Are private insurers in the United States using cost-effectiveness analysis evidence to set their formularies?

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By Elizabeth Brouwer

As prices rise for many prescription drugs in the United States (US), stakeholders have made efforts to curb the cost of medications with varying degrees of success. One option put forth to contain drug spending is to connect drug coverage and cost-sharing to value, with cost-effectiveness analysis being one of the primary measures of drug value.

In 2010, a payer in the Pacific Northwest implemented a formulary where cost-sharing for prescription drugs was driven by cost-effectiveness evidence. This value-based formulary (VBF) had 5 tiers based on cost-effectiveness ranges determining a patient’s copay amount, aka their level of cost-sharing (Table 1). There was allotment for special cases where a drug had no alternatives or treated a sensitive population, however a majority of the drugs fell within each of these categories. Later analysis found that this VBF resulted in a net (including both payer and patient) decrease in medication expenditures of $8 per member per month, with no change in total medication or health services utilization. A 2018 literature review found slightly different (but still optimistic) results, that value-based formulary design programs increased medication adherence without increasing health spending.


Given the potential benefits of implementing value-based cost-sharing for prescription drugs, we wanted to know if other private payers in the US were using cost-effectiveness value evidence to set their drug formularies. If private payers were “moving toward value,” we would expect to see cost-sharing for high-value drugs getting cheaper relative to cost-sharing for low-value drugs (Figure 1).


To test this theory, we used claims data from a large portion of Americans with private, employee-sponsored health insurance to find the average out-of-pocket cost for each prescription drug in each year from 2010-2013. The collapsed claims data were then linked to the value designation (or “tier”) for each drug. We used a random effects model to see how out-of-pocket costs changed each year in each cost-effectiveness category. (For more details on our methods, please check out our paper, which was recently published in PharmacoEconomics journal).

The results revealed a few interesting trends.

Cost-sharing for prescription drugs was trending toward value in those years, but in a very specific way. Average cost-sharing across all “tiers” decreased over the time frame, and drugs with cost-effectiveness ratios below $10,000 per quality-adjusted life-year (QALY) were getting cheaper at a faster rate than those with cost-effectiveness ratios above that threshold. But there was no distinction in cost-sharing for drugs within those two groups, even accounting for generic status.

Additionally, the movement toward value that we saw was largely the result of increased use of generic drugs, rather than an increased use of more cost-effective drugs. Splitting the data by generic status showed that we are not using higher value drugs within generic and brand name categories (Figure 2).


Figure 2Source

Our results indicate that there is probably space in private drug formularies to further encourage the use of higher value drugs options and, conversely, to further discourage use of lower-value drug options. This is particularly true for drugs with ICERs in the range of $10,000-$150,000 per QALY and above, where payers are largely ignoring differences in value.

One limitation of the analysis was that it was restricted to years 2010-2013. Whether private payers in the US have increased their use of value information since the implementation of the Affordable Care Act in 2014, or in response to continually rising drug prices, is an important question for further research.

In conclusion, there is evidence indicating payers have an opportunity to implement new (or expand existing) VBF programs. These programs have the potential to protect patient access to effective medical treatments while addressing issues with affordability in the US health care system.


Economic evaluation of New Rural Cooperative Medical Scheme in China

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By Boshen Jiao

In China, while the private health insurance is growing rapidly, the government-funded basic health insurance still dominates the health care landscape. Chinese government defines three types of beneficiaries: urban employees, urban residents, and rural residents. Accordingly, three main types of healthcare coverage plans were implemented in China: the Urban Employee Basic Medical Insurance, the Urban Resident Basic Medical Insurance, and the New Rural Cooperative Medical Scheme (NCMS).

The NCMS, which was initiated in 2003 and financed by both governments and individuals, was specifically designed for rural residents in China. In some sense, the Chinese government can feel proud since 98% of the rural residents are covered and this, undoubtedly, has been viewed as a great success. In particular, many of the newly covered individuals are considered to be poor and underserved, with a long history of struggling for access to basic health care.

However, the health and economic consequence of the NCMS might not be that pleasing. While the effectiveness for mortality reduction remained controversial based on current scientific evidence, the NCMS resulted in a 61% increase in out-of-pocket spending. Given the fact that the NCMS has finite resources and impacts a large number of lives, it was critical to do a “thought experiment” and assess the cost-effectiveness of the NCMS. This is the subject of a paper I recently published with Dr. Jinjing Wu from the Asian Demographic Research Institute at Shanghai University and several coauthors from the Columbia Mailman School of Public Health. This paper, titled “The cost-effectiveness analysis of the New Rural Cooperative Medical Scheme in China,” was recently published in PloS One.

Initial estimates of NCMS’s effect on mortality were based on quasi-experimental studies that produced conflicting results. Some argued that NCMS significantly decreased the death rate among the elderly in the eastern region, while the other study using a nationally representative sample concluded to have no statistically significant effect. Although it was tempting to embrace the favorable results, our investigators decided to take on the less-favorable study. We made this call mainly because the nationally representative sample was derived from the Disease Surveillance Point system which was widely accepted as a very reliable data source. Besides, we hoped to draw from the whole country, rather than only focusing on East China where more economic resources and better healthcare are offered. In addition to the effect on mortality rate, the NCMS had proved to successfully lower the risk of hypertension, which was also included as an effectiveness parameter in our model.

Because of uncertainty around its effect on rural residents’ survival, it is likely that the NCMS is not cost-effective. Based on our analysis, the NCMS can only buy one more QALY for rural residents at the social price of 71,480 international (Int) dollars (Note: the costs and economic benefits were converted into 2013 Int dollars using purchasing power parity exchange rate reported by the World Bank). This is not optimal for China. If we believe that three times per capita GDP can be a fair willingness-to-pay threshold (Int$845,659), the NCMS had only a 33% chance to be cost-effective. The results were not surprising, however, nonetheless disappointing.  One possibility that we did not explore is that the elderly benefit the most from NCMS. Using a nationally representative sample, however, the NCMS is plausibly costly for the society and failed to produce sufficient health benefits.

We discussed the reasons why the NCMS appears to be inefficient. Current literature described the NCMS as providing catastrophic coverage that mostly covers inpatient services. People may barely use the preventative care or other necessary outpatient services, which would plausibly lead to severe illness and costly complications in the future. Moreover, the NCMS is associated with high copayments, which restricts low-income rural residents’ access to health care and fails to reduce out-of-pocket expenses. We concluded that, while the Chinese government indeed achieved a great success in coverage expansion, the program’s efficiency should be a consideration for future improvements. In order to achieve this goal, cost-effectiveness analysis could be a useful tool when designing the plan.

Our study presented an overall picture of the cost-effectiveness of the NCMS, in which the effect was estimated based on an aggregated of the data collected from different regions. However, the heterogeneity across the regions, particularly at the county level, would need to be taken into account for the future study. This is because the county governments play a critical role in financing for the NCMS, and their budget constraint for the plan has a fundamental effect on the design and implementation of it. As a consequence, the health outcome of NCMS may vary dramatically across the counties. Our analysis would have been enriched and would have provided more informative policy implications if the county level data can be obtained.