Skip to main content
Log in

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

  • Published:
Review of Industrial Organization Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  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.

References

  • Aizcorbe, A., & Nestoriak, N. (2011). Changing mix of medical care services: Stylized facts and implications for price indexes. Journal of Health Economics, 30(3), 568–574.

    Article  Google Scholar 

  • Breyer, F., Bundorf, M. K., & Pauly, M. (2011). Health care spending risk, health insurance, and payment to health plans. Handbook of Health Economics, 2, 691–762.

    Article  Google Scholar 

  • Bundorf, M. K., Royalty, A., & Baker, L. C. (2009). Health care cost growth among the privately insured. Health Affairs, 28(5), 1294–1304.

    Article  Google Scholar 

  • Center for Health Information and Analysis (CHIA). (2014). Massachusetts commercial medical care spending: Findings from the all-payer claims database. Available at http://www.mass.gov/anf/docs/hpc/apcd-almanac-chartbook.pdf. Accessed 3 January 2017.

  • Center for Health Information and Analysis (CHIA). (2015). Annual report on the performance of the Massachusetts health care system: Data book. Available at http://www.chiamass.gov/assets/2015-annual-report/2015-Annual-Report-Data-Books.zip. Accessed 3 January 2017.

  • Chernew, M. E., Mechanic, R., Landon, B. E., & Safran, D. (2011). Private-payer innovation in Massachusetts: The ‘alternative quality contract’. Health Affairs, 30(1), 51–61.

    Article  Google Scholar 

  • Cutler, D. M., McClellan, M., Newhouse, J. P., & Remler, D. (1998). Are medical prices declining? Evidence from heart attack treatments. Quarterly Journal of Economics, 113, 991–1024.

    Article  Google Scholar 

  • Cutler, D. M., McClellan, M., Newhouse, J. P., & Remler, D. (2001). Pricing heart attack treatments. In D. Cutler, E. Berndt (Eds.) Medical care output and productivity (pp. 305–362). Chicago: University of Chicago Press.

  • Dunn, A., Liebman, E., & Shapiro, A. H. (2017). Decomposing medical-care expenditure growth. NBER Working Paper 23117.

  • Handel, B. (2013). Adverse selection and inertia in health insurance markets: When nudging hurts. American Economic Review, 103(7), 2643–2682.

    Article  Google Scholar 

  • Herrera, C., Gaynor, M., Newman, D., Town, R. J., & Parente, S. T. (2013). Trends underlying employer-sponsored health insurance growth for Americans younger than age sixty-five. Health Affairs, 32(10), 1715–1722.

    Article  Google Scholar 

  • Johns Hopkins ACG Software System Technical Reference Guide, Version 10.0. (2011).

  • Office of Attorney General Martha Coakley. (2010). Examination of health care cost trends and cost drivers. Retrieved from http://www.mass.gov/ago/docs/healthcare/2010-hcctd.pdf. Accessed 3 January 2017.

  • Office of Attorney General Martha Coakley. (2011). Examination of health care cost trends and cost drivers. Retrieved from http://www.mass.gov/ago/docs/healthcare/2011-hcctd.pdf. Accessed 3 January 2017.

  • Office of Attorney General Martha Coakley. (2013). Examination of health care cost trends and cost drivers. Retrieved from http://www.mass.gov/ago/docs/healthcare/2013-hcctd.pdf. Accessed 3 January 2017.

  • Song, Z., Rose, S., Safran, D. G., Landon, B. E., Day, M. P., & Chernew, M. E. (2014). Changes in health care spending and quality 4 years into global payment. New England Journal of Medicine, 371(18), 1704–1714.

    Article  Google Scholar 

  • Song, Z., Safran, D. G., Landon, B. E., Landrum, M. B., He, Y., Mechanic, R. E., et al. (2012). The ‘alternative quality contract’, based on a global budget, lowered medical spending and improved quality. Health Affairs, 31(8), 1885–1894.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kate Ho.

Additional information

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).

Appendix

Appendix

See Table 8.

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ho, K., Pakes, A. & Shepard, M. The Evolution of Health Insurer Costs in Massachusetts, 2010–2012. Rev Ind Organ 53, 117–137 (2018). https://doi.org/10.1007/s11151-018-9623-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11151-018-9623-2

Keywords

Navigation