Non-Sedentary Economics

This week a study appeared on sedentary behavior and life expectancy.  The conclusions are that if sitting could be limited to less than 3 hours per day and watching television could be limited to less than 2 hours per day the life expectancy would increase.  The results were based on a meta-analysis, i.e, a study combining results from other studies, of five studies where people were asked about their behaviors like sitting and watching TV and then followed for some amount of time with mortality as an outcome.

A few thoughts on this issue.  First, the studies were not all US studies.  Is there reason to suspect that the effects of a sedentary lifestyle may not be the same everywhere?  Perhaps.  It may depend on the entire range of activities an individual undertakes.

Second, it is not clear whether the studies tested for any interaction terms.  The results have been interpreted as meaning that even people who exercise regularly are at risk if they also sit a lot or watch TV a lot.  The effects seem to be independent.  But what about a person who exercises regularly (perhaps even pretty hard) and also sits a lot.  Take myself.  Does running 25-45 miles per week (there is a lof of variation in my schedule depending on the temperatures, travel, vacation, work intensity, etc.) while sitting easily 8 hours a day at work, make a difference.  Perhaps the running has an independent effect (I sure hope it does). But do the two interact in more complex ways that are difficult to capture?  And how does sitting and working on a computer compare with watching TV? Or is watching different because one's body is more completely relaxed?  And what about those who multi-task rather than just "vegging out" in front of the TV?

Regardless of the study's limitations, (and the authors are up front about a number of others and no study is perfect), what if we take the results at face value?  First, is there a government role?  Is there a market failure for activity?  Is there a market failure with respect to TV watching?  Or are these just choices that people make that they should be allowed to make?  And what if people with jobs that are largely desk jobs want to change their behaviors?  what options do they have?  Is there any way to facilitate these individuals being productive while not sitting at work?  Is there suddenly going to be more of a market for the standing work desks?  And, if so, whose responsibility will it be to buy them?  The employer?  The employee?  Is there a place for government intervention here?


In the end, for me personally, there are a lot of tradeoffs and a lot of possible behaviors.  No easy answers. I'll just take my chances that my combination of activities, job responsibilities, food choices, and sleep quantities is right for me.  I suppose that is all most economists suggest--that people understand their choices, understand the implications of their choices, and are left as free as possible to make them.   

Income Inequality and Health

Some researchers hypothesize that there is a relationship between income inequality and health. The hypothesis suggests that in countries with less income inequality health outcomes will be better. This can provide an argument in favor of income redistribution from upper income individuals to lower income individuals. One interpretation would be higher taxes on higher income individuals with some type of extra support/benefits for lower income individuals.

Many of the studies that have supported this hypothesis have focused on cross-sectional data from multiple countries. One concern that other researchers raise is that when using this type of data, the countries with a great degree of income inequality also have more generous social support programs. In this case, it may not be the lower income inequality that actually results in better health outcomes. Instead, the argument may be in favor of more generous social programs--however we may find the resources for such programs Understanding what is only a correlation and contrasting that with causation is key.

One way to re-assess this question is to use panel data. A recent study by Dr. Mauricio Avendano at the London School of Economics tests this hypothesis. He used data over a number of years from the Organization on Economic Cooperation and Development (OECD). The data are advantageous for addressing the research question of interest to Dr. Avendano as he can look at how changes in income inequality over time within a country leads to any changes. He focused on infant mortality data. He did not find a strong relationship between income inequality and infant mortality.

So, does this completely rule out the possibility that programs leading to less income inequality will benefit health? No. But it does suggest that we should look harder for alternative hypotheses to explain what has been observed in cross sectional data and use this information to motivate policy. Policy that is based on correlations rather than causation is not likely to be efficient policy.

As with medicine, our focus in developing policies with respect to the economy and public health should be evidence based. This is simply one more study that suggests that the evidence to support specific programs that explicitly are aimed at reducing income inequality is not there. To clarify--it may be that the only way to find resources for more generous support programs would be to impose higher taxes on higher income individuals. That could result in less inequality in income that can be consumed. The key to the interpretation of Dr. Avendaon's finding is that while we could argue that reducing the income of high income individuals by a relatively small amount should not hurt them much while giving more resources to those with lower incomes should benefit them a lot may be true, there is nothing specifically about reducing income inequality itself that leads to better outcomes.

"My Disease Cost More Than Yours": It Really Depends on What is Counted

In the world of those who advocate for individuals who have a particular disease or condition, one thing that is often discussed is how much a disease costs.  In my casual observation as a scientist working with advocates, sometimes it seems like we get into a discusion of "My disease is bigger than your disease!"  Or, more appropriately, "My disease costs more than your disease and therefore deserves more attention."  Not quite like a schoolyard brawl. But definitely a sense of trying to get some attention based on the magnitude of the impact.

How do we measure the magnitude of the impact?  We often talk about direct costs (or how much we spend on medical care) and indirect costs (or measures having to do with productivity).  Sometimes, it is hard enough to measure the direct costs.  Who pays what?  How do we know how much they pay? IIs that is paid what it really costs?  How does the system (in the US or elsewhere) help to make it clear whether there is much of a relationship between what is paid and what it costs?

But that is the easy side.  Measuring lost productivity is even more complicated.  A key question that has been debated in the health economics community is whether to measure the value of the individual's time and the concept of potential productivity (often referred to as the "human capital approach") or whether to measure just the productivity lost by the firm (referred to as the "friction cost approach").

The conceptual model has been discussed in the literature extensively, but there is limited literature comparing estimates using the two approaches for the same disease with the same population.  A recent study by Paul Hanly and colleagues compared the estimates of the productivity cost of breast and prostate cancer using the two approaches.

It is at this point that we see that the numbers that are used by advocates greatly depend on what is being counted.  When counting all productivity costs over a lifetime, breast cancer has a far higher impact per person in Ireland than prostate cancer.  The breast cancer cases are younger, are likely earning more, and live longer with the impact of cancer.  However, with the friction cost approach, the two conditions are responsible for nearly identical productivity losses with prostate cancer having a slightly higher value.

In both cases, the wage is used rather than total compensation.  Total compensation includes things like employer sponsored health insurance premiums, payments to retirement accounts by the employer, etc.  Perhaps these are not issues in Ireland in the same way that they are in the United States, but they do need to be considered.

The next time you see that a given disease is costing a given country some enormous number of billions of dollars per year, be careful to stop and think "what is being counted," what should be counted, and how should we count it?  I find that I'm not ever sure of the answer to the last question.  And while the answer to the first one should be clear from reading a well-written scientific article, it may not be clear from reading a popular press interpretation.  Finally, the answer to the middle question may well depend on the policy context.  Let the user of results beware.  

Response Rates of Emergency Medical Services and Mortality

A recently published article looks at the association between reduced emergency medical system (EMS) response time and the mortality outcomes of patients.  You may be asking, "Well, why does it take a study to show that?"  It would seem logical and intuitive that faster response times are associated with better outcomes.  Many municipalities and others responsible for local EMS units have spent quite a bit of time and money trying to minimize response times.  If they did not lead to better outcomes why would we be doing such a thing?

In fact, there are many things in medical care where what is intuitive is what is done and there is not a strong evidence base to support the action.  There are many in the system who are trying to change this and move us to a more "evidence-based" medicine approach, but it takes a while.

How did this study address the question in a novel manner?  Sometimes, randomized trials are appropriate.  In this case it would be completely unethical to make it take longer to respond to some people at random.  The approach used is describe in the study's abstract which can be found here.  The author, Dr. Elizabeth Wilde, points out that some studies focusing on cardiac events have shown the expected relationship but that there was little evidence outside cardiac events and what evidence there was outside cardiac events suggested no relationship.  Why might there be no relationship when the data are analyzed?  That requires us to think about incentives and to think about who knows what.  If the caller indicates a dire emergency the dispatcher can (and has an incentive to) communicate this to the EMS unit.  This is a form of triage.  The researcher working with the data later has not idea how the dispatcher communicated with the EMS unit.  So, if the dispatcher consistently triages cases in ways that make the response times for more dire cases shorter, then those cases may do better than they would otherwise.  But if the original mortality rate for those cases was high, making it a little lower will just make it similar to the mortality rate for the ess severe cases that take longer.  Then, there will be no apparent relationship between the  time of response and the morality outcomes.

Dr. Wilde found a way to use some other data--the distance from the location of the person who called for EMS services to the nearest EMS unit--as a proxy for the response time.  People have used this type of proxy (or to use the technical term, instrumental) variable before--to show things like the effectiveness of more intense treatment for heart attacks. In that case, there was a similar concern about the severity of the condition being observable to the medical care provider but not the researcher.

In the end, Dr. Wilde found that a one minute increase in response time was associated with an 8% mortality increase one day after the incident and a 17% mortality increase 90 days after the incident.

So, now we have an evidence base for efforts to improve response time.  What is the most appropriate way to do that?  That is a separate economic, political, and normative question.  It could involve technology of locating individuals.  It could involve technology for traffic control?  It could involve enforcement of traffic rules.  Or it could involve a change in norms where people are more aware of the true costs of not moving out of the way of EMS vehicles as quickly as possible.

Regardless, the study by Dr. Wilde shows that every minute can be associated with increasing the potential to save more lives.  

Some less well known costs of obesity

I've commented many times on obesity over the past couple of years of writing in this blog space.  Here is a link to a Baltimore Sun piece about some less well known costs of obesity.

The key question is just which portion of these costs get captured in most economic evaluations of efforts to reduce obesity.  And, looking at which appears in the text, just how are we defining obesity.  The text of the article uses the term "mild obesity".  In the most recent reading I'd done on the topic, this was referred to as "overweight but not obese".  Yes, it is all a matter of semantics as it is the same BMI range-more than 25 and less than 30.  (I'm lucky enough to be just under 22.5 and still I try to be careful.)  But labeling does make a difference in how people interpret the information.  All the stuff that we used to say about "stick and stones may break my bones but names will never hurt me"--probably bogus.  Words matter.  Perceptions matter.  Perceptions affect behaviors and behaviors affect people's weight, health, and their notions of whether they can do much about it.

The costs that I have not seen before are things like the cost of extra airline fuel to carry passengers.  I'm not sure whether those numbers are literally just for the passengers' weight or also, presumably, for the extra clothing and perhaps even larger luggage.  Same goes for cars carrying people.  And, even more interestingly, if those change, then what about all the other effects such as how the increasing price of fuel will change the amount of money people have to spend for other things.

We can realistically only trace the effects so far.  The key is that the numbers of dollars and cents may be a bit bigger than we had previously anticipated.  

Medicare's Poor Incentivizing

Today's Johns Hopkins Bloomberg School of Public Health news feed provided a link to an article on what has been described as waste in the Medicare system.  The key is that as part of the health care reform legislation back in 2010, payments to most Medicare managed care plans were cut, but there was an incentive created to make bonus payments to plans that offer high quality care.

The concern now is that that the bonus payments have been given to plans with only mediocre quality care.  The plan has cost more than was intended as the bonuses have gone to many more plans than were expected.  It may seem patently obvious but giving bonuses to a large number of plans with so-so quality will not provide any incentive to provide care at the highest quality level.  That type of incentive might have been given by using the same amount of money (or perhaps even less) to provide a significant bonus to a smaller number of Medicare plans.

The Obama administration claims that it will not cancel the program as it is still expected to help to improve the quality of care being provided.

This is obviously highly questionable, and, if the newspaper story is correct, this is not likely to be a cost-effective way to improve the quality of care.

While the amount of money is small relative to the entire health care or Medicare budget is small, the principle is that making poor use of even small amounts of resources will sooner or later add up to poor uses of large amounts of resources.

Something to think about as we move forward with the implementation (or not) of the health care reform legislation from 2010. 

"Americans Still Not Exercising"

I put the title for this week's blog post in quotes as it is the exact title of the article that you can find by clicking here.  This is a short piece in the Baltimore Sun that came to my attention this morning thanks to the Johns Hopkins Bloomberg School of Public Health News Feed.  The title is pretty self-explanatory and the news "bite" is shorter than this blog entry will be.

First, let's acknowledge one thing.  The survey that says fewer people are active was conducted by...the Sporting Goods Manufacturers Association.  The fact that they have an incentive to tell people they are not sufficiently active so they can sell more goods is not lost on me.

Second, the change in the number of inactive adults over a one year period was a 1.6 percent increase--noticeable but not huge.  What is more noticeable is the 8 percent increase in a three year time period.  The article does not make clear whether that is adjusted for population growth or not, but 8 percent in three years is certainly more than could be accounted for by population growth.

Third, the piece says that Utah is the most active area.  Here, the article makes a distinction between regular exercise and complete non-participation.  I am not sure what it is about Utah, but it seems like a place that has great skiing, moderate temps for part of the year, and a lot of open space.  Since hiking and camping are activities listed, this may help to account for some of this difference.

Fourth, the piece comments that southern states are less active.  I'm not sure that could explain complete non-participation, but I could certainly see regular exercise being an issue for those who don't have a climate controlled place in which they can exercise.

Fifth, the article points out that hiking and camping are growing in popularity.  At least hiking is something that does not necessarily require organization, can be social as well as physical, does not require a gym membership, and can be done with little planning ahead.  It seems like a lot of forces in people's lives might push them toward this type of activity.

Finally, there is also the comment that yoga and boot camp classes are popular.  I don't participate in either regularly but I have done each at different points in time.  I would be very interested to know what brings more people to each of two very different activities.  I don't have a strong hypothesis about this one other than a general trendiness of each.

So, the brief news piece does not have all bad news, but does leave us to wonder what incentives or information could be used to move the 24 percent of US adults labeled as inactive (i.e., no participate in any of 119 possible activities) to do something.