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

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iii “I want to become a thought leader for public and private healthcare finance systems, using my leadership to help build quality systems and to communicate reasonable expectations.” Tia Goss Sawhney March 2008, DrPH Application, Statement of Purpose, First Sentence
iv ACKNOWLEDGEMENTS I am thankful for support from the Center for Disease Control and Prevention (CDC) Training Program Grant # 1 T01 CD000189-01 and the Society of Actuaries (SOA) James C. Hickman Scholar Doctoral Stipend Program. TGS
Page 1: Controlling Indirect Selection unde
Page 5 and 6: vi C. Regulatory Timing ...........
Page 7 and 8: viii 22. Profit = .................
Page 9 and 10: x MEPS P&C PBM PHI PPACA RA SES SOA
Page 11 and 12: I. INTRODUCTION Consultants from th
Page 13 and 14: 3 This paper is meant to be a prime
Page 15 and 16: 5 1970; Rothschild & Stiglitz, 1976
Page 17 and 18: 7 the right side of the bar. With r
Page 19 and 20: 9 limits choice for other high risk
Page 21 and 22: 11 2. Process. Direct selection is
Page 23 and 24: 13 just a department. This expansiv
Page 25 and 26: 15 F. Consumers Demand Change Direc
Page 27 and 28: 17 2. Risk adjustment (PPACA, Secti
Page 29 and 30: 19 consumer selection. This section
Page 31 and 32: 21 Whether the adjustment occurs vi
Page 33 and 34: 23 risk. 43 Today’s risk adjustme
Page 35 and 36: 25 Figure 8 Other Predictive Variab
Page 37 and 38: 27 Genetics are also important. A d
Page 39 and 40: 29 generally socially and economica
Page 41 and 42: 31 of costs and very few have $50,0
Page 43 and 44: 33 have no recent diagnoses or pres
Page 45 and 46: 35 Unidentified Sick are unidentifi
Page 47 and 48: 37 Figure 15 Demonstration: Financi
Page 49 and 50: 39 The benefits of indirect selecti
Page 51 and 52: 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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109 6E: Disenrollment per head pena
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111 While insurers cannot directly
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113 the insureds who have a diagnos
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115 E. Monitoring If regulations ar
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117 enrollment and disenrollment ra
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119 publicly discussed. Discussing
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121 Such a strategy is dependent up
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REFERENCES AAA. (2009). Issue Brief
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125 Byrne, S. K. (2008). Healthcare
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127 Gallo, L. C., Penedo, F. J., Ex
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129 Lewin Group. (2010). Individual
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131 Riley, T. (2010). Reform has a
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133 van Barneveld, E., van Vliet, R
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APPENDIX Health Insurance Risk Adju
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Appendix 137 ABSTRACT The U.S. Pati
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Appendix 139 BACKGROUND As expresse
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Appendix 141 not eligible for subsi
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Appendix 143 Starting in 2011 insur
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Appendix 145 1) Current conditions
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Appendix 147 First consider the lin
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Appendix 149 counter this argument.
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Appendix 151 Current Risk Adjustmen
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Appendix 153 Model 4 fits the risk
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Appendix 155 An examination of the
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Appendix 157 ANALYSIS Data Descript
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Appendix 159 Specifically, the anal
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Appendix 161 Equation 1 Proxy Expen
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Appendix 163 Expenditures have been
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Appendix 165 Equation 3 Normalized
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Appendix 167 The technical difficul
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Appendix 169 prediction residuals?
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Appendix 171 The lower income and h
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Appendix 173 RESULTS The results ar
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Appendix 175 Figure 12 Income Effec
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Appendix 177 LIMITATIONS The analys
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Appendix 179 Likewise, due to healt
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Appendix 181 DISCUSSION Even with t
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Appendix 183 Much of Jost’s timel
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Appendix 185 RECOMMENDATIONS Based
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Appendix 187 TABLES
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Appendix 189
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Appendix 191
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Appendix 193 REFERENCES AAA. (2010)
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Appendix 195 Diez Roux, A. A. (2001
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Appendix 197 McKinsey & Company. (2
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Appendix 199 WHO. (2005). Facing th
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TIA GOSS SAWHNEY Vita 201 Directo
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TIA GOSS SAWHNEY Vita 203 “Busine
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