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Physician Reimbursement and Technology adoption

December 3, 2007 in Academic Articles, Physician Compensation | Permalink

Economists and health researchers have generally shown that when doctors are paid on a fee-for-service basis, they will advice the patient to undergo more medical procedures than when the doctor is paid on a capitation or salaried basis (see my own paper: “Operating on Commission“). Which payment method maximizes welfare has not been proven and is likely to vary based on the medical procedure in question.

To add to the confusion, Astrid Selder writes in “Physician Reimbursement and Technology Adoption” that physician reimbursement mechanism may also affect the rate at which new technologies are adopted.

Selder lays out the following simple theoretical structure in which the social planner maximizes the following:

  • (1-π)U(Y-P)+πU(Y-P-acm-ε +G(m) -nm)
  • s.t.: P + πacm-%pi;R=πpm>=0
  • s.t.: R + (p-c)m>=0
  • s.t.: m<=B

The first constraints represent the zero-profit conditions for the insurer and the physician respectively and the third is a technology constraint. The probability of falling ill is π. Individuals have income Y, an insurance premium of P, and a coinsurance rate of a to may for the marginal cost, c, of a given level of medical treatment, m. Non monetary treatment costs (e.g.: side effects, time costs) are represented by the parameter n, while the benefits of the treatment are represented by the function G(m). The doctor receives a capitation payment of R and a marginal revenue amount given by p-c. B represents the technology constraint.

It seems obvious, but Selder shows that an increase in monetary medical costs (c) or non-medical costs (n) are welfare destroying. Technological innovations, represented as an increase in B are welfare improving if the technological constraint is binding or have no impact on welfare is the technological constraint is not binding. This results is intuitive: higher costs are bad, technological innovation is good.

This result, however, only holds when there is perfect information. With asymmetrical information, technological innovations may be welfare enhancing or welfare destroying. The welfare enhancing argument is similar to above: better technology leads to better medical care. If there is a moral hazard problem, however, using more basic technologies may actually help to counteract the tendency to over-consume medical care in the presence of insurance. Also, in the presence of moral hazard, technological innovations will cause insurance companies to charge higher premiums in order to cover the additional cost of this care.

How does the technological constraint, B, affect welfare in different physician compensation schemes?

  • “If B is a binding constraint and increases in B are welfare enhancing then a fee-for-service system should be used. Increases in B and reductions in [monetary medical costs] c are induced all of which are welfare improvements. Incentives with respect to [non-monetary costs] n cannot be improved upon.
  • If B is a binding constraint and increases in B are welfare reducing then a system of cost sharing [e.g.: capitation] should be implemented which induces reductions in B and c and gives no clearcut incentives with respect to n. Incentives with respect to B and c are thus optimal, and incentives with respect to n cannot be improved upon.
  • If B is not binding the optimal reimbursement system depends on the demand elasticities with respect to costs.”

In words:

“It has turned out that the state should classify diseases or treatments into subgroups which are reimbursed differently in order to achieve the desired welfare effects in each subgroup. Especially for the case of extremely severe illness shocks the introduction of a fee-for-service system may be socially desirable. It is these very severe diseases where the capitation system seems to induce negative welfare effects with respect to the technologically feasible boundary of treatment. Cost sharing/capitation systems are most suitable for treatments where a reduction of the amount of health care consumed is desirable.”

While the solution Selder proposes is logical, it does not take into account the cost to the state to collect information regarding these disease classes. Lobbying will likely influence whether a given treatment falls into the fee-for-service or cost-sharing grouping. Nevertheless, Selder’s general findings seems reasonable and applicable to the medical care finance design in the real world.

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Value-based pricing

November 27, 2007 in Academic Articles, International Health Care Systems, Pharmaceuticals | Permalink

In February of 2007, the UK’s Office of Fair Trading (OFT) recommended reform to Britain’s current Pharmaceutical Price Regulation Scheme (PPRS). The PPRS sets maximum and minimum profit levels from the sale of branded drugs to the NHS. The PPRS allows companies freedom to set prices as they please on new substances, but restricts subsequent prince increases. A system of price cuts has also been instituted as well, yet it is likely that firms take these future price cuts into account when making their original pricing decisions.

Reform

Are there any other options? Simeon Thornton (Health Econ 2007) argues that a value-based pricing (VBP) scheme would be superior. In VBP, the NHS would pay pharmaceutical companies based on the value of the pharmaceutical to the patient base. One question is how value is determined. Thornton proposes cost effectiveness studies, which in effect means that the government or academics will determine the price.

Pricing will also be allowed to vary by subgroup since people with certain diseases may benefit more from a disease than others. Also, incremental improvement will be encouraged since marginal improvements in treatment will receive higher payments.

This program does seem to be an improvement. It is dynamically efficient since pharmaceutical companies will be paid more for more cost effective treatments. Further, if it turns out patients do not like the medicine and no one takes it, then NHS will not be paying a lot for failed drugs. Also, after generics are available, the price will adjust downward.

In the static environment, the pharmaceutical company will capture all the consumer surplus since price will equal marginal benefit. However, as time passes and generics enter the market, a large consumer surplus will occur.

The major impediment of this reform is the problem of any centralized system: information. How will the NHS determine cost-effectiveness? Will it be impartial? Will the conclusions be manipulable by interested parties? These questions are easy to answer theoretically, but very difficult to predict empirically.

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Commentary on P4P

November 16, 2007 in Academic Articles, Physician Compensation | Permalink

I recently read a Health Affairs article analyzing a pay-for-performance (P4P) demonstration. The Local Initiative Rewarding Results (LIRR) demonstration in California involved seven Medicaid-focused health plans in California between 2003 and 2005. Here are some of my most recent thoughts on P4P:

  • The article seemed to show that P4P worked best when there was much consensus over how doctors should treat patients. The irony of P4P is the following: P4P programs with high levels of physician consensus work, but if everyone physician is already acting appropriately P4P does not make a major change in behavior; larger behavioral changes can occur when physicians are greatly deviating from the optimal practice, but in this case it is very difficult to implement P4P.
  • P4P plans are often implemented so those that do well gain income and those that preform poorly maintain their status quo income. When no one loses any money, P4P is supposed to gain more acceptance among physicians. In equilibrium, however, an economist would predict that those with low P4P scores will receive lower wages in the future (or wage increases below inflation) due to P4P.
  • Unsurprisingly, the authors of the Health Affairs paper found that meager financial incentives do not significantly change physician behavior, while larger financial incentives have a greater impact. Also, good communication with physicians helps to increase physician compliance with P4P mandates.
  • If P4P evaluates doctors on care levels which depend on patient compliance, health plans can help physicians to increase the quality of care. For instance in the above study, “many providers used the plan lists of members turning fifteen months old as the basis for outreach…”
  • When P4P financial incentives vary across plans, it is difficult for physicians to focus on specific quality indicators due to complex reimbursement schemes in each plan.
  • When HEDIS scores improve, some of this improvement is due to better quality while much of it may be attributed to better documentation in a patient’s medical records.

Suzanne Felt-Lisk, Gilbert Gimm and Stephanie Peterson (2007) “Making Pay-For-Performance Work In Medicaid” Health Affairs, v26(4) pp. w516-w527.

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The truth about the RAND HIE

November 15, 2007 in Academic Articles, Health Insurance | Permalink

Recently, there has been much controversy regarding whether or not the RAND Health Insurance Experiment (HIE) results are truly robust. Many blogs have been questioning the results (see here, here and here). One of the major conclusions of the HIE are that higher co-insurance rates lead to lower levels of medical utilization and lower medical cost, but do not have any adverse impact on health outcomes.

A paper by John Nyman (2007) in the Journal of Health Politics, Policy and Law calls these findings into question. He notes that there was differential attrition in the insurance plans with cost-sharing for the patients compared to those with no cost-sharing. If people who became sick in the cost sharing insurance plans elected to drop out of the experiment and seek care from their previous insurance plan, then an attrition bias would occur. This bias would incorrectly deflate the medical utilization of the cost sharing group and thus lead to the erroneous conclusion that more cost sharing causes lower medical utilization patterns.

Nyman’s paper also wisely notes that moral hazard is only a problem with individuals afflicted with a disease. For instance, no matter if one has insurance that would reduce the price of a mastectomy to zero, no one ever elect to have a mastectomy unless they had breast cancer. There are personal costs of the side effects of treatment and time costs which imply that moral hazard will generally only be a problem for those afflicted with a disease. There are exceptions. For cosmetic surgery procedures, individual do not need to be sick to suffer from the problem of moral hazard with insurance.

Nyman’s points are valid, but I believe that the RAND HIE results are robust. First, each RAND HIE participant receive a participation payment each month. Even if the individual had large medical expenses and had to pay a large deductible (capped at $1000, which $4000 in 2007 dollars), the monthly participation fees would add up to more than $1000 so it was in the individual’s best interest to stay in the experiment. Nyman argues that cash flow problems many have affected some participants. If I receive $100 per month for a year, but have $1000 in medical expenses today, I may decide to leave the experiment. Yet because of this participation fee, it seem that this would not be a major problem.

Also, the problem of attrition was recognized in the original RAND HIE. Nyman himself states on his website:”When the RAND data were re-analyzed to account for the differential utilization from attrition, the difference in hospitalizations between free care and all cost-sharing arms was about 19 percent, not 25 percent as has been reported (Manning, Duan, Keeler, 1993, p. 13).” While the magnitude of the cost sharing effect may be somewhat smaller once we take into account attrition, the general finding remains the same: more cost sharing leads to less utilization.

Finally, while RAND is the only large scale RCT that has been conducted, many other studies have shown that increased cost sharing leads to lower medical utilization. Newhouse states that “enormous number of observational studies over many years, in many settings, with many different methodologies, that find utilization of medical services responds to relatively modest cost sharing.” Nyman’s response is that:

“I do not dispute that cost sharing reduces utilization. I think it does and agree that the non-experimental studies that Newhouse et al. (2007) refer to are generally convincing. The issue that I am addressing, however, is whether cost-sharing in the RAND Experiment actually produced a 25 percent reduction in equally effective hospitalizations and whether such a reduction would actually have no effect on health.”

One question you may still have is how is it possible that lower medical utilization leads to the same health outcomes. One possibility is that the moral hazard may have resulted in only the excess use ineffective medicine. It could be the case that much of medicine has little impact in the overall health of the patient and too much treatment can actually harm the patient (see my post on Overtreated). It could be the case that health was measured poorly by the HIE, but Nyman concedes that a “broad spectrum of health status measures” where used in the RAND experiment.

Overall, I do believe Nyman brought up some reasonable points. No experiment is ever perfect…even the RAND HIE. Attrition was a problem and may affect the magnitude of the result. Nevertheless, the conclusions from the RAND HIE remain robust and I believe that the attrition issue was addressed previously.

Can we still conclude that more cost sharing reduces medical utilization? Is moral hazard a problem in health insurance markets? We can still confidently say: YES!

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Local Instrumental Variables

November 6, 2007 in Academic Articles, Econometrics | Permalink

Traditional instrumental variables (IV) econometric methodologies often fail to take into account response heterogeneity. Response heterogeneity based on characteristics not observed by the researcher can create a heterogeneity in the self-selection process. For instance, one group of people who elect to receive surgery may have knowledge of a family history where surgery is typically successful, whereas another group may elect not to receive surgery due to a different family history. If this information is unobservable to the researcher than an analysis of the average of effect of surgery may be biased. In the medical context, traditional IV assumes that:

  1. treatment effects are constant conditional on observed characteristics, or
  2. if treatment effects are heterogeneous, patients or physicians cannot anticipate these effects and use this information to select the most beneficial treatment.

In traditional IV, the treatment parameter gives researchers a local average treatment effect (LATE). But can a researcher characterize a heterogeneous response using IV? A solution to this problem is presented by Basu, Heckman, Navarro-Lozano and Urzua in a 2007 Health Economics paper. They use a local IV to estimate marginal treatment effect (MTE) parameters.

Basic Econometrics Review

Let us assume that a person will have two different outcomes based on whether or not they are treated:

  • Y1 = μ1(X) + U1
  • Y0 = μ0(X) + U0
  • Δ = Y1 – Y0 = {μ1(X) -μ0(X)} + (U1 – U0)
  • Y=μ0(X)+D*{μ1(X)-μ0(X)} + {D(U1 – U0) + U0}

The variable Y1 represents the outcome if the person is treated and Y0 represents the outcome if they are not treated. We only have one observation per person, however, since we cannot observe the counterfactual. If we could observe the counterfactual, Δ would give us the effect of the treatment for each person. Unfortunately we only observe Y. The dummy variable D is equal to unity if the person is in the treatment group and zero otherwise. If there were a randomized trial where people are randomly placed into the treatment and control groups, it would be easy to estimate the treatment effect by comparing the mean outcomes of the treated and control groups. We could examine the mean outcomes for individuals with similar characteristics to determine the treatment parameter by subgroup. However, if individuals can select whether or not to be treated, the error term–which may be composed of unobserved heterogeneity in the effectiveness of the treatment–may be correlated with the regressors that impact the outcome.

The traditional solution to the endogeneity problem is IV. Let X be the set of regressors and Z represent the instruments. “LATE computes the mean gain to those induced to switch from no treatment to treatment by a change in Z from z to z‘.”

  • LATE={E(Y|X=x, Z=z‘)-E(Y|X=x, Z=z)} / {P(D=1| X=x, Z=z‘) – P(D=1| X=x, Z=z)}

Marginal Treatment Effect (MTE)

Developed by Björklund and Moffitt (1987) and furthered by Heckman (1997), the MTE measures “the average gain to patients who are indifferent between receiving treatment 1 [the treatment] versus treatment 0 [the control] given X and Z.” The benefit of using MTE is that one can calculate the marginal treatment effect for different subgroups based on the propensity score. This places a high degree of reliance on the accuracy and precision of the propensity score in order to determine these subgroup treatment parameters.

Let V denote a latent variable which measures the difference in benefits from being in the treated and control groups. Treatment choice can be modeled as follows.

  • V= μv(Z,X) + Uv
  • E(Uv)=0
  • D=1(V>0)

The authors use a propensity score to determine the probability of selecting treatment.

  • P(z,x)=P(D=1|Z=z, X=x) = P(Uv > -μv(z,x)) = 1 – FUv(-μv(z,x))
  • FUv() is the cdf of Uv.

Now we can define MTE to be:

  • MTE(x,z)=E(Δ|X=x, Z=z, V=0)
  • =E(Δ|X=x, Z=z, Uv=-μv(z,x))
  • =μ1(x) -μ0(x) + E{U1 – U0|Uv=-μv(z,x)}
  • =μ1(x) -μ0(x) + E{U1 – U0|UD= FUv(-μv(z,x))}

where FUv(Uv)=UD. The last equation after the ‘|’ is a monotonic transformation of the terms after the ‘|’ in the third equation.

Local IV (LIV)

The LIV estimates the derivative of the expected outcome conditional on observed characteristics and the probability of electing to be in the treatment group, E(Y|X=x, P(z,x)), with respect to the probability of treatment, P(z,x). The term E(Y|X=x, P(z,x)) is defined as follows:

  • E(Y|X=x, P(z,x))=E{ DY1 – (1-D)Y0 |X=x, P(Z,X)=P(z,x)}
  • =μ0(x) + P(z,x){μ1(x) -μ0(x)} + E{U0|P(Z,X)=P(z,x)} + P(z,x){E{U1-U0 | P(Z,X)=P(z,x), D=1)
  • =μ0(x) + P(z,x){μ1(x) -μ0(x)} + K{P(z,x))

The term K(P(z,x)) is a general function of the propensity score, P(z,x). Often, K() will be a polynomial of the propensity score. The MTE can be computed mathematically as below:

  • {∂E(Y|X=x, P(z,x)) / ∂P(z,x)} |1-P(x,z)=UD
  • = μ1(x) -μ0(x) + ∂K(P(z,x))/∂P(z,x)

The equation above “…is implemented by regressing the outcome Y on all covariates [X], the propensity score, the interaction of the propensity score with all covariates and a polynomial on the propensity score.” This procedure is carried out in the paper empirically by applying these methods to data on breast cancer patients and their choice of breast-conserving surgery with radiation compared to mastectomy.

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When can IT improve medical quality and cut costs

October 31, 2007 in Academic Articles, Asymmetric Information, Healthcare IT | Permalink

Many people believe that better information technology (IT) can help improve the quality of medical care in the U.S. and around the world. For instance, if a doctor prescribes a drug which interacts harmfully with a drug the patient is already taking, a computer program could notify the doctor of this problem. If a patient has a certain disease, a computer program could help to narrow down the physician’s treatment options.

The problem with this top-down, one-size-fits-all doctoring is that every patient is unique. Of course, there are medical standards which should generally be followed, but often these standardized treatments have to be altered slightly or completely depending on patient wishes, the prevalence of multiple diseases, or economic constraints. Further, the physician typically has superior information regarding the patient’s condition than a standardized computer algorithm.

An NBER working paper by Javitt, Rebitzer and Reisman investigates a randomized trial of the implementation of a new IT system at a commercial HMO plan. The program was ‘turned on’ for some patients for each physician in the HMO and ‘turned off’ for other patients. The program would give three care consideration (CC) alerts: stop a drug, do a test, or add a drug. Each of these warnings had 3 severity levels.

The authors compare data on the CC alerts which occurred for the ‘switched on’ patients with the hypothetical CC alerts which would have occurred if the software had been running. The authors found that the IT program lead to 6% lower charges in the turned on group compared to the control. The authors interpret this to be due to lower hospitalization rates and medication complications in the treatment group.

One key aspect of the program is that the CC alerts could be ignored by doctors.

“Physicians often have better information about their patients than does the error detecting software. Actions that look like a misstep to the computer may in fact be the result of informed physician choice, informed patient choice and/or patient non-compliance. For this reason, the HMO and the software company viewed CCs as recommendations that physicians were free to ignore if they disagreed.”

Physicians seemed to heed these CC alerts more than other studies had previously thought they would. Why?

Our results, however, suggest something more: the messages in the CCs seem to have had a bigger effect on physicians than the conventional medical channels used to promote the HOPE trial findings. It seems plausible that the CCs were influential because they linked a general recommendation (“take ACE inhibitors�?) to a specific patient [my italics] and a specific cite to the medical literature.”

To me, this seems to be the most appropriate way to implement medical IT.

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Therapeutic non-adherence: a rational behavior revealing patient preferences?

October 29, 2007 in Academic Articles, Pharmaceuticals | Permalink

Many studies have revealed patient non-compliance with medical prescriptions (e.g.: not taking prescribed drugs, not visiting the doctor) at around 50%. Most medical researchers believe that non-adherence is either due to 1) irrationality or 2) misinformation. Yet a Health Economics article by Lamiraud and Geoffard (2007) tests the hypothesis of whether or not this behavior may in fact be rational.

Most medical treatments have costs. There are monetary costs, time costs, inconvenience costs, and physical costs from any side affects which occur. Physicians often focus on the benefits of medication without fully taking into account the discomfort patients experience from medication.

To solve this problem the authors used a data from a randomized controlled trial (RCT) of HIV-1 infected patients. The trial aimed to test the safety and efficacy of two HAART therapies. The authors use the following system of equations to estimate factors associated with compliance.

  • θ*it=x1itβ1+ε1it
  • h*it=x2itβ2+ θitγ +ε2it
  • εjit=νjt+ηjit

The variable θ* represents a latent variable as to the level of adherence (θ=1 for full adherence and 0 otherwise) and h* is a latent variable representing the health status of the individual (h=1 if the individual is an a good health status and 0 otherwise). The model is estimated using a panel non-linear simultaneous two-equation system.

The authors find that higher adherence levels are associate with higher patient welfare. Thus, if two drugs are equally effective, the authors recommend that policy-makers, insurance companies, and government should select the pharmaceutical with higher compliance levels. “To the contrary, the clinicians of the trial were tempted to support the treatment in which the adherence level was smaller, based on the rationale that a higher adherence would have pushed the efficacy to upper levels in that group.”

Realizing that adherence is an endogenous behavior made by rational individuals may change the way in which the benefits of prescription drugs are measured in the future.

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Lifetime Benefits of Medical Technology

October 24, 2007 in Academic Articles, Hospitals, Specialists, Supply of Medical Services | Permalink

Revascularization (bypass surgery or angioplasty) have been frequently used procedures to treat patients who have experienced a myocardial infarction (MI). These procedure are expensive, but are supposed to enhance longevity. Do they?

This is the question analyzed by David Cutler in his NBER working paper titled “The Lifetime Benefits of Medical Technology.” The problem with many MI studies is that they are short term. Cutler uses data on Medicare beneficiaries who had a heart attack between 1986 and 1988. This means that Cutler can utilize 17 years of follow-up mortality data.

Another issue with non-randomized trials is that of patient selection. Very sick MI patients likely will not receive revascularization surgeries because they not be well enough to survive the surgery. Relatively healthy MI patients may not need revascularization. Thus, even the direction of the selection bias is unknown in this case. In order to account for this selection problem, the paper uses an instrumental variables approach.

The instrument is “the distance to the nearest revascularization hospital [defined as a hospital capable of preforming a revascularization] minus the distance to the nearby hospital of any type.” This instrument was used in a paper by McClellan, McNeil and Newhouse (JAMA 1994). In order for the instrument to be valid, “patients who are more likely to benefit from invasive treatments do not select their residential location based on distance to high-tech medical care.” Cutler argues that this likely true since most covariates are balance above and below the median differential distance. It is possible, however, that richer, healthier people live in more affluent areas and revascularization hospitals locate their facilities to attract these types of patients. I do not know whether or not this is the case. Also, “…if hospitals that provide revascularization are also better at providing aspirin at admission, at managing post-acute follow-up, or at treating subsequent illnesses years later, the instrumental variables estimates will overstate the importance of revascularization.”

Cutler does show that his instrument is relevant as “people who live closer to a revascularization hospital are 3 percentage points more likely to receive a revascularization procedure than those who live farther away.”

Results

The affect of revascularization on mortality is not clear.

“The marginal person receiving a revascularization is about 4 percentage points more likely to survive the first day after the MI than if the person did not receive a revascularization (although not statistically significantly). This gap narrows over time and even reverses by 1 year. At that time interval, people who received revascularization are 6 percentage points more likely to have died than people not receiving revascularization.”

After 17 years, people with MI have a mortality benefit of 5% but this result is not statistically significant. Cutler also examines the cost-effectiveness of revascularization procedures:

“…The greater survival for the marginal patients receiving revascularization translates into 1.1 years of additional life expectancy. The cost of this gain is about $38,000. Thus, the cost per year of life is $33,246.”

A year of life is generally valued at around $100,000, which might lead one to conclude that this is a worthwhile procedure. Since the mortality differential is not measured very precisely, however, one should be somewhat skeptical of this conclusions. Further, Cutler wisely notes that he is not sure whether or not the actual revascularization caused the decreased mortality. Being admitted to a revascularization hospital may simply be a proxy for the receipt of other hospital services, or it could be the case that revascularization hospitals provide superior patient management and patient care than non-revascularization hospitals. Also, due to data constraints, this paper only examines mortality effects. The impact of heart surgery on quality of life is not considered.

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Can poverty improve health?

October 11, 2007 in Academic Articles, Medical Studies | Permalink

A recent paper by Franco et al. (2007) claims that increased poverty may improve health (see also NPR’s Marketplace report). How is this possible? Lower income reduces excess food as well as cigarette consumption.  Further, poverty makes public transportation less affordable and individuals may substitute walking for taking the bus.  The authors study Cuba’s experience between 1989 and 2005.

Cuba has been subjected to an economic embargo by the United States since the 1960s. After the loss of the Soviet Union as a trading partner in 1989, Cuba entered a prolonged economic crisis known as the ‘‘Special Period.’’ The crisis worsened continuously over the next 5 years, with economic output reaching a nadir in 1995 of about half the level in 1990.

The decreased economic activity lead to the following changes:

  • Calorie intake: “Average per capita daily energy intake…declined from 2,899 kcal in 1988 to 1,863 kcal in 1993.”
  • Physical Activity: “In 1987, only 30 percent of the population living in Havana was characterized as physically active. In national data, approximately 70 percent of Cubans were considered physically active in 1991–1995, and 67 percent were active in 2001″
  • Obesity: The prevalences of obesity in Havana were 11.9 percent, 5.4 percent, and 9.3 percent in 1982, 1994, and 1998, respectively. In Cienfuegos, prevalences were 14.3 percent, 7.2 percent, and 12.1 percent in 1990, 1995, and 2001, respectively, reflecting a 49 percent fall during the economic crisis.”
  • Cigarette smoking: Cigarette smoking decreased over this period as well.

We see that the prevalence of many of risk factors declined during the “Special Period.”  What were the affects on health?

  • “In subsequent years (1997–2002), rates of mortality from type 2 diabetes, coronary heart disease, and all causes dropped 51 percent, 35 percent, and 18 percent, respectively…No significant changes in total cancer mortality were observed, consistent with the current knowledge that obesity is not strongly associated with this condition.”

So poverty is the answer?  It turns out that all the news is not rosy:

  • All-cause mortality among persons over the age of 65 years increased 13 percent from 1989 to 1996, primarily because of excess deaths from infections (21). The secular decline in infant mortality was interrupted for 3 years, and the incidence of low birth weight increased from 7.3 percent to 9.0 percent between 1989 and 1993 (21). An epidemic of optical and peripheral neuropathy attributed in part to vitamin and protein deficiencies affected 50,000 people between 1992 and 1993.”

Can these results be extended to other countries? Cuba is a communist country where health is provided publicly.  The government can incur debt in order to provide medical care for its citizens.  In the U.S., increased poverty will likely make medical care less affordable–for those not on Medicaid–and thus health outcomes may suffer.  Further, long run economic decline will make the provision of even government-run high quality medical care unaffordable for a society.  A final critique is that although the data presented in the paper is suggestive, correlation does not imply causation.  One should always maintain a healthy skepticism regarding the conclusion of time-series correlation studies.While no economist would advocate for policy-makers to attempt to increase poverty, the Franco study may guide individuals into believing that “less is more,” at least when it comes to food intake and car usage.

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Implicit Tax: Medicare as a Secondary Payer

October 10, 2007 in Academic Articles, Medicaid/Medicare, Taxes | Permalink

“Medicare adopted its [Medicare as a Secondary Payer] MSP policy in 1982, effective January 1, 1983. This legislation states that for individuals working at firms with 20 or more employees, and otherwise eligible for Medicare benefits, Medicare serves as a secondary payer for health care expenses. The employer’s health insurance is the first payer. Because employer-sponsored health plans tend to be more comprehensive than Medicare, these workers are effectively foregoing their Medicare benefits by working. If these same individuals were not working, they would receive Medicare as their primary health insurance.”

A recent NBER working paper (“A Tax on Work for the Elderly: Medicare as a Secondary Payer“) claims that the MSP policy creates an implicit tax for elderly workers and thus creates disincentives to work. The authors calculate that the implicit tax is 15-20 percent at age 65 and increases to 45-70 percent by age 80. Making the Medicare the primary payer (MPP) will have two fiscal impacts. First, the cost of Medicare will increase. Since Medicare will be the primary payer, it will then be paying for more treatments. Secondly, enacting an MPP policy will decrease the implicit tax, increase the income and hours worked of elderly individuals and thus increase income tax receipts. The authors claim that since the second fiscal impact will dominate the first and thus recommend that an MPP system be implemented.

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