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.
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.
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
(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
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.
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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