May 2009

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Many parents believe that vaccination provides little benefit for their own children, and instead only reduces the probability other kids get sick.  A Kaiser Permanente study refutes this belief for the whooping cough:

Compared to children who are immunized against whooping cough, those who aren’t vaccinated are 23 times more likely to get the infection — a finding that shows the danger faced by children whose parents refuse to have their children vaccinated.

“This study helps dispel one of the commonly held beliefs among vaccine-refusing parents: that their children are not at risk for vaccine preventable diseases,” lead author Jason Glanz, a senior scientist at Kaiser Permanente’s Institute for Health Research, said in a Kaiser news release.  

“It also shows that the decision to refuse immunizations could have important ramifications for the health of the entire community. Based on our analysis, we found that one in 10 additional whooping cough infections could have been prevented by immunization,” Glanz said.

At least that is what a study by Falk and Kosfeld (2006) found.  

The research question they tested was if the principal can set a minimum level of x, should they?  An economist would say, of course.  The agent has an incentive to not work at all.  Setting a minimum level of work would guarantee a return for the principal.  To test this, they set up the following experiment.  

  • An agent (worker) must decide on a level of x, which is seen as a proxy for work.  The cost function is simply c(x)=x.  They have an initial salary of 120.  Thus, their payoff is 120-x.
  • The principal (boss) wants the agent to choose a large x (aka to work as much as possible.  Their payoff is 2x.
    • If the agent decides x=20, then the worker gets 100 and the principal gets 40.

However, it turns out that setting a minimum level of x is counterproductive.  By setting the minimum level of x, this shows that the principal does not trust the agent.  This leads the agent to choose a lower level of x chosen by the worker.  For instance, if the principal chooses a minimum x of 5, the agents chose x=12.2 on average.  However, if the principal does not choose a minimum x, then the agent chose an average x of 25.1.Further, principals knew this would happen.  About two-thirds of principals decided not to dictate a minimum level of x.  

The best interpretation of these results can be shown by an excerpt from the memoirs of David Packard, the founder of Hewlett-Packard (HP):

“In the late 1930s, when I was working for General Electric…, the company was making a big thing of plant security. … GE was especially zealous about guarding its tool and parts bins to make sure employees didn’t steal anything. Faced with this obvious display of distrust, many employees set out to prove it justified, walking off with tools and parts whenever they could. … When HP got under way, the GE memories were still strong and I determined that our parts bins and storerooms should always be open. … Keeping storerooms and parts bins open was advantageous to HP in two important ways.  From a practical standpoint, the easy access to parts and tools helped product designers and others who wanted to work out new ideas at home or on weekends. A second reason, less tangible but important, is that the open bins and store- rooms were a symbol of trust, a trust that is central to the way HP does business” (David Packard, 1995, p. 135). 

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Yesterday, May 27, Clive Granger passed away at the age of 74.  Dr. Granger was one of the most-respected faculty members at UC-San Diego.  I frequently saw him around the Economics department even when he was in his 70s.

Dr. Granger won the Nobel prize in 2003 for is work on the econometrics of time-series data.  In 2005, Dr. Granger was knighted.  Over his entire career, Dr. Granger published 12 books and over 250 articles.

He will be missed.

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The latest edition of the Health Wonk Review is up at Boston Health News.  It’s even Bruce Springstein themed.

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When beginning your research, here are the questions you need to ask yourself [from Mostly Harmless Econometrics]:

  1. What is the causal relationship of interest?  What specific mechanism will cause a change in the dependent variable of interest?  Often one uses economic theory to predict these causal relationships.
  2. What experiment could be used to capture the causal effect of interest?  Before you can decide on an identification technique, one must figure out what the ideal experiment would be.  If you want to estimate the effect of physician payment on surgery rates, would you randomize patients to different physicians?  Different physicians may select into different payment schemes.  Would you randomize physician payment?  In this case, different types of patients may select different doctors.  What would be the ideal?
  3. What is your identification strategy?  Many medical studies use randomized control trials, but there are very few RCTs investigating economic phenomenon.  A researcher must decide how to eliminate problems of selection and endogeneity.  Common strategies include OLS, difference-in-difference, instrumental variables, and others.
  4. What is your mode of statistical inference?  This is the nitty-gritty stuff.  How will you estimate your standard errors?  What variables do you include in your regression?   Is the sample representative?  What is the correct group to study.  In my paper on Marriage and Weight Gain, I limit the sample to individuals aged 18-55 since these are individuals most likely to be in the dating market.  

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Case and Paxson (2009):

We document the impact of the AIDS crisis on non-AIDS related health services in fourteen sub-Saharan African countries…Regions of countries that have light AIDS burdens have witnessed small or no declines in health care, using the measures noted above, while those regions currently shouldering the heaviest burdens have seen the largest erosion in treatment for pregnant women and children.

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Yesterday, I attended a lecture by Peter Wagner about grant-writing.  The talk focused on grants in the sciences, and I will pass on Dr. Wagner’s advice to my loyal readers.

Three Golden Rules

  • After writing each section of the grant, re-read it as if you were a reviewer.
  • Finish the grant application at least 2 weeks before the due date.
  • Put the grant away for at least 1 week. Then go back and re-read it.

Other helpful tips

  • You proposal will be valuable if it is: 1) novel and can contributes significant knowledge to the field, 2) is technically feasibly and uses sound methodology, 3) can test your hypothesis as definitely as the state of the art permits.
  • The abstract is the most important part of your grant application. Make sure it is concise and generates interest in your project even among those who are not specialists in your field. Be sure reviewers can easily answer the question “Who cares?” when reading over the grant application. Also, do not put any references in the abstract.
  • For the sciences, the objective of your grant application should be the additional knowledge you wish to be gained from the study. The specific aims are the broad steps that need to be accomplished in order to accomplish your objective. For instance, if going to Seattle for a conferences is your goal, then the specific aims would be: book a flight, reserve a hotel room, register for the conference, etc.
  • Dr. Wagner recommends that in the Methods section, the subheadings should relate to each specific aim.  For instance, “Experimental Design for Specific Aim 1.”
  • Your literature review should be concise but also display that you have an understanding of the field.  Be sure to include all major papers in the field.  Also, include papers whose methodology you will use or will expand in your methodology.  If possible, check who will be on the reviewing committee.  If a reviewer has published papers that are relevant to your area of study, be sure to cite them in your grant application.
  • Be sure to justify your budget.  Do not say “15% of Joe Blogg’s time is required therefore 15% of salary is requested.”  Instead, spell out in detail the time requirements including set-up, execution, data analysis and writing up the finished product.  Also, justify expenses for equipment, supplies and travel.  If you can’t answer the question “Why couldn’t you have done this for 80% of the budget you are proposing,” then you have not sufficiently justified your budget.

See also: Wagner (1991) “On writing a grant application. A personal view.” Physiologist. 1991 Apr;34(2):29-31.

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Since 2004, every service man and woman killed in Iraq or Afghanistan has been given a CT scan.  The military has a database of over 3000 of these scans.  This information “…has revealed deficiencies in body armor and vehicle shielding and led to improvements in helmets and medical equipment used on the battlefield.”

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Using data from the 2004 and 2006 Health and Retirement Survey (HRS), Levy and Weir (2009) analyze the take up of Medicare Part D after its enactment on Jan 1, 2006.  They find that in 2006 only 7% of seniors lacked drug coverage compared to 24% in 2004.  It seems that Medicare Part D caused this large increase in drug coverage.

Medicare Part D Eligibility

Medicare Part D eligibility can be defined as follows:

  • Medicaid-covered Medicare beneficiaries (“dual eligibles”) were automatically enrolled in both Part D and a means-tested subsidy. 
  • Individuals with other coverage–usually through their employer–were instructed to keep their coverage.
  • Medicare Advantage (MA) plans had to offer drug coverage after Part D was implemented. Many of the MA plans, however, already had included drug benefits in their benefits package.
  • Individuals with private, non group insurance or those without prescription drug insurance had to decide whether or not they wanted to enroll in a Part D plan.

Results

The evolution of senior drug coverage is shown in the following table.  After the implementation of Medicare Part D we see that  7% of seniors lacked drug coverage compared to 24% before part D.  Why didn’t these 7% take up Medicare Part D?  Are they uneducated?  Are not native English-speakers? It turns out that they just have low demand for prescription drugs.  ”Those with low levels of education or income were no less likely to enroll in Part D than were beneficiaries with more education or income.”  Also, the authors find that 41% of individuals who didn’t take up Medicare Part D said they didn’t need any medications. 

Crowd out

Did part D crowd out employer drug coverage?  We did see  employer drug coverage drops from 40% in 2002 and 2004 to 37% in 2006.  However, individuals who have employer-provided drug benefits were almost just as likely to retain these benefits in 2004 as in 2006.  The authors argue that “while this does not rule out the possibility that some individuals dropped employer drug coverage because of Part D, it suggests that most new Part D enrollees are coming from individuals who would have remained uninsured or purchased Medigap in the absence of Part D.”

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