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Unformatted text preview: Chapter 12 References AghaKouchak et al. 2014. citing Gräler, B., M.J. van den Berg, S. Vandenberghe, A. Petroselli, S. Grimaldi, B.D. Baets, and N.E.C. Verhoest. 2013. Multivariate return periods in hydrology: A critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Science 17(4):1281–1296. doi:10.5194/hess-17-1281-2013. Available at: . (Accessed: February 26, 2018). Agnolucci, P. 2007. Prospects of Fuel Cell Auxiliary Power Units in the Civil Markets. International Journal of Hydrogen Energy 32:4306–4318. doi:10.1016/j.ijhydene.2007.05.017. Ahmadi, L., A. Yip, M. Fowler, S.B. Young, and R.A. Fraser. 2014. Environmental feasibility of re-use of electric vehicle batteries. Sustainable Energy Technologies Assessments 6:64-74. doi:10.1016/j.seta.2014.01.006. Aksoy, M. 1989. Hematotoxicity and Carcinogenicity of Benzene. Environmental Health Perspectives 82:193–197. Available at: . (Accessed: February 26, 2018). Alexeef, S.E., B.A. Coull, A. Gryparis, H. Suh, D. Sparrow, P.S. Vokonas, and J. Schwartz. 2011. Mediumterm Exposure to Traffic-related Air pollution and Markers of Inflammation and Endothelial Function. Environmental Health Perspectives 119(4):481–486. doi:10.1289/ehp.1002560. Available at: . (Accessed: March 3, 2018). Altieri, A.H. and K.B. Gedan. 2015. Climate Change and Dead Zones. Global Change Biology 21:13951406. doi:10.1111/gcb.12754. Altizer, S., R.S. Ostfeld, P.T. Johnson, S. Kutz, and C.D. Harvell. 2013. Climate Change and Infectious Diseases: From Evidence to a Predictive Framework. Science 341(6145):514–519. doi:10.1126/science.1239401. Alvarez, R.A., S.W. Pacala, J.J. Winebrake, W.L. Chameides, and S.P. Hamburg. 2012. Greater Focus Needed on Methane Leakage from Natural Gas Infrastructure. Proceedings of the National Academy of Sciences of the United States 109(17):6435–6440. doi:10.1073/pnas.1202407109. Available at: . (Accessed: February 26, 2018). Alvarez, R.A., Zavala-Araiza, D., Lyon, D.R., Allen, D.T., Barkley, Z.R., Brandt, A.R., Davis, K.J., Herndon, S.C., Jacob, D.J., Karion, A. and Kort, E.A., Lamb, B.K., Lauvaux, T., Maasakkers, J.D., Marchese, A.J., Omara, M., Pacala, S.W., Peischl, J., Robinson, A.L., Shepson, P.B., Sweeney, C., Townsend-Small, A., Wofsy, S.C., and S.P. Hamburg. 2018. Assessment of methane emissions from the US oil and gas supply chain. Science 361(6398):7204. doi: 10.1126/science.aar7204. American National Standards Institute. 2005. Quantities and Procedures for Description and Measurement of Environmental Sound - Part 4: Noise Assessment and Prediction of Long-term Community Response. ANSI S12.9-2005/Part 4. Acoustical Society of Amerca: Melville, NY. Available at: . (Accessed: February 26, 2018). Anderson, C.M., M. Mayes, and R. LaBelle. 2012. Update of Occurrence Rates for Offshore Oil Spills. BOEM 2012-069. OCS Report. June, 2012. Department of the Interior, Bureau of Ocean Energy 12-2 TABLE Distribution of population by number of MA parent organizations operating in the county, October 2018 13–6 Number of MA parent organizations in county As share of total Medicare population As share of MA enrollment None 1% 1 2 1 2 5 3 3 9 6 4 11 10 5 or more 72 80 Note: 0.1% MA (Medicare Advantage). Excludes plans offered only to employer group–sponsored retirees. Numbers may not sum due to rounding. The 0.1 percent of MA enrollees residing in areas with no MA organizations are “out-of-area” enrollees whose recorded address is outside of the designated service area of their plan. Source: MedPAC analysis of CMS enrollment reports. nonmetropolitan areas, the top 2 organizations accounted for over half the enrollment (55 percent of the 2.5 million MA enrollees residing in these areas, compared with 54 percent in 2017). Another way of looking at the market structure and level of competition in the MA program is to determine the number of parent organizations offering MA options in markets across the country. In 2018, 92 percent of Medicare beneficiaries resided in a county where at least three companies offered MA plans to individual Medicare beneficiaries, compared with 87 percent in 2017 (Table 13-6). Thus, although the MA market is relatively concentrated by some measures, most beneficiaries reside in geographic areas where multiple companies offer MA options. Among beneficiaries residing in a county with at least three sponsors offering MA products, 30 percent live in a county in which one sponsor has 50 percent or more of the county’s MA enrollment. Looking at access based on the profit status of plans, 65 percent of Medicare beneficiaries reside in a county where a nonprofit plan is available, compared with 99 percent for for-profit plans. Seventy-three percent of MA enrollment in 2018 is in for-profit MA plans, and the top three sponsors have 72 percent of the for-profit MA enrollment. For the 27 percent of MA enrollment in nonprofit entities, 50 percent of enrollees are in the top three sponsors’ plans. Each of the top 3 for-profit sponsors have offerings in 40 or more states for individual (non-employer-groupsponsored) Medicare beneficiaries, and all 3 are often present in a given market. Two of the top three nonprofit sponsors operate in only one state (for individual Medicare beneficiaries), while the third is available in eight states. Two of the three organizations have partially overlapping service areas and compete in the same markets. The majority of Medicare beneficiaries (58 percent) living in metropolitan areas reside in counties where all three of the top for-profit entities have MA plans, which is true for only 21 percent of residents of nonmetropolitan areas. Medicare Advantage risk adjustment and coding intensity Medicare payments to MA plans are adjusted to account for differences in beneficiary medical costs through the CMS hierarchical condition category (CMS–HCC) model. The model uses demographic information (e.g., age, sex, Medicaid enrollment, and disability status) and certain diagnoses grouped into HCCs to calculate a risk score for each enrollee. Higher risk scores generate higher payments for beneficiaries with higher expected expenditures, and the reverse is true for lower risk scores. CMS designed this risk adjustment model to maximize its ability to predict annual medical expenditures for Medicare beneficiaries, with some constraints. Therefore, in developing the model, Report to the Congress: Medicare Payment Policy  |  March 2019 359 Chapter 12 References Management and Bureau of Safety and Environmental Enforcement. Herndon, VA. Available at: nt/Oil_Spill_Modeling/AndersonMayesLabelle2012.pdf. (Accessed: April 5, 2018). Andreae, M.O. and A. Gelencsér. 2006. Black Carbon or Brown Carbon? The Nature of Light-absorbing Carbonaceous Aerosols. Atmospheric Chemistry and Physics 6:3131–3148. doi:10.5194/acp-6-31312006. Available at: . (Accessed: February 26, 2018). ANL (Argonne National Laboratory). 2016. GREET Model. Argonne National Laboratory. Available at: . (Accessed: November 2016). ANL. 2017. The Greenhouse Gases, Regulated Emissions and Energy use in Transportation (GREET) Model 2017. October 2017. Available at: . (Accessed: May 29, 2018). Appelman, L.M., R.A. Woutersen, and V.J. Feron. 1982. Inhalation Toxicity of Acetaldehyde in Rats. I. Acute and Subacute Studies. Toxicology 23(4):293–307. doi:10.1016/0300-483X(82)90068-3. Appelman, L.M., R.A. Woutersen, V.J. Feron, R.N. Hooftman, and W.R. Notten. 1986. Effect of Variable Versus Fixed Exposure Levels on the Toxicity of Acetaldehyde in Rats. Journal of Applied Toxicology 6(5):331–336. doi:10.1002/jat.2550060506. Archsmith, J., A. Kendall, and D. Rapson. 2015. From cradle to junkyard: assessing the life cycle greenhouse gas benefits of electric vehicles. Research in Transportation Economics 52:72-90. doi:10.1016/j.retrec.2015.10.007. Arnell, N.W. and B. Lloyd-Hughes. 2014. The Global-scale Impacts of Climate Change on Water Resources and Flooding under New Climate and Socio-economic Scenarios. Climatic Change 122:127–140. doi:10.1007/s10584-013-0948-4. Available at: . (Accessed: February 26, 2018). Asthana, A. and M. Taylor. 2017. Britain to Ban Sale of All Diesel and Petrol Cars and Vans from 2040. The Guardian. Last revised: July 25, 2017. Available at: . (Accessed: February 15, 2018). Atabani, A.E., I.A. Badruddin, S. Mekhilef, and A.S. Silitonga. 2011. A Review on Global Fuel Economy Standards, Labels and Technologies in the Transportation Sector. Renewable and Sustainable Energy Reviews 15(9):4586–4610. doi:10.1016/j.rser.2011.07.092. ATSDR (Agency for Toxic Substances and Disease Registry). 1995. Toxicological Profile for Polycyclic Aromatic Hydrocarbons (PAHs). August, 1995. U.S Department of Health and Human Services, Agency for Toxic Substances and Disease Registry. Atlanta, GA. Available at: . (Accessed: February 26, 2018). ATSDR. 1999. Toxicological Profile for Formaldehyde. July, 1999. U.S Department of Health and Human Service, Agency for Toxic Substances and Disease Registry. Atlanta, GA. Available at: . (Accessed: February 26, 2018). 12-3 CMS used statistical analyses to select certain HCCs for inclusion in the model based on each HCC’s ability to predict annual Medicare expenditures, ensuring that the diagnostic categories included in the model were clinically meaningful and specific enough to minimize opportunities for gaming or discretionary coding (Pope et al. 2004). CMS applied additional criteria to ensure the validity and reliability of the diagnostic data used in the model and to determine payment to MA plans: (1) diagnoses must appear on a claim from a hospital inpatient stay, a hospital outpatient visit, or a face-to-face visit with a physician or other health care professional and (2) diagnoses must be supported by evidence in the patient’s medical record.3 Diagnostic data in the CMS–HCC model are used prospectively, meaning that diagnoses collected during one calendar year are used to predict Medicare costs for the following calendar year. A particular diagnosis code needs to be submitted only once during the data collection year for the related HCC to be counted in an enrollee’s risk score in the following payment year. Multiple submissions of the same diagnosis code and submissions of different diagnosis codes that are grouped in the same HCC do not affect an enrollee’s risk score. Each demographic and HCC component in the risk adjustment model has a coefficient that represents the expected medical expenditures associated with that component. These coefficients are estimated based on FFS Medicare claims data such that all Medicare spending in a year is distributed among the model components. Medicare payment for a particular MA enrollee is approximately equal to the sum of the dollar-value coefficients for all components identified for that enrollee.4 In practice, the actual dollar amount a plan will receive for newly identifying a particular HCC for an enrollee depends on several additional factors, but for a simplified example of how coding additional HCCs increases payment to a plan, we consider amounts received by an MA plan that are representative of average FFS Medicare spending. In this example, the annual Medicare payment to the MA organization in 2018 for an 84-year-old male who was not eligible for Medicaid (demographic component valued at $5,707) with diabetes without complication (HCC 19, valued at $1,058) would have been $6,765, the sum of the two model components. Documenting each additional HCC for that enrollee can significantly increase the Medicare payment. If the same 84-year-old male with diabetes were also found to have vascular disease (HCC 108, valued at $3,031), the Medicare payment to the MA 360 The Medicare Advantage program: Status report organization would increase from $6,765 to $9,796. The payment per MA enrollee for most HCCs when identified is between $1,000 and $5,000, although some HCCs increase payment by $10,000 or more. In addition to the direct increase in payment rates, plans benefit from coding more comprehensively by gaining advantage through the determination of extra benefits. Plans that can offer a higher value of extra benefits may attract more new enrollees. How coding differences affect the determination of extra benefits is a function of the bidding rules. There are two steps in the bidding process that involve risk adjustment and the determination of extra benefits. In the first step, a plan states its revenue need—its bid—for providing the Medicare Part A and Part B benefit, based on its expected enrolled population, and determines a risk score for the expected population. The second step compares the bid with a benchmark, which is adjusted by the risk score for the plan’s expected population so that the comparison is based on a population with equivalent health status. If the bid is higher than the risk-adjusted benchmark, beneficiaries pay the difference in the form of a premium.5 When the bid is below the risk-adjusted benchmark, the plan receives part of the difference as a rebate that is used to provide extra benefits to beneficiaries. The size of the rebate (or the value of extra benefits) is a share of the difference between the bid and risk-adjusted benchmark.6 Plans that put more effort into documenting all diagnosis codes, increasing their average risk score relative to other plans, can affect the process by inflating the risk-adjusted benchmark used to determine the size of the rebate when compared with the bid. Table 13-7 illustrates this effect, with all three plans having the same cost of care for their set of enrollees, at $900 per month. Although all three plans have actual costs of $900 per month, Plans A and Z have an expected risk score below 1.0 (at 0.97), and Plan B has an expected risk score of 1.03. All three plans have bids below the risk-adjusted benchmark and must provide rebates. Because Plan B has a higher risk score, its rebate is larger and it can offer enrollees more benefits—$37 per month more in extra benefits ($53 minus $15). If Plan B has inflated its risk score through greater diagnostic coding effort and its risk score otherwise would be the same as that of Plans A and Z, Plan B will have an unfair competitive advantage. The higher risk score also gives Plan B, which has only 3.5 stars, an advantage over bonuslevel Plan Z; Plan B has a higher total rebate amount—$7 ...
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