For each of 6 diagnoses at admission—acute myocardial infarction, congestive heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and pneumonia—patient admission to higher-spending hospitals was associated with lower risk-adjusted inpatient mortality. During 1999 to 2003, for example, patients admitted with acute myocardial infarction to California hospitals in the highest quintile of hospital spending had lower inpatient mortality than did those admitted to hospitals in the lowest quintile (odds ratio, 0.862 [95% CI, 0.742 to 0.983]). Predicted inpatient deaths would increase by 1831 if all patients admitted with acute myocardial infarction were cared for in hospitals in the lowest quintile of spending rather than the highest. The association between hospital spending and inpatient mortality did not vary by region or hospital size.

Source:

• John A. Romley, Anupam B. Jena, Dana P. Goldman; Hospital Spending and Inpatient Mortality: Evidence From CaliforniaAn Observational Study. Annals of Internal Medicine. 2011 Feb;154(3):160-167.

Beginning in the fourth quarter of 2008, [CMS] ceased to reimburse for costs due to selected preventable adverse events, including patient safety indicator 5 (PSI-5) (leaving a foreign object in the body during surgery) and PSI-7 (central line–associated bloodstream infection [CLABSI], unless present on admission). Data from the National Inpatient Sample indicate no change in the PSI-5 rate over the years 1998 through most of 2008 with an increase and plateau of the PSI-7 rate over the same period. However, when reimbursement for these preventable adverse effects stopped in 2008, the reported PSI-5 and PSI-7 events decreased by 50% in a single quarter. By contrast, a study based on clinical laboratory data finds no evidence that the nonpayment policy affected the true CLABSI rate. Accordingly, the true rate of these important, preventable adverse events can no longer be reliably monitored using administrative data.

Other Medicare policy decisions may also affect the quality of administrative data. If Medicare institutes more bundled payments, for instance, then hospitals and other providers will likely decrease the amount of detail they include on claims for items that do not affect their reimbursement. Thus, increased bundling will likely omit clinically relevant information.

Source:

• Steven A. Farmer, Bernard Black, Robert O. Bonow. Tension Between Quality Measurement, Public Quality Reporting, and Pay for Performance. JAMA, January 23/30, 2013—Vol 309, No. 4

Fifty-seven percent of U.S. workers surveyed reported less than $25,000 in total household savings and investments excluding their homes, according to a report to be released Tuesday by the Employee Benefit Research Institute. Only 49% reported having so little money saved in 2008.

As the graphic below shows, more and more people do not believe they have enough money saved for retirement.

In the past, workers could count on employers defined benefit plans to provide them with security in retirement. This is no longer the case. “The portion of private-sector U.S. workers covered only by so-called defined-benefit plans fell to 3% in 2011 from 28% in 1979, according to U.S. Department of Labor data compiled by EBRI.”

Researchers may measure the treatment effect a variety of ways.  Sensible research questions include:

1. What is the average effect of the treatment across all individuals?
2. What is the average treatment effect only among those who received treatment?
3. What is the difference in the treatment effect between those who are more likely to receive treatment compare to others?
4. What is the difference in the treatment effect between those who are marginally more likely to receive treatment compare to others?

Question 2 differs from 1 when the people who receive treatment are more (or less) likely to benefit from it. For instance, one could consider a case where patients either receive surgery or watchful waiting. If patients who receive surgery are exactly those who are more likely to benefit, one many be interested in answering question 2 rather than question 1, since including the people who don’t get surgery (since they don’t benefit from it) may dilute the measured effect of interest.

Questions 3 and 4 are similar, except that question 3 examines the difference in outcomes for individuals with different average treatment probability whereas question 4 examines the derivative.  More specifically, question 4 aims to answer what the is the average effect for people who are just indifferent between receiving treatment or not at a given value of the instrument.

One can answer these four questions using the following four parameters:

• Average Treatment Effect (ATE)
• Expected effect of treatment on the treated (TT)
• Local Average Treatment Effect (LATE)
• Marginal Treatment Effect (MTE) or Local Instrumental Variables (LIV)

Each of these is defined below from Heckman and Vytlacil (1999).

where Δ is the change in outcomes, X is the covariates of interest.

In this equation, and D is an indicator for whether the individual received a treatment. One can also incorporate a propensity score P(Z) based on a set of instruments Z.

For a given propensity score, one can also calculate the treatment effect on the treated as:

When moving to examining how a change in the probability of treatment effects outcomes, one must calculate either LATE or LIV.

LATE gives the average treatment effect for those who would have been effected by the instrument. Consider the case of examining the impact of military service on earnings where draft lottery status is an instrument for military service. This IV approach identify the effect of military service on individuals who would always join the military or those who would never join the military. (it is assumed that there are no “defiers” that join the military when they are not drafted and do not join the military when they are drafted). Thus, LATE only identifies the effect of military service on “compliers”, those would would join the army if drafted, but would not join if not drafted.  The IV strategy only identifies ATE if all individuals are compliers, in which case LATE and ATE are equivalent.

LIV is the limit form of the LATE parameter and is defined as follows:

As Heckman, Urzua and Vytlacil explain, to compute this value, the probability of being treated (conditional on covariates X) must fall between 0 and 1 (i.e., 0<P(D=1|X) <1).  In other words, for each set of individuals with covariates X, there must exist both a treatment and comparison group.

Heckman and Vytlacil (1999) show that different treatment effect parameters can be seen as averaged versions of the LIV parameter that differ according to how they weight the LIV parameter.

ATE weights all LIV parameters equally. LATE gives equal weight to the LIV parameters within a given interval. TT gives a large weight to those LIV parameters corresponding to the treatment effect for individuals who are the most inclined to participate in the program.

For more information on local instrumental variables (LIV) see my previous post.

Source:

• Basu A, Heckman JJ, Navarro-Lozano S, Urzua S. Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients. Health Econ. 2007 Nov;16(11):1133-57.
• Heckman, J.J. and E.J. Vytlacil, Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proc Natl Acad Sci U S A, 1999. 96(8): p. 4730-4.
• Heckman, J., J., S. Urzua, and E. Vytlacil, Understanding Instrumental Variables in Models with Essential Heterogeneity. The Review of Economics and Statistics, 2006. 88(3): p. 389-432.
• Guido W. Imbens and Joshua D. Angrist. Identification and Estimation of Local Average Treatment Effects. Econometrica , Vol. 62, No. 2 (Mar., 1994), pp. 467-475.
• Joshua D. Angrist. Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records. The American Economic Review , Vol. 80, No. 3 (Jun., 1990), pp. 313-336

Compared to the United States, Europe more formally and consistently considers value to determine which technologies to cover and at what price, especially for complex, costly devices. Both the United States and Europe have introduced policies to provide temporary coverage and reimbursement for promising technologies while additional evidence of value is generated.

Europe

Europe previously focused mostly on the cost of devices, but now looks at value as part of its pricing determinations.

In Europe, the Conformité Européenne means that if a device is deemed safe functions according to the intended purpose by one country, it can be sold in any EU country. “In practice, however, institutional arrangements for financing differ among countries,which can result in divergent coverage, reimbursement, and pricing decisions for a particular device.” Most European countries use a health technology assessment to determine if their health plans will cover a device and if so at what price.

The European and U.S. health care systems only update prices occasionally and thus when new products hit the market, special procedures are used. Typically, policymakers create a new diagnosis-related group (DRG) or an increase in the reimbursement price or create a separate CPT code to temporarily price the device.

U.S. Public Payers

In the U.S., CMS will only cover new devices is they are approved by the FDA. Because most devices are used to treat existing conditions covered by the inpatient prosepective payment system’s (IPPS) DRG payments or the outpatient prospective payment system’s (OPPS) ambulatory payment classifications (APCs).

For some new devices that do not fall into existing DRGs or APCs, CMS conducts a national coverage determination (NCD) using research form CMS, MedCAC, AHRQ, physicians, the manufacturer and other sources. CMS can also use local coverage determinations (LCD) where fiscial intermediaries and carriers in certain regions can approve a device. If coverage is approved, CMS will often grant separate “add-on payments” (or “pass through payments” in the outpatient setting) to account for the high cost of new technology relative to the base diagnosis-related group payment and to encourage providers to adopt the technology. “Fewer
than ten technologies have been approved for add-on payments, while pass-through payments have been made for more than a hundred different device categories.”

U.S. Private Payers

Private payers in the U.S. often use Medicare NCDs as part of their own coverage decisions but not always.

WellPoint draws on comparative effectiveness evidence and on input from panels of medical experts to assign existing and new treatments to one of four value tiers. Both the Blue Cross Blue Shield Association and Kaiser Permanente have established institutional policies and dedicated funding for in-house or external programs that generate evidence to support coverage determinations and clinical practice guidelines.

Private insurers, however, rarely pay for devices directly, rather they are bundled into payments to hospitals and physicians for specific services.

Post Market Surveillance Trends

Some European countries—including Germany, Italy, Sweden, and the United Kingdom—have introduced registries, particularly in orthopedics and cardiology, to collect postmarket data.

Source

• CORINNA SORENSON, MICHAEL DRUMMOND, AND LAWTON R. BURNS. Evolving Reimbursement And Pricing Policies For Devices In Europe And The United States Should Encourage Greater Value.  HEALTH AFFAIRS 32, NO. 4 (2013), doi: 10.1377/hlthaff.2012.1210

In contrast with the United Kingdom and the United States, Singapore has sought to minimize LTC costs by adopting an LTC policy that promotes “the family as the first line of care and support for persons with disabilities”, a position in accord with the widely accepted Confucian tradition of filial piety. Only in instances when family support is insufficient may qualifying, means-tested elders receive subsidies from the government either to employ a foreign domestic worker (FDW) (equivalent to a live-in maid in the United States and Europe) or pay for the use of home- and community-based services (HCBS) or a nursing home.

Correspondingly, there are 59 nursing homes and a total of just 9,300 nursing home beds in Singapore, all of which are occupied.

A paper by Ansah et al (2013) uses a simulation methodology and estimates that by 2030, average family elder care hours per week are projected to increase by 41 percent from 29 to 41 hours.

Source:

• Ansah, J. P., Matchar, D. B., Love, S. R., Malhotra, R., Do, Y. K., Chan, A. and Eberlein, R. (2013), Simulating the Impact of Long-Term Care Policy on Family Eldercare Hours. Health Services Research. doi: 10.1111/1475-6773.12030

• How to lose $8.4 billion.
• Healthcare SEC?
• Cardboard cribs.
• Another P4P failure?
• According to coach, coaches don’t matter.

Ultimately, the solution to the drug problem might be the solution to the problem of life, which is how to navigate our time here with minimal suffering. Unfortunately, the policy that offers that solution will be not a drug policy but an existential one, and it remains as elusive as ever.

Graeme Wood in The New Republic.