2325_sejda-DYJ.pdf - Chapter 12 References Available at http/www.cdc.gov/mmwr/preview/mmwrhtml/su6203a8.htm(Accessed February 26 2018 Bohra-Mishra P M

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Unformatted text preview: Chapter 12 References Available at: . (Accessed: February 26, 2018). Bohra-Mishra, P., M. Oppenheimer, R. Cai, S. Feng, and R. Licker. 2017. Climate variability and migration in the Philippines. Population and Environment 38(3):286-308. doi:10.1007/s11111-016-0263-x. Available at: . (Accessed: February 26, 2018). Boland, C., R. DeKleine, A. Moorthy, G. Keoleian, H.C. Kim, E. Lee, and T.J. Wallington. 2014. A Life Cycle Assessment of Natural Fiber Reinforced Composites in Automotive Applications. SAE Technical Paper 2014-01-1959. doi:10.4271/2014-01-1959. Boland. S. and Unnasch. S. 2014. Carbon Intensity of Marginal Petroleum and Corn Ethanol Fuels. Life Cycle Associates Report LCA.6075.83.2014, Prepared for Renewable Fuels Association. Available at: . (Accessed: February 26, 2018). Boothe, V.L. and D.G. Shendell. 2008. Potential Health Effects Associated with Residential Proximity to Freeways and Primary Roads: Review of Scientific Literature, 1999–2006. Journal of Environmental Health 70:33–41. Boothe, VL., T.K. Boehmer, A.M. Wendel, and F.Y. Yip. 2014. Residential Traffic Exposure and Childhood Leukemia: A Systematic Review and Meta-analysis. American Journal of Preventive Medicine 46(4):413–422. doi:10.1016/j.amepre.2013.11.004. Borasin, S., S. Foster, K. Jobarteh, N. Link, J. Miranda, E. Pomeranse, J. Rabke-Verani, D. Reyes, J. Selber, S. Sodha, and P. Somaia. 2002. Oil: A Life Cycle Analysis of its Health and Environmental Impacts. [Epstein, P.R. and J. Selber (Eds.)]. Prepared by: Harvard University, Center for Health and the Global Environment: Cambridge, MA. Available at: . (Accessed: February 26, 2018). Bouchama, A., M. Dehbi, G. Mohamed, F. Matthies, M. Shoukri, and B. Menne. 2007. Prognostic Factors in Heat Wave Related Deaths: A Meta-analysis. Archives of Internal Medicine 167:2170–2176. doi:10.1001/archinte.167.20.ira70009. Boumans, R.J.M., D.L. Phillips, W. Victery, and T.D. Fontaine. 2014. Developing a Model for Effects of Climate Change on Human Health and Health-environment Interactions: Heat Stress in Austin, Texas. Urban Climate 8:78–99. doi:10.1016/j.uclim.2014.03.001. Boustani, A., S. Sahni, T. Gutowski, and S. Graves. 2010. Tire Remanufacturing and Energy Savings. MITEI-1-h-2010. Prepared by the Environmentally Benign Manufacturing Laboratory, Sloan School of Management, Massachusetts Institute of Technology. Available at: . (Accessed: February 26, 2018). Bowles, A.E. 1995. Responses of Wildlife to Noise or Wildlife and Recreationists: Coexistence through Management and Research. Washington, D.C. [Knight, R.L. and K.J. Gutzwiller (Eds.)]. Island Press: Washington. pp. 109–156. 12-6 Policies to address the impact of coding differences Impact of coding differences on payment to MA plans A series of congressional mandates have required CMS to reduce MA risk scores as a way of addressing the impact of coding differences. Because of the mandates, CMS reduced MA risk scores by 3.41 percent in each year from 2010 through 2013. Starting in 2014, the mandates specified a minimum reduction of about 4.9 percent, which increased gradually to about 5.9 percent in 2018, where it will remain until CMS estimates a risk adjustment model using MA cost and use data. CMS reduced MA risk scores by the minimum amount required by law for 2014 through 2018, although larger reductions would have been allowed. To assess the overall impact of coding differences on payments to MA plans for a given year, we built retrospective cohorts of beneficiaries enrolled in either FFS or MA for all of 2017. We tracked each beneficiary backward for as long as they were continuously enrolled in the same program (FFS or MA) or as far back as 2007. Our analysis calculates differences in risk score growth by comparing FFS and MA cohorts with the same years of enrollment (e.g., 2007 through 2017, 2008 through 2017, etc.), adjusting for differences in age and sex. CMS has taken an additional step to help control the increased coding intensity in MA by phasing in a new CMS–HCC model that removes some diagnoses suspected of being more aggressively coded by MA plans (e.g., lower severity kidney disease and polyneuropathy). Our analysis suggests that the new CMS–HCC model makes MA risk scores more similar to FFS scores by reducing them by 2 percent to 2.5 percent relative to the old model. The new model was phased in during 2014 and 2015, and MA payments were based entirely on the new model in 2016. Before 2017, the HCC model accounted for dual enrollment in Medicare and Medicaid with a set of variables that increased payment for Medicaid enrollees. This approach treated MA enrollees with partial Medicaid enrollment and MA enrollees with full Medicaid enrollment as a single group; however, enrollees with full Medicaid benefits have Medicare spending that is significantly higher than enrollees with partial Medicaid benefits. As a result, risk scores under the old model were systematically too low for full dual enrollees and too high for partial dual enrollees.8 In addition to the inaccuracy in individual risk scores, partial dual enrollees make up a larger share of dual enrollees in MA than in FFS Medicare, causing the overall risk scores for MA enrollees who are enrolled in Medicaid to be inflated under the old model. For 2017, CMS began differentiating between MA enrollees with full Medicaid and partial Medicaid enrollment using separate models that more accurately determined risk scores for partial benefit and full benefit Medicaid enrollees.9 We found that the 2017 model reduced MA risk scores by almost 1 percent by accurately determining risk scores for subgroups of beneficiaries, particularly partial dual and full dual enrollees. Figure 13-4 (p. 364) shows the impact of differences in coding intensity on MA risk scores relative to FFS for payment years 2013 through 2017 and the amount by which CMS reduced MA risk scores for the coding intensity adjustment in each year. The difference between the lines shows the portion of coding intensity impact that was not accounted for by payment policies and resulted in the additional Medicare spending for beneficiaries enrolled in MA relative to the amount Medicare would have spent if the same beneficiaries had been enrolled in FFS Medicare. Three different versions of the CMS–HCC risk model were used for payment over this period. A blend of two of these model versions was used for payment in 2014 and 2015. The impact of coding intensity on MA risk scores changed over this period, largely because of three factors: changes to the risk score model used for payment, changes in MA risk score growth relative to FFS risk score growth, and the addition of encounter data as a source of diagnostic information. Changes in the risk model Our analysis has found that newer versions of the CMS– HCC model have been less susceptible to diagnostic coding differences between MA and FFS. Figure 13-4 (p. 364) shows that the version phased in over 2014 to 2016, removing specific diagnoses with large differences in MA and FFS coding rates, reduced the impact of coding differences by 2 percent to 2.5 percent. The version introduced in 2017, adding separate aged/disabled and Medicaid enrollment status segments, reduced the impact of coding differences by almost 1 percent. Relative risk score growth rates Between 2013 and 2015, our analysis shows that MA risk score growth outpaced FFS risk score growth by 1 percent Report to the Congress: Medicare Payment Policy  |  March 2019 363 Chapter 12 References Bradford, M.A., Wieder, W.R., Bonan, G.B, Fierer, N., Raymond, P.A., and Crowther, T.W. 2016. Managing uncertainty in soil carbon feedbacks to climate change. Nature Climate Change. 6, 751758. doi:10.1038/nclimate3071. (Accessed: Jan 26, 2017). Brandt, A.R., G.A. Heath, E.A. Kort, F. O’Sullivan, G. Pétron, S.M. Jordaan, P. Tans, J. Wilcox, A.M. Gopstein, D. Arent, S. Wofsy, N.J. Brown, R. Bradley, G.D. Stucky, D. Eardley, and R. Harriss. 2014. Methane Leaks from North American Natural Gas Systems. Science 343(6172):733–735. doi:10.1126/science.1247045. Available at: . (Accessed: June 17, 2016). Brandt, A.R., Y. Sun, S. Bharadwaj, D. Livingston, E. Tan, and D. Gordon. 2015. Energy Return on Investment (EROI) for forty global oilfields using a detailed engineering-based model of oil production. PloS one 10(12):e0144141. doi:10.1371/journal.pone.0144141. Available at: . (Accessed: February 26, 2018). Brodrick, C., T.E. Lipman, M. Farshchi, N.P. Lutsey, H.A. Dwyer, D. Sperling, S.W. Gouse III, D.B. Harris, and F.G. King. 2002. Evaluation of Fuel Cell Auxiliary Power Units for Heavy-Duty Diesel Trucks. Transportation Research Part D 7:303–315. Available at: EN=88995268. (Accessed: February 26, 2018). Brown, C.L., S.E. Reed, M.S. Dietz, and K.M. Fristrup. 2013. Detection and Classification of Motor Vehicle Noise in a Forested Landscape. Environmental Management 52(5):1262–1270. doi:10.1007/s00267013-0123-8. Brown, S., S. Hanson, and R.J. Nicholls. 2014. Implications of Sea-level Rise and Extreme Events around Europe: A Review of Coastal Energy Infrastructure. Climatic Change 122(1-2):81–95. doi:10.1007/s10584-013-0996-9. Brzoska, M. and C. Frohlich. 2015. Climate change, migration and violent conflict: vulnerabilities, pathways and adaptation strategies. Migration and Development 5(2):190-210. doi:10.1080/21632324.2015.1022973. Brzoska and Frohlich 2015. citing Hsiang, S.M., M. Burke, and E. Michael. 2013. Quantifying the influence of climate on human conflict. Science 341:6151. doi:10.1126/science.1235367. Bulka, C., L.J. Nastoupil, W. McClellan, A. Ambinder, A. Phillips, K. Ward, A.R. Bayakly, J.M. Switchenko, L. Waller, and C.R. Flowers. 2013. Residence Proximity to Benzene Release Sites is Associated with Increased Incidence of Non‐Hodgkin Lymphoma. Cancer 119(18):3309–3317. doi:10.1002/cncr.28083. Available at: D7D76A362.f02t02. (Accessed: February 26, 2018). Buhaug, H., T.A. Benjaminsen, E. Sjaastad, and O.M. Theisen. 2015. Climate variability, food production shocks, and violent conflict in Sub-Saharan Africa. Environmental Research Letters 10(12). doi:10.1088/1748-9326/10/12/125015. Available at: . (Accessed: February 20, 2018). 12-7 F IFIGURE GURE Title here.... Impact of coding intensity on MA risk scores was larger than coding adjustment, 2013–2017 11-XX 3–4 11 Impact as a percent above FFS 10 9 8 7 6 5 Portion of coding intensity impact not accounted for by payment policies 4 Impact on MA risk scores used for payment Coding adjustment applied to MA risk scores 3 CMS–HCC version 2013–2015 2 CMS–HCC version 2014–2016 CMS–HCC version 2017 1 0 2013 Note: 2014 2015 2016 2017 MA (Medicare Advantage), FFS (fee-for-service), CMS–HCC (CMS–hierarchical condition category). All estimates account for any differences in age and sex between MA and FFS populations. A blend of two model versions was used for payment in 2014 and 2015. Source: MedPAC analysis of CMS enrollment and risk score files. to 1.5 percent per year, increasing the overall impact of 2016 and 93 percent of MA enrollees in 2017.11 However, coding intensity on MA risk scores in each year. Between for enrollees with different encounter-based and RAPSNote: and Note 2016, and Source are risk in InDesign. 2015 MA scores continued to increase at based risk scores, the RAPS score tends to be higher. about the same rate as in prior years, but FFS risk scores Overall, encounter-based risk scores were about 2 percent Source: grew faster than prior years and roughly matched the lower than RAPS-based risk scores in both 2016 and 2017, MA risk score growth rate.10 Risk score growth between despite a decrease in the overall difference by about a half Notes about this graph: percent in 2017. The phase-in of encounter-based risk 2015 and 2016 was affected by the transition from ICD–9 scores (see Figure 13-3, p. 362) reduced the overall impact to ICD–10 diagnosis codes. BetweenMake 2016 and 2017, in the datasheet. • Data is in the datasheet. updates of coding intensity by about 0.2 percent in 2016 and by we again found similar growth rates for MA and FFS • I deleted the years from the x-axis and put in my own. about 0.4 percent in 2017. For 2018, CMS decreased the risk scores, with MA risk score growth outpacing FFS • 0.3 I had to manually draw tickpenetration marks andofaxis linesusebecause they kept risk resetting I changed any of encounter-based scoreswhen to 15 percent, which is data. by only percent. An increase in the likely to increase the impact of coding intensity on MA alternative payment models in FFS Medicare over this • The dashed line looked ok here, so I didn’t hand draw it. risk scores. period may also have affected the FFS risk score growth • I can’t delete the legend, so I’ll just have to crop it out in InDesign. rate. Overall Otherwise impact of ifMA • Use direct selection tool to select items for modification. youcoding use the intensity black selection tool, they w Encounter data as ayou source of diagnostic default when change the data. information We found that MA risk scores for 2017 were about 7 percent higher than for a comparable FFS population. The • Use paragraph styles (and object styles) to format. Starting in 2016, CMS blended risk scores based on decline from our 2016 estimate of 8 percent is the net of encounter data with risk scores based on RAPS data. We faster MA risk score growth (0.3 percent), implementing found that encounter-based and RAPS-based risk scores a new version of the risk adjustment model (–0.8 percent), were the same for about 92 percent of MA enrollees in and increasing the use of encounter data for risk scores 364 The Medicare Advantage program: Status report ...
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