Controlling Indirect Selection under Healthcare Reform - Society of ...

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Appendix 160 Figure 6 Ratio of Expenditures to Charges Family Income Category Ratio of Family Income to FPL Ratio Expenditures to Charges 1 POOR/NEGATIVE Less than 1.00 40% 2 NEAR POOR 1.00 to 1.24 42% 3 LOW INCOME 1.25 to 1.99 48% 4 MIDDLE INCOME 2.00 to 3.99 53% 5 HIGH INCOME 4.00 or Greater 59% Mean 50% FPL = Federal Poverty Level. Developed from MEPS 2005-2006-2007 cost data for people under age 65. Expenditures represent the total paid from all sources, including the patient. Expenditures are clearly not an appropriate outcome measure for predicting healthcare costs by income for people who will be covered under individual health insurance. Charges are not necessarily appropriate either. As stated, they are a largely fictitious value and their level may well vary by income. MEPS provides high-level counts of the healthcare units consumed per participant, per year. For example, the number of office based encounters with sub-counts for physician visits, physical therapy visits, etc; the number of hospital admissions and days; and the number of drugs. The mean cost per unit can be calculated across the entire MEPS dataset, including for inpatient stays. (Inpatient stays require the use of linear regression to separately calculate a mean cost per admission and per hospital day.) This paper develops and uses two new outcome measures: “proxy expenditures” and “proxy charges” that assign mean costs to each participant’s units of healthcare consumption. 27 Mean costs are calculated for each service category, separately for adults and children, by year. 27 I have been unable to find references to other researchers developing such proxies or otherwise normalizing expenditures for differences in payer mix.
Appendix 161 Equation 1 Proxy Expenditure i,AC,yr = Σ k (# of Units Consumed k,i,AC,yr x Mean Unit Expenditure k,AC,yr ) Proxy Charge i,AC,yr = Σ k (# of Units Consumed k,i,AC,yr x Mean Unit Charge k,AC,yr ) Where i = MEPS participant in year yr AC = adult or child yr = year k = healthcare service type An analysis of the proxy expenditures and charges revealed that children have lower unit costs (expenditures and charges) than adults, hence the above adult/child differentiation. Intuitively it makes sense that the average child unit of care is less resource intensive than the average adult unit of care. 28 Hence the differentiation between children and adults in the above formula. More problematic is that higher income people have higher unit charges than lower income people. Based on this data it is impossible to determine the cause of the income-charge differentials and to speculate whether the differentials would persist if everyone was covered under the same insurance plan. It could be that higher income people tend to go to providers who charge more for essentially the same services. Or it could be that higher income people receive more intensive services. For example, that they are more likely to have a cardiac surgical procedure if admitted to a hospital for myocardial infarction. Proxy Expenditures and Proxy Charges represent cost-weighted aggregate consumption measures. To the extent that the higher unit costs for wealthier people reflect higher quality or intensity of services, actual charges are also an aggregate consumption measure. To the extent that they reflect simply higher prices for the same service, actual charges are not a consumption measure. 28 Children might be expected to have proportionately more primary care visits for routine care and minor illnesses and fewer visits for chronic and comorbid condition management, specialist visits, and expensive diagnostic tests.
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Controlling Indirect Selection unde
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iv ACKNOWLEDGEMENTS I am thankful f
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vi C. Regulatory Timing ...........
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viii 22. Profit = .................
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x MEPS P&C PBM PHI PPACA RA SES SOA
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I. INTRODUCTION Consultants from th
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3 This paper is meant to be a prime
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5 1970; Rothschild & Stiglitz, 1976
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7 the right side of the bar. With r
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9 limits choice for other high risk
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11 2. Process. Direct selection is
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13 just a department. This expansiv
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15 F. Consumers Demand Change Direc
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17 2. Risk adjustment (PPACA, Secti
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19 consumer selection. This section
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21 Whether the adjustment occurs vi
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23 risk. 43 Today’s risk adjustme
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25 Figure 8 Other Predictive Variab
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27 Genetics are also important. A d
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29 generally socially and economica
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31 of costs and very few have $50,0
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33 have no recent diagnoses or pres
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35 Unidentified Sick are unidentifi
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37 Figure 15 Demonstration: Financi
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39 The benefits of indirect selecti
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41 get insurance or gets it on less
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43 Assuming that at least some insu
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III. INDIRECT SELECTION UNDER HEALT
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47 insurers should use a Lifestyle
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49 Figure 19 Neighborhood-Level Dat
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51 This list of data elements align
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53 Consumer marketing databases pro
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55 Figure 23 New Business Predictiv
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57 diabetics while finding ways to
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59 risk insureds, “skimping” wi
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61 Figure 28 The model is consisten
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63 Insurers will rely upon predicti
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65 that they are related to the cha
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67 profitable and the second neighb
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69 relevant to insurers with a larg
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IV. PUBLIC POLICY CHALLENGES Indire
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73 will never be a clear line betwe
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75 healthcare cost variance outside
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77 to educate poor children. Althou
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79 Figure 30 Dynamic System: The ke
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81 regulators. If regulators don’
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83 regulatory front, while struggli
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85 in Chapter 3. The discussion wil
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87 1A: Include family income in ris
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89 example, assigning a risk premiu
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91 2. Market Entry Regulators desig
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93 2A: Individual insurance only fr
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95 “restricting choices”. It is
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97 adverse consumer selection. Whil
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99 3B: Fixed benefit designs. It is
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101 There is general consensus in t
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103 4D: Agent restrictions. At a mi
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105 Figure 35 Regulatory Options 5
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107 6. Post-Enrollment Consumer sel
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Page 121 and 122: 111 While insurers cannot directly
Page 123 and 124: 113 the insureds who have a diagnos
Page 125 and 126: 115 E. Monitoring If regulations ar
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Page 129 and 130: 119 publicly discussed. Discussing
Page 131 and 132: 121 Such a strategy is dependent up
Page 133 and 134: REFERENCES AAA. (2009). Issue Brief
Page 135 and 136: 125 Byrne, S. K. (2008). Healthcare
Page 137 and 138: 127 Gallo, L. C., Penedo, F. J., Ex
Page 139 and 140: 129 Lewin Group. (2010). Individual
Page 141 and 142: 131 Riley, T. (2010). Reform has a
Page 143 and 144: 133 van Barneveld, E., van Vliet, R
Page 145 and 146: APPENDIX Health Insurance Risk Adju
Page 147 and 148: Appendix 137 ABSTRACT The U.S. Pati
Page 149 and 150: Appendix 139 BACKGROUND As expresse
Page 151 and 152: Appendix 141 not eligible for subsi
Page 153 and 154: Appendix 143 Starting in 2011 insur
Page 155 and 156: Appendix 145 1) Current conditions
Page 157 and 158: Appendix 147 First consider the lin
Page 159 and 160: Appendix 149 counter this argument.
Page 161 and 162: Appendix 151 Current Risk Adjustmen
Page 163 and 164: Appendix 153 Model 4 fits the risk
Page 165 and 166: Appendix 155 An examination of the
Page 167 and 168: Appendix 157 ANALYSIS Data Descript
Page 169: Appendix 159 Specifically, the anal
Page 173 and 174: Appendix 163 Expenditures have been
Page 175 and 176: Appendix 165 Equation 3 Normalized
Page 177 and 178: Appendix 167 The technical difficul
Page 179 and 180: Appendix 169 prediction residuals?
Page 181 and 182: Appendix 171 The lower income and h
Page 183 and 184: Appendix 173 RESULTS The results ar
Page 185 and 186: Appendix 175 Figure 12 Income Effec
Page 187 and 188: Appendix 177 LIMITATIONS The analys
Page 189 and 190: Appendix 179 Likewise, due to healt
Page 191 and 192: Appendix 181 DISCUSSION Even with t
Page 193 and 194: Appendix 183 Much of Jost’s timel
Page 195 and 196: Appendix 185 RECOMMENDATIONS Based
Page 197 and 198: Appendix 187 TABLES
Page 199 and 200: Appendix 189
Page 201 and 202: Appendix 191
Page 203 and 204: Appendix 193 REFERENCES AAA. (2010)
Page 205 and 206: Appendix 195 Diez Roux, A. A. (2001
Page 207 and 208: Appendix 197 McKinsey & Company. (2
Page 209 and 210: Appendix 199 WHO. (2005). Facing th
Page 211 and 212: TIA GOSS SAWHNEY Vita 201 Directo
Page 213 and 214: TIA GOSS SAWHNEY Vita 203 “Busine
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