June 2009

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Friday Links

June 12, 2009 in Uncategorized | No comments

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Paying poor people to get vaccinated

June 11, 2009 in Health Care in Developing Nations, Public Policy, Vaccinations | No comments

Conditional Cash Transfer (CCT) programs have become very popular among development economists.  This programs pay poor families to have their children attend school and/or get vaccinated.  Some of the larger programs include Bolsa Família in Brazil and Oportunidades in Mexico.  

Should economists support CCTs that pay the poor to get vaccinated?  This depends on 2 factors: 1) are these program effective and 2) what are the unintended consequences of implementing CCT.  Let us review both issues.

CCT program effectiveness

Barham and Maluccio (2009) review some of the studies on CCTs and vaccination rates.

  • Mexico.  ”Barham et al. (2007) examine the effect of the Mexican CCT program, Oportunidades, in rural areas using data from a randomized experiment…They find small average program effects, on the order of 3 percentage points, and argue that this is due to high coverage rates (above 90%) before the program. They also find…larger effects for those children whose mothers were less educated or who lived further away from a health facility.”
  • Honduras. “Morris et al. (2004) examine the impact of a conditional voucher program in rural Honduras, also using a randomized experiment. They find small significant increases for the first dose of DPT and no effect for MCV, but do not investigate DPT3 or sub-population effects.”
  • Nicaragua. Barham and Maluccio (2009) investigate the Red de Protección Social (RPS) CCT which began in 2000.  They find that ”the program led to large increases in vaccination coverage…Effects were particularly large for those sub-populations that are traditionally harder to reach children who live further away from a health facility or whose mothers are less educated. In terms of achieving eradication, on-time vaccination coverage in the treatment group was close to or greater than 95% for BCG, OPV3 and DPT3 by 2002, whereas it remained below 90% for the country as a whole for OPV3 and DPT3.”

Overall, it does seem that paying individuals does increase vaccination rates.

Unintended Consequences

Below is a list of some of the unintended consequences of CCTs:

  • Weakening the formal sector and stifling tax revenues.  Usually, only poor individuals are eligible for these conditional cash transfers.  This program structure gives poor individuals an incentive to either underreport income or to avoid participating in the formal sector workforce.
  • Using CCTs for political means.  Politicians may vary the CCT by municipality to get votes.  Populist candidates will advocate raising the level of the cash transfer to attract the votes of the poor.  
  • Corruption.  Whenever government officials are handing out cash, one has to worry that some portion of program funds land in the pockets of program administrators.  
  • Weakening of the social contract.  Many individuals may get vaccines to protect their children, but also to protect the health of one’s neighbor.  Gneezy and Rustichini (2000) found that fining parents who are late to pick up their children from school actually increased tardiness.  Similarly, the long term effect of paying people to get vaccines may reduce citizen’s motivation to get their children vaccinate in the absence of payment.
  • Effect on Government Credibility in the cash of a deadly vaccine.  Earlier this month, I blogged about how the rush to produce a flu vaccine in 1976 actually lead to dozens of deaths.  If the government uses a CCT to convince individuals to take an unsafe vaccine, the political backlash will be overwhelming.  Poor citizens may lose faith in a number of other well-intentioned government initiatives.

Conclusion

So what do you think?  Are conditional cash payments to increase vaccination coverage a good strategy?

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Health Wonk Review – Health Reform Edition

June 11, 2009 in Carnivals | No comments

Sick and tired of today’s health care system?  Joe Paduda hosts this weeks Health Reform HWR, where scholars, professionals, and pundits all give their opinion of how to improve our healthcare system.

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50 days to see a doctor in Boston…Is Massachusetts’ universal coverage laws the cause?

June 9, 2009 in Health Insurance, Supply of Medical Services, Wait Times | 8 comments

From the USA Today, here are the wait times to see a doctor in the following cities:

  • Boston: 49.6
  • Philadelphia: 27
  • Los Angeles: 24.2
  • Houston: 23.4
  • Washington, D.C.: 22.6
  • San Diego 20.2
  • Minneapolis: 19.8
  • Dallas: 19.2
  • New York: 19.2
  • Denver: 15.4 days
  • Miami: 15.4 days

The first thing that jumps out from these numbers is that Boston has by far the longest wait to see a doctor.  Is this caused by the universal health coverage enacted in Massachusetts?  The answer is maybe.  Physician supply adjusts slowly (i.e., it takes a long time to finish med school).  On the other hand, Massachusetts decision to increase insurance coverage lead to a spike in the demand for medical services.  Thus, universal health care may have caused the run up in wait times, but this phenomenon may be short lived.  Physicians may migrate to Massachusetts as insurance coverage becomes more available.  

Do wait times reflect quality of care?  If Boston residents have very short waits to see nurse practitioners or physicians assistants, this could be a cost-effective substitute for services provided by physicians in the primary care setting.  Further, longer wait times for specialists could be a good thing.  While longer wait times would certainly hurt some patients–likely the most seriously ill patients–it would discourage other patients from waiting to see a specialist.  This patients could, instead, forego treatment if had a low marginal benefit to begin with or they could rely on their primary care provider.  

Let’s dig deeper into the numbers (see original report):

  • Wait times for Boston cardiologists decreased from 37 days in 2004 to 21 days in 2009.  
  • Wait times for Boston orthopedic surgery increased from 24 days in 2004 to 40 days in 2009.  
  • Wait times for a Boston ObGyn increased from 45 to 70 days between 2004 and 2009 in Boston.
  • Wait times for a Boston Family Practice physician was 63 days in 2009. 

We see that after the Massachusetts health reform was enacted, there was no uniform effect on specialist wait times, but there was a large increase in wait times for primary care providers.  This could be explained by a number of phenomenon:

  • Those who gained health insurance after the Massachusetts health reform were a healthier population and used their new insurance coverage to increase the number of primary care visits, but not specialist visits.
  • After the Massachusetts health reform, the increase in demand was homogenous across primary and specialty care.  However, physician supply adjusted.  Specialist may have been more attracted to practicing in Massachusetts, but primary care doctors were not.  Specialists may have moved to Massachusetts in larger numbers, particularly if New England health plans reimburse specialists at a much higher rate.  
  • This could be a statistical anomaly.  Sample sizes in were less than 20 for five specialities in Boston.

Whatever the case, further study is needed to understand how health insurance expansions affect waiting times in both the short- and long-run.

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Shadow Price

June 9, 2009 in Economics - General | No comments

Let us say you are a person trying to choose between buying bananas and oranges. What you are trying to do is maximize a utility function u(x,y) where x represents bananas and y represents oranges.  You can not buy an infinite amount of each however.  This is subject to a budget constraint.  Thus, we have the following maximization problem.

  • max u(x,y) s.t. p1x + p2y ≤ I

If we add functional form assumptions on the utility function we can form the following Lagrangian:

  • L= ln(x) + αln(y) – λ[p1x + p2y - I]

Our first order conditions are:

  • Lx:  1/x – λp1 =0
  • Ly:  α/y – λp2 =0
  • Lλ:  p1x + p2y – I =0

Our optimal level of bananas and oranges is:

  • x* = I/[(1+α)p1]
  • y* = Iα/[(1+α)p2]
  • λ* = (1+α)/I

We solved for x* (bananas), y* (oranges), and λ*, but what the heck is λ? The term λ is the shadow price. The shadow price represents the following: assume that instead of I dollars of income, we had I+ε dollars. If we had the extra ε of income, the shadow price λ tells us by how much the objective function (utility function) would increase since we could buy a few more apples and bananas.

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Top Countries for Biotechnology Innovation

June 8, 2009 in HC Statistics, Health Care Around the World, Pharmaceuticals | No comments

Scientific American has an article ranking countries based on how conducive they are to biotechnology innovation.  The criteria are based on what is best for biotech firms and not necessary what is best for society.  The rankings are based on the following metrics:

  • Intellectual Property (IP) protection.  In this ranking, more IP protection is considered better.  For a firm’s point of view, better IP laws lead to more profits and a higher incentive to innovate.  However, IP also increases cost and may decrease innovation of biotech companies modifying existing patented compounds.  The U.S. ranked #1 in this category with Canada, Japan, and a number of EU nations tied for 2nd place.
  • Intensity.  This looks at the how much money is directed towards R&D and how much drugs a country can afford.  Metrics include: public companies per capita, portion of overall R&D spending used for biotech.  Here, Iceland is by far the leader, the U.S. is 2nd, and New Zealand third.
  • Enterprise Support.  Measures how “business friendly” a country was perceived to be and the availability of various forms of capital, which are essential to support the growth of emerging biotechnology firms.  Top 3 Countries: U.S., Singapore, Australia.
  • Education Workforce. The more educated the workforce, the better the score.  Education was measured in the number of R&D and biotech workers as well as the number of published papers.  Top 3: Singapore, Switzerland, U.S.
  • Foundations.  Includes broad measures of foundations for biotech innovation.  Top 3: Israel, Sweden, Finland.

Overall the Top 5 Countries for Biotechnology Innovation

  1. U.S.
  2. Singapore
  3. Denmark
  4. Israel
  5. Sweden

You can see the full list of rankings here.  More discussion on these rankings can be found here.

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Medical Advances

June 7, 2009 in Medical Studies | No comments

The Economist‘s Technology Quarterly reveals some recent advances in medical technology:

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Public Health Plan

June 6, 2009 in Uncategorized | 2 comments

Should we have “a public plan that Americans can buy into as an alternative to private insurance?”

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CPS undercounts Medicaid Enrollees

June 5, 2009 in Data, Medicaid/Medicare | No comments

Survey estimates of Medicaid enrollment are 43 percent lower than raw Medicaid program enrollment counts.  Why is this the case?  Roebuck and Liberman (HSR 2009) find that many people are not reporting that they have Medicaid coverage.  “43 percent of Medicaid enrollees answering the CPS as though they were not enrolled and 17 percent reported being uninsured.”

One reason for the underreporting could be that the poor may only enroll after they get sick.  Further, if they do not pay for Medicaid, they may feel that they are just receiving government assistance rather than “insurance.”

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Estimating Price increases for Medicare using Bundling

June 4, 2009 in HC Statistics | No comments

One question that plagues health economists is estimating the price inflation of medical care.  The output measured in these indices should be “health,” but this is often difficult to measure. Typically, economists simply look at the price increase of different inputs (e.g., the cost of a doctor’s visit, the cost of pharmaceuticals, the cost of different procedures)

In a novel approach, Aizcorbe and Nestoriak (2008) use cost of a bundle of treatments for a given disease as the unit of measurement.  This way, the authors can track how the cost to treat a specific disease changes over time. Even if the input costs for treating a disease increase over time, the mix of inputs used to treat a disease may also shift.  Further, if a new treatment arises, this will be included in the treatment bundles, eliminating the “new goods problem.”  The authors find the following results:

“ While the price of treating diseases grew an average of 12% over this period [2003-2005], costs would have risen even faster, 17%, if the mix of treatments in 2005 had been the same as that in 2003…For example, in the treatment of depression, there  has been a shift away from talk-therapy and towards (the lower cost) drug therapy that has reduced the cost of treating depression…[also, there have been] shifts from care at hospitals towards care at ambulatory surgical centers for orthopedic and gastroenterological conditions…”

In order for this estimation to be valid, a few things must hold.  First, disease severity must be constant over time.   Second, treatment outcomes must be constant over time.  If increase spending leads to a better outcome, this may not be price inflation, but simply a better treatment bundle.  Bundling will not correct this problem.  Third, the period of observation is only over 3 years and the data lacks information on Medicare claims.  Nevertheless, the bundling of treatments provides an attractive option to constructing medical care price indices.

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