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The Evolution of Health Insurer Costs in Massachusetts, 2010–2012

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We analyze the evolution of health insurer costs in Massachusetts between 2010 and 2012, paying particular attention to changes in the composition of enrollees. This was a period in which Health Maintenance Organizations (HMOs) increasingly used physician cost control incentives but Preferred Provider Organizations (PPOs) did not. We show that cost growth and its components cannot be understood without accounting for (1) consumers’ switching between plans, and (2) differences in cost characteristics between new entrants and those leaving the market. New entrants are markedly less costly than those leaving (and their costs fall after their entering year), so cost growth of continuing enrollees in a plan is significantly higher than average per-member cost growth. Relatively high-cost HMO members switch to PPOs while low-cost PPO members switch to HMOs, so the impact of cost control incentives on HMO costs is likely different from their impact on market-wide insurer costs.

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  1. See, e.g., Bundorf et al. (2009), Aizcorbe and Nestoriak (2011), Herrera et al. (2013) and Dunn et al. (2017). There is also significant attention in this literature to whether improvements in health care quality might bias measures of prices: see e.g., Cutler et al. (1998, 2001).

  2. As we describe in Sect. 2, we limit the sample to enrollees in the “big three” insurers—Blue Cross, Harvard Pilgrim, and Tufts—for data quality reasons. These three insurers cover more than three-fourths of commercial enrollees.

  3. Alternative payments technically encompass a broader set of non-fee-for-service payment mechanisms, including limited budgets and bundled payments for episodes of care. However, in practice, global payments comprised 97% of alternative payment schemes in Massachusetts. We therefore use these terms interchangeably.

  4. The stayer sample for 2011–2012 includes enrollees who enter in 2010.

  5. Medical costs do not include prescription drug or dental costs, since not all plans cover these benefits. In cases where physicians are paid using alternative payment arrangements, our data contain the fee-for-service component of the payment rather than later reconciliations with respect to shared savings.

  6. The ACG grouper software begins by aggregating individual patient ICD-9 codes into 32 Aggregate Diagnosis Groups (ADGs) based on duration and severity of the condition, diagnostic certainty, types of health care services likely to be used, and the degree to which specialty care is likely to be required. See the Johns Hopkins ACG Software System Technical Reference Guide, Version 10.0, December 2011. The software then uses a clearly defined algorithm to place individuals into 93 discrete ACG categories based on their assigned ADGs, age and gender. These categories are listed in the Appendix with their associated Resource Utilization Bands, which are assigned based on expected spending required for each category.

  7. Seven provider organizations agreed to use these contracts in 2009, followed by four more in 2010. At the same time, Blue Cross also introduced comprehensive support for participating physician groups, including regular provision of data on the care provided to patients by providers (e.g. hospitals) outside of the group, and organized sessions where the groups met to discuss best practices. See Chernew et al. (2011) for additional details.

  8. Note, however, that the decomposition is an identity and the cost component is the part that we can not explain by our severity and age/gender groupings. Consequently, any error in the construction of those two components will be transmitted, with opposite sign, to our cost component.

  9. Under the alternative payment arrangements that are frequently used by HMO plans in the data, a target for overall spending is established for each physician group based on the severity level of the group’s patients. Savings relative to this target—and in many cases excesses above the target—are shared between the insurer and the physician group. This introduces an incentive for the physician group to control costs, and potentially also to “upcode”: if physicians list a larger number of diagnoses on the patient’s record, this will result in a larger number of diagnoses per patient and a resulting movement of patients to ACGs that are coded as higher severity level. In turn this generates an increase in the group’s target spending level.

  10. The analyses of entry, exit and switching use a slightly different data sample from the cost decompositions in Tables 3 and 7. For example we simplify by dropping enrollees who switch plans more than once. For this reason the aggregate cost numbers in Tables 1, 3 and 7 differ slightly from those in Tables 4, 5 and 6.

  11. This is partly offset by the high cost growth of exiters in their last 2 years in-sample.

  12. For example, this is a much larger switching percentage than that recorded in Handel (2013).

  13. That is, \(C_{t+1}-C_{t} = \sum _{r} (s_{r,t+1}C_{r,t+1} - s_{r,t}C_{r,t}) \) where \(C_{r,t}\) = Average monthly cost of plan j’s enrollment who are in group r and \(s_{r,t}\) is the share of plan j’s enrollment (in member-months) who are in group r. Note that \(s_{r,t+1}=0\) for exiters/switchers out of the plan (before the start of \(t+1\)), so they only contribute to \(C_{r,t}\). Similarly, entrants/switchers in only contribute to \(C_{r,t+1}\).

  14. The cost growth data in Tables 4, 5 and 6 differ somewhat from those in Tables 3 and 7 due to differences in the underlying data samples. In particular, our switching and entry/exit analyses drop enrollees who switch plans multiple times.

  15. In the full sample, Blue Cross HMO experienced a cost reduction of $12.83, while the PPO had a cost reduction of $1.47.


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Correspondence to Kate Ho.

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The authors gratefully acknowledge funding support for this work through a grant from the Commonwealth Fund. We also acknowledge financial support from a pilot Grant under NIH Grant P01AG005842 via the National Bureau of Economic Research. Shepard gratefully acknowledges Ph.D. and post-doctoral funding support from the National Institute on Aging Grant No. T32-AG000186 (via the National Bureau of Economic Research).



See Table 8.

Table 8 Adjusted clinical group (ACG) descriptions and resource utilization bands (RUB)

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Ho, K., Pakes, A. & Shepard, M. The Evolution of Health Insurer Costs in Massachusetts, 2010–2012. Rev Ind Organ 53, 117–137 (2018).

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