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Corresponding Author: Nikolaos Vrachnis, MD, National and Kapodistrian University of Athens Medical School, 76 Sofias, Athens 11528, Greece (nvrachnis @hotmail.com). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Pasternak B, Svanström H, Mølgaard-Nielsen D, Melbye M, Hviid A. Metoclopramide in pregnancy and risk of major congenital malformations and fetal death. JAMA. 2013;310(15):1601-1611. 2. Rowland M, Tozer TN. Clinical Pharmokinetics and Pharmacodynamics: Concepts and Applications. 4th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2010. 3. Tomson T, Battino D, Bonizzoni E, et al; EURAP Study Group. Dose-dependent risk of malformations with antiepileptic drugs: an analysis of data from the EURAP epilepsy and pregnancy registry. Lancet Neurol. 2011;10(7):609-617.

In Reply Dr Vrachnis and colleagues claim that our study does not account for dosage with sufficient detail. We do not agree. First, they state that dosage was not reported in the article. However, the number of dispensed doses was reported as a median of 40 doses (interquartile range, 30-40 doses). The number of dispensed doses corresponds to units of the drug, which in Denmark includes tablets containing 10 mg and suppositories containing 20 mg, 2 formulations of metoclopramide that are considered equipotent. Second, Vrachnis and colleagues suggest the need for doseresponse analyses. Given the narrow distribution of the number of doses received in our study, dose-response analyses are not feasible. The sensitivity analyses according to the number of received prescriptions (1 and 2 or more) were not intended as dose-response analyses. Rather, they were conducted on the assumption that women refilling prescriptions are more likely to have taken the drug.

Michael Kitchell, MD Author Affiliation: McFarland Clinic, Ames, Iowa. Corresponding Author: Michael Kitchell, MD, McFarland Clinic, 1215 Duff Ave, Ames, IA 50010 ([email protected]). Conflict of Interest Disclosures: The author has completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and reported receiving payment for serving on the Wellmark of Iowa Quality Oversight Committee. 1. Newhouse JP, Garber AM. Geographic variation in health care spending in the United States: insights from an Institute of Medicine report. JAMA. 2013;310(12):1227-1228. 2. Institute of Medicine. Variation in health care spending: target decision making, not geography. http://www.nap.edu/catalog.php?record_id=18393. Accessed November 8, 2013. 3. Medicare Payment Advisory Commission. Report to the Congress: Medicare and the health care delivery system. http://www.medpac.gov/documents/Jun13 _EntireReport.pdf. Accessed September 19, 2013.

Björn Pasternak, MD, PhD Henrik Svanström, MSc Anders Hviid, MSc, DrMedSci Author Affiliations: Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. Corresponding Author: Björn Pasternak, MD, PhD, Statens Serum Institut, Artillerivej 5, 2300 Copenhagen S, Denmark ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Geographic Differences in US Health Care Spending To the Editor In their Viewpoint regarding the Institute of Medicine (IOM) report on geographic variation in health care spending, Drs Newhouse and Garber1 indicated the spending data were adjusted for geographically different input prices. Use of a flawed input price–adjustment method leaves many of the IOM report’s conclusions in doubt. The input price adjustment did not use the actual dollars spent on physician work but used geographic practice cost index (GPCI) adjusters to change the spending figures to reflect local differences in input price.2 The problem is that the GPCI does not use real data on physician labor prices for determining the adjustment but in624

stead uses the wages of other professionals by region to set the price of physician labor. The GPCI adjustments have never used actual physician compensation figures, and there has never been proof of a relationship between the wages of other professionals and physicians by region. The Medicare Payment Advisory Commission has stated “the current GPCI is flawed in concept and implementation” and urged Congress to “direct the Secretary to develop an adjuster to replace it.”3 Even though the GPCI input price adjustment of Medicare data may be useful to determine differences in use, adjusting private insurance data are even more unreliable due in large part to the variation in physician supply and demand that drives even greater variation in the regional price of physician labor. Using the actual dollars paid to physicians rather than adjusting spending data using flawed input prices based on the wages of other professionals might lead to different conclusions.

In Reply Dr Kitchell provides us an opportunity to explain the methods used in the IOM report on geographic variation more fully. The report used Medicare’s GPCI to adjust physician spending for geographic variation in input prices. We know of no serious concerns about the measurement of the price of the nonphysician inputs in the GPCI, such as the wages of billing clerks. Questions about the GPCI center on the weight given to its physician work component and how well the geographic units it uses approximate local labor markets. Fortunately, the effects of inaccuracies in these 2 dimensions are necessarily limited. Physician work accounts for about half of physician costs (slightly more or less depending on the year), but by statute it is only given a weight of one-quarter of that amount in the actual index.1 Whether one-quarter is the proper weight does not much matter for our purposes because spending on physician and clinical services accounts for only 24% of total medical spending and 22% of Medicare spending.2 Consequently, any misweighting of the work component of the GPCI affects at most about one-eighth of the input price index used to adjust both

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total medical spending and Medicare spending across geographic areas (1/8 ≈ 0.24 × 0.5). In regard to geographic areas used by GPCIs not corresponding to actual labor market areas, errors may be largely offset at the hospital referral region level (the main unit of analysis used in the report) because hospital referral regions are typically aggregates of several local labor markets and GPCI areas. For these reasons, the plausible magnitude of errors in the GPCI would almost certainly not change any of the report’s principal conclusions, which include: (1) there is variation at every level of geography and so a value index to adjust Medicare spending is not advisable; (2) the bulk of geographic variation in Medicare spending comes from postacute services (for which the GPCI has little applicability); and (3) most of the geographic variation in spending by the commercially insured comes from variation in markups of output prices over input prices. Kitchell’s proposed alternative to the GPCI (the actual physician fee) does not measure input prices; rather it measures output prices. Thus, it cannot serve as an input price index, and it would not allow one to determine how much of the geographic variation in spending by the commercially insured was attributable to variation in input prices and how much to markups in output prices above input prices. The current GPCI not only has the advantage of familiarity but it is the only extant input price index for physician services with sufficient geographic detail for the questions the report sought to answer. Although there was thus no real alternative to the report’s use of the GPCI, we believe its flaws are quantitatively unimportant for the purpose for which the report used it. Joseph P. Newhouse, PhD Alan M. Garber, MD, PhD Author Affiliations: Harvard University, Cambridge, Massachusetts. Corresponding Author: Joseph P. Newhouse, PhD, Harvard University, 180 Longwood Ave, Boston, MA 02115 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Newhouse reported serving as a board member for and holding stock in Aetna. Dr Garber reported serving as a director and holding stock in Exelixis; and holding equity in and advising Castlight Health. Both authors reported receiving travel accommodations from the Institute of Medicine. 1. Institute of Medicine. Geographic adjustment in Medicare payment, phase I: improving accuracy. http://www.iom.edu/Reports/2011/Geographic -Adjustment-in-Medicare-Payment-Phase-I-Improving-Accuracy.aspx. Accessed October 30, 2013. 2. Centers for Medicare and Medicaid Services. National health expenditures by type and source of funds. http://cms.hhs.gov/Research-Statistics-Data-and -Systems/Statistics-Trends-and-Reports/NationalHealthExpendData /NationalHealthAccountsHistorical.html. Accessed October 30, 2013.

CORRECTION Author Name Misspelled and Data and Wording Errors: In the Original Investigation entitled “Acute Skeletal Muscle Wasting in Critical Illness” published in the October 16, 2013, issue of JAMA (2013;310[15]:1591-1600. doi:10.1001/jama .2013.278481), an author name was misspelled and there were several data and wording errors. In the byline, the eighth author should have been listed as “Rahul Phadke, FRCPath”; Dr Phadke’s affiliation should have been “Division of Neuropathology, National Hospital for Neurology and Neurosurgery and Department of Molecular Neuroscience, UCL Institute of Neurology, London, England.” In the Results section of the abstract, the first sentence should be “There were significant reductions in the rectus femoris CSA observed at day 10 (−17.7% [95% CI, −20.9% to −4.8%]; P < .001).” The third sentence of this section should be “Decrease in the rectus femoris CSA was greater in patients who experienced multiorgan failure compared with single organ failure by day 7 (−15.7% [95% CI, −19.1% to −12.4%] vs −3.0% [95% CI, −10.5% to 4.6%], P < .001), even by day 3 (−8.7% [95% CI, −13.7% to −3.6%] vs −1.8% [95% CI, −7.3% to 3.8%], respectively; P = .03).” In the Results section of the text, under the subheading “Changes in Markers of Muscle Mass,” the first sentence should be “In the group overall, rectus femoris crosssectional area decreased significantly from days 1 to 7 (−12.5% [95% CI, −15.8% to −9.1%]; P = .002), and continued to decrease to day 10 (−17.7% [95% CI, −20.9% to −4.8%]; P < .001).” In the last section of the Results section, under the subheading “Clinical Correlates, Patient Stratification, and Risk Factors for Muscle Wasting,” the second sentence of the first full paragraph should be “Change in rectus femoris cross-sectional area differed between patients with multiorgan failure vs single organ failure (day 3: −8.7% [95% CI, −13.7% to −3.6%] vs −1.8% [95% CI, −7.3% to 3.8%], respectively, P = .03; day 7: −15.7% [95% CI, −19.1% to −12.4%] vs −3.0% [95% CI, −10.5% to 4.6%], P < .001).” The next sentence of the paragraph should be “Change in rectus femoris cross-sectional area was greater in those with 4 or more failed organs (−20.3%; 95% CI, −27.6% to −13.0%) than in those with 2 to 3 failed organs (−13.9%; 95% CI, −19.6% to −10.1%) (P < .001).” In the figure legends for Figures 2, 3, and 5, the data are expressed as means instead of medians. This article was corrected online.

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Geographic differences in US health care spending.

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