On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Wenjia Zhu who has a PhD from Boston University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.
Health plan innovations and health care costs in the commercial health insurance market
Randall P. Ellis, Thomas G. McGuire, Keith M. Ericson
What kinds of ‘innovations’ did you want to look at in your research, and why?
My dissertation investigated health plan “innovations” for cost containment, in which certain features are designed into health insurance contracts to influence how health care is delivered and utilized. While specifics may vary considerably across health plans, recent “innovations” feature two main strategies for constraining health spending. One is a demand-side strategy, which aims to reduce health care utilization through high cost-sharing on the consumer side. Plans using this strategy include “high-deductible” or “consumer-driven” health plans. The other is a supply-side strategy, in which insurers selectively contract with low-cost providers whom consumers have access to, thereby directing consumers to those low-cost providers. Plans employing this strategy include “narrow network” health plans.
Despite an ongoing debate about whether the demand-side or supply-side strategy is more effective at reducing costs, there is little work to guide this debate due to challenges in causal inference, estimation, and measurement. As a result, the question of cost containment through insurance benefit designs remains largely unresolved. To shed light on this debate, I investigated these two strategies using a large, multiple-employer, multiple-insurer panel dataset which allowed me to address various methodological challenges through the use of modern econometrics tools and novel estimation methods.
How easy was it to access the data that you needed to answer your research questions?
The main data for my dissertation research come from the Truven Analytic’s MarketScan® Commercial Claims and Encounters Database, which contains administrative claims of a quarter of the U.S. population insured through their employment. I was fortunate to access this database through the data supplier’s existing contract with Boston University, and the entire process of accessing the data involved low effort overall.
Occasionally I needed to refine my research questions or find alternative approaches because certain pieces of information were not available in this database and were hard to access elsewhere. For example, in Chapter 1, we did not further examine heterogeneity of plan coverage within plan types because detailed premiums or benefit features of health plans were not observed (Ellis and Zhu 2016). In Chapter 3, I sought out an alternative approach in lieu of the maximum likelihood (ML) method when estimating provider network breadth because provider identifiers were not coded consistently across health plans in my data, precluding the reliable construction of one key element in the ML method.
Your PhD research tackled several methodological challenges. Which was the most difficult to overcome?
In the course of my research, I found myself in constant need of estimating models that require controlling for multiple fixed effects, each of high dimension (something we called “high-dimensional fixed effects”). One example is health care utilization models that control for provider, patient, and county fixed effects. In these models, however, estimation often became computationally infeasible in the presence of large sample sizes and unbalanced panel datasets. Traditional approaches to absorbing fixed effects no longer worked, and the models with billions of data points could barely be handled in Stata even though it provides some convenient user-written commands (e.g. REGHDFE).
This motivated me and my coauthors to devote an entire chapter in my dissertation to looking into this issue. In Chapter 2, we developed a new algorithm that estimates models with multiple high-dimensional fixed effects while accommodating such features as unbalanced panels, instrumental variables, and cluster-robust variance estimation. The key to our approach is an iterative process of sequentially absorbing fixed effects based on the Frisch-Waugh-Lovell Theorem. By writing up our algorithm into a SAS macro that does not require all data to reside in core memory, we can handle datasets of essentially any size.
Did you identify any health plan designs that reduced health care costs?
Certainly. My dissertation shows that health plans that manage care – imposing cost-sharing, requiring gatekeepers, or restricting consumer choice of providers – spent much less (on procedures) compared to comprehensive insurance plans that do not have any of these “care management” elements, even after controlling for patient selection into plan types.
On the other hand, we did not find evidence that either of the new health plan “innovations” – high cost-sharing or narrow networks – particularly saved health care costs compared to Preferred Provider Organizations (PPOs) (Ellis and Zhu 2016). One possibility is that incentives to control one aspect of spending create compensating effects in other aspects. For example, although high-deductible/consumer-driven health plans shift cost responsibility from employers to enrollees, they did not reduce health care spending due to higher provider prices and higher coding intensity. Similarly, while narrow network plans reduced treatment utilization, they did so mostly for the less severely ill, creating the offsetting incentive of up-coding by providers on the remaining sicker patients.
Based on your findings, what would be your first recommendation to policymakers?
To improve the effectiveness of health care cost containment, my first recommendation to policymakers would be to design mechanisms to more effectively monitor and reduce service prices.
My dissertation shows that while tremendous efforts have been made by health plans to design mechanisms to manage health care utilization (e.g., through imposing a higher cost-sharing on consumers) and to direct patients to certain providers (e.g., through selective contracting), overall cost containment, if any, has been rather modest due to insufficient price reductions. For example, we found that high-deductible/consumer-driven health plans had significantly higher average procedure prices than PPOs (Ellis and Zhu 2016). Even for narrow network plans in which insurers selectively contract with providers, we did not find evidence that these plans were successful in keeping low-cost providers. Difficulties of keeping prices down may reflect unbalanced bargaining power between insurers and providers, as well as special challenges in consumers price-shopping in the presence of complex insurance contract designs (Brot-Goldberg et al. 2017).