Hirth who is now a full professor at the University of Michigan) sometimes  referred to health economics as the study of unintended consequences.  On so many occasions in the history of health economics in the United States, a policy was made without fully anticipating how stakeholders in the health care system would respond to the change.  After a policy changed, people changed behavior in ways that were not expected and more research was needed to understand what went wrong.

On Friday of last week, I commented on the idea of taxing generous health insurance plans.  I want to spend a little more time on that topic.  There was a New York Times article published yesterday on the issues around the idea of taxing generous health insurance plans.  Since this information is out there, if the bill passes as is (unlikely) we will not be able to say we were unaware of the consequences.  They may even be fully intended.  The key is that they are not unknown or unanticipated and that is usually part of how we would define unintended consequences.

Since employer-provided health insurance plans receive favorable tax treatment, one way of thinking of the plan to tax generous insurance policies is that this would simply represent realigning the tax incentives so that the are less favorable and there will be less distortion of the system than there is at this time.

While that is a legitimate economic argument, people are used to having the favorable tax treatment.  What the article points out is that while the plan is labeled as "targeting the most generous plans" it may capture any number of those who consider themselves middle class and who are getting only "good" plans rather than really excellent plans.  There are three reasons:

(1) A "one price fits all" policy across the nation is often a problem.  Some people face higher prices for medical care and live in areas where medical care is practiced more aggressively.  Both of these will result in higher insurance premiums.  An interesting distributional question is whether everyone  should face identical thresholds or whether they should be adjusted for local circumstances.  We do some of each in public policy.

(2) The possibility that the threshold for premiums for policies that will be taxed may not keep up with inflation and, over time, more people will end up paying the tax.  This is similar to the alternative minimum tax that was originally intended to affect only those with high (often non-wage) income.  However, because of a lack of adjusting the threshold for this tax over time, this tax now affects more people.

(3) From the description in the New York Times piece, it appears that anyone with a cafeteria style flexible spending account may have the entire value of their flexible spending account  rather than only their health insurance included in the calculation of whether the premium is high enough for the individual to be taxed.  Robert Hansen was quoted on the second screen of the article.  I had not seen that anywhere else.

In short, while my economic logic may favor making people who spend the most face the true cost of their choices, this type of policy must carefully consider not who is the intended target but who actually ends up paying more as a result of policy change.  This policy would affect a lot more people than I'd considered when commenting on Friday.  That does not change the theory or the rational response.  Just the distributional implications.  And that is a political question.