1 Factors Associated with Regional Variation in Medicare Part D Prescription Drug Plan Participation and Beneficiary Leslie M. Greenwald, Ph.D. Principal.

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Presentation transcript:

1 Factors Associated with Regional Variation in Medicare Part D Prescription Drug Plan Participation and Beneficiary Leslie M. Greenwald, Ph.D. Principal Scientist RTI, International

2 RTI colleagues who participated in this research Judith Shinogle John Kautter Elisabeth Root Dan Crespin Greg Pope

3 Purpose Understand the factors that influence regional variation in Medicare Part D prescription drug plan (PDP) participation and beneficiary premiums. Gain insight into regional characteristics that may be attractive to Medicare PDPs Identification of regional characteristics “attractive” to PDPs may help policy makers predict long term PDP plan participation “Region”, in this work, refers to the 34 Medicare PDP regions.

4 Methods Data on PDP regional characteristics were developed from a comprehensive database of U.S. Census, Medicare administrative files, the Area Resource File, Interstudy, AARP, and other sources. Regional characteristics included: Population Characteristics Payment Rates, Cost and Disease Risk Insurance Market Characteristics General Provider Supply Characteristics The number of PDP plan options and average monthly premiums charged were correlated (Pearsons r) with regional characteristics available in this database.

5 Findings: Total PDP Plans by PDP Region Map Total Number of PDP Plans by PDP Region, 2006

6 Findings: Average Total Monthly PDP Part D Premium by PDP Region Average Total Monthly PDP Part D Premium by PDP Region, 2006

7 Findings: Relationship Between Population Characteristics and PDP Participation/Premiums Both the number of defined standard benefit, and total number of PDP plans in each PDP region, are correlated to the total number of elderly and number of Medicare eligible beneficiaries in each region. Also found positive relationships between the total number of PDP plans and the number of Medicare managed care enrollees. Results suggest, not surprisingly, regions with larger potential enrollment pools attract more PDP plans.

8 Findings: Relationship between Population Characteristics and PDP Participation/Premiums Also noted relationships between population characteristics and PDP plan premiums at the regional level. Total (basic and total) premiums were negatively correlated to various measures of population size. Suggests, in general, that the greater the population size, the lower the average premiums. Exceptions: found strongly positive relationships between the percent of elderly living in poverty and the percent of elderly living in rural areas and premium levels. Findings indicate that regions with larger proportions of traditionally higher cost populations are likely to face higher average premiums.

9 Findings: Relationship between Payment Rates, Cost, Disease Risk and PDP Participation/Premiums Few payment rate or cost/disease factors were related to number of PDP plans at the regional level. May reflect that there is little actual relationship between historic Medicare payment rates and payment for the new PDP plans. Observed a positive correlation between the number of PDP plans and higher CMS HCC risk scores. One might expect insurers would avoid areas with higher risk scores. Also found a positive correlation between the percent of Medicare beneficiaries with Medicaid coverage (dual eligibles) and higher premiums, suggesting perhaps that regions with greater percentages of dual eligible beneficiaries may be perceived by plans to be higher cost.

10 Findings: Relationship between Insurance Market Characteristics and PDP Participation/Premiums Insurance market characteristics were correlated to the total number of PDPs at the regional level. Percent of total and Medicare population in an HMO plan was positively correlated with number of PDPs in a region. Relationship may indicate that PDP organizations, many of whom also offer HMOs and other managed care products, are attracted to regions with existing HMO penetration. Also found that the total number of PDPs was negatively correlated with the percent of total population enrolled in any PPO plans, suggesting that PPO penetration (at least by risk bearing PPOs) is not an attractive regional characteristic to potential PDP organizations. Regions with higher HMO and Medicare penetration in HMOs were associated with lower average monthly premiums. Conversely, regions with higher PPO penetration were associated with higher total PDP premiums.

11 Findings: Relationship between Insurance Market Characteristics and PDP Participation/Premiums Also noted interesting relationships between the number of PDP plans at the regional level, and both the market share and number of private insurers operating in that region. Fewer PDP plans were associated with PDP regions where the largest private insurer had greater market share. Similarly, noted a positive correlation between the number of private insurers and the total number of PDP plans. Findings may suggest that the presence of a dominant existing large private insurer – perhaps offering coverage to a dominant employer or group of employers – may be a deterrent to a larger number of PDP plans.

12 Discussion and Conclusions Medicare Part D standalone PDPs are widely available to Medicare beneficiaries in all regions, though the number and cost of these plans vary and are related to key PDP regional characteristics. Our findings are generally consistent with views expressed by PDP organizations, including: that more sparsely populated would have fewer PDPs and face higher premiums; and that the structure of health insurance markets in the PDP regions would affect the viability of the new Part D products.

13 Discussion and Conclusions Characteristics of the PDP regions insurance markets, particularly penetration of HMOs versus PPOs, and market share of existing large private insurers, also appear to be related to the number and pricing of PDP products. Currently options are widely available, however, attention to these characteristics may help policy makers understand and predict where PDPs may be successful in future years as the market for this benefit matures.