Jason Shafrin’s journal round-up for 9th September 2019

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Price effects of a hospital merger: heterogeneity across health insurers, hospital products, and hospital locations. Health Economics [PubMed] [RePEc] Published 1st July 2019

Most economics literature indicates that hospital mergers typically result in higher prices. But what does higher prices mean? Does it mean higher prices for all services? Higher prices for all health insurers?

Many economic models assume that hospitals charge a standard base rate and charges for individuals’ procedures are a fixed ratio of the base across all hospitals. This approach would make sense in a DRG-based system where prices are proportional to the product of a hospital’s base rate and the Medicare Severity DRG specific weight for a given hospitalization.

In practice, however, it is possible for prices to vary across procedures, across different negotiated contracts with insurers, and even across different locations within the same hospital system. For instance, the economic theory in this paper shows that the effect of a hospital merger increases prices most when an insurer’s bargaining power is high. Why? Because if the insurer had weak bargaining power, the hospital already would have high prices; the marginal impact is only felt when insurers had market power to begin with. Another interesting theoretical prediction is that if substitution between hospitals is stronger for service A than service B, prices will increase more for the former product, since the merger decreases the ability of consumers to substitute across hospitals due to decreased supply.

In their empirical applications, the authors use a comprehensive nationwide patient‐level data set from the Netherlands, on hospital admissions and prices. The study looks at three separate services: hip replacement, knee replacement, and cataract surgery. They use a difference-in-difference approach to measure the impact of a merger on prices for different services and across payers.

Although the authors did replicate earlier findings and showed that prices generally rise after a merger, the authors found significant heterogeneity. For instance, prices rose for hip replacements but not for knee replacements or cataracts. Prices rose for four health insurers but not for a fifth. In short, while previous findings about average prices still hold, in the real world, the price impact is much more heterogeneous than previous models would predict.

The challenges of universal health insurance in developing countries: evidence from a large-scale randomized experiment in Indonesia. NBER Working Paper [RePEc] Published August 2019

In 2014, the Indonesian government launched Jaminan Kesehatan Nasional (JKN), a national, contributory health insurance program that aimed to provide universal health coverage by 2019. The program requires individuals to pay premiums for coverage but there is an insurance mandate. JKN, however, faced two key challenges: low enrollment and high cost. Only 20% of eligible individuals enrolled. Further, the claims paid exceeded premiums received by a factor of more than 6 to 1.

This working paper by Banerjee et al describes a large-scale, multi-arm experiment to examine three interventions to potentially address these issues. The interventions included: (i) premium subsidy, (ii) transaction cost reduction, and (iii) information dissemination. For the first intervention, individuals received either 50% or 100% premium subsidy if they signed up within a limited time frame. For the second intervention, households received at-home assistance to enroll in plans through the online registration system (rather than traveling to a distant insurance office to enroll). For the third intervention, the authors randomized some individuals to receive various informational items. The real benefit of this study is that people were randomized to these different interventions.

Using this study design, the authors found that premium assistance did increase enrollment. Further, premium assistance did not affect per person costs since the individuals who enrolled were healthier on average. Thus, the fear that subsidies would increase adverse selection was unfounded. The authors also found that offering help in registering for insurance increased enrollment. Thus, it appears that the ‘hassle cost’ of signing up for a government program represents a real hassle with tangible implications. However, the additional insurance information provided had no effect on enrollment.

These results are both encouraging and discouraging. Premium subsidies work and do not drive up cost per person. However, enrollment levels – even with a 100% premium subsidy and assistance registering for insurance – were only at 30%. This figure is far better than the baseline figure of 8%, but far from the ‘universal’ coverage envisioned by the creators of JKN.

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Jason Shafrin’s journal round-up for 15th July 2019

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Understanding price growth in the market for targeted oncology therapies. American Journal of Managed Care [PubMed] Published 14th June 2019

In the media, you hear that drugs prices—particularly for oncology—are on the rise. With high prices, it makes it difficult for payers to afford effective treatments. For countries where patients bear significant cost, patients may even go without treatment. Are pharmaceutical firms making money hand over fist with these rising prices?

Recent research by Sussell et al. argues that, despite increased drug price costs, pharmaceutical manufacturers are actually making less money on every new cancer drug they produce. The reason? Precision medicine.

The authors use data from both the IQVIA National Sales Perspective (NSP) data set and the Medicare Current Beneficiary Survey (MCBS) to examine changes in the price, quantity, and total revenue over time. Price is measured as episode price (price over a fixed line of therapy) rather than the price per unit of drug. The time period for the core analysis covers 1997-2015.

The authors find that drug prices have roughly tripled between 1997-2015. Despite this price increase, pharmaceutical manufacturers are actually making less money. The number of eligible (i.e., indicated) patients per new oncology drug launch fell between 85% to 90% over this time period. On net, median pharmaceutical manufacturer revenues fell by about half over this time period.

Oncology may be the case where high cost drugs are a good thing; rather than identifying treatments indicated for a large number of people that are less effective on average per patient, develop more highly effective drugs targeted to small groups of people. Patients don’t get unnecessary treatments, and overall costs to payers fall. Of course, manufacturers still need to justify that these treatments represent high value, but some of my research has shown that quality-adjusted cost of care in oncology has remained flat or even fallen for some tumors despite rising drug prices.

Do cancer treatments have option value? Real‐world evidence from metastatic melanoma. Health Economics [PubMed] [RePEc] Published 24th June 2019

Cost effectiveness models done from a societal perspective aim to capture all benefits and costs of a given treatment relative to a comparator. Are standard CEA approaches really capturing all costs and benefits? A 2018 ISPOR Task Force examines some novel components of value that are not typically captured, such as real option value. The Task Force describes real option value as value that is “…generated when a health technology that extends life creates opportunities for the patient to benefit from other future advances in medicine.” Previous studies (here and here) have shown that patients who received treatments for chronic myeloid leukemia and non-small cell lung cancer lived longer than expected since they were able to live long enough to reach the next scientific advance.

A question remains, however, of whether individuals’ behaviors actually take into account this option value. A paper by Li et al. 2019 aims to answer this question by examining whether patients were more likely to get surgical resection after the advent of a novel immuno-oncology treatment (ipilimumab). Using claims data (Marketscan), the authors use an interrupted time series design to examine whether Phase II and Phase III clinical trail read-outs affected the likelihood of surgical resection. The model is a multinomial logit regression. Their preferred specification finds that

“Phase II result was associated with a nearly twofold immediate increase (SD: 0.61; p = .033) in the probability of undergoing surgical resection of metastasis relative to no treatment and a 2.5‐fold immediate increase (SD: 1.14; p = .049) in the probability of undergoing both surgical resection of metastasis and systemic therapy relative to no treatment.”

The finding is striking, but also could benefit from further testing. For instance, the impact of the Phase III results are (incrementally) small relative to the Phase II results. This may be reasonable if one believes that Phase II is a sufficiently reliable indicator of drug benefit, but many people focus on Phase III results. One test the authors could look at is to see whether physicians in academic medical centers are more likely to respond to this news. If one believes that physicians at academic medical centers are more up to speed on the literature, one would expect to see a larger option value for patients treated at academic compared to community medical centers. Further, the study would benefit from some falsification tests. If the authors could use data from other tumors, one would expect that the ipilimumab Phase II results would not have a material impact on surgical resection for other tumor types.

Overall, however, the study is worthwhile as it looks at treatment benefits not just in a static sense, but in a dynamically evolving innovation landscape.

Aggregate distributional cost-effectiveness analysis of health technologies. Value in Health [PubMed] Published 1st May 2019

In general, health economists would like to have health insurers cover treatments that are welfare improving in the Pareto sense. This means, if a treatment provides more expected benefits than costs and no one is worse off (in expectation), then this treatment should certainly be covered. It could be the case, however, that people care who gains these benefits. For instance, consider the case of a new technology that helped people with serious diseases move around more easily inside a mansion. Assume this technology had more benefits than cost. Some (many) people, however, may not like covering a treatment that only benefits people who are very well-off. This issue is especially relevant in single payer systems—like the United Kingdom’s National Health Service (NHS)—which are funded by taxpayers.

One option is to consider both the average net health benefits (i.e., benefits less cost) to a population as well as its effect on inequality. If a society doesn’t care at all about inequality, then this is reduced to just measuring net health benefit overall; if a society has a strong preference for equality, treatments that provide benefits to only the better-off will be considered less valuable.

A paper by Love-Koh et al. 2019 provides a nice quantitative way to estimate these tradeoffs. The approach uses both the Atkinson inequality index and the Kolm index to measure inequality. The authors then use these indices to calculate the equally distributed equivalent (EDE), which is the level of population health (in QALYs) in a completely equal distribution that yields the same amount of social welfare as the distribution under investigation.

Using this approach, the authors find the following:

“Twenty-seven interventions were evaluated. Fourteen interventions were estimated to increase population health and reduce health inequality, 8 to reduce population health and increase health inequality, and 5 to increase health and increase health inequality. Among the latter 5, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact.”

Despite the attractive features of this approach analytically, there are issues related to how it would be implemented. In this case, inequality is based solely on quality-adjusted life expectancy. However, others could take a more holistic approach and look at socioeconomic status including other factors (e.g., income, employment, etc.). In theory, one could perform the same exercise measuring individual overall utility including these other aspects, but few (rightly) would want the government to assess individuals’ overall happiness to make treatment decisions. Second, the authors qualify expected life expectancy by patients’ sex, primary diagnosis and postcode. Thus, you could have a system that prioritizes treatments for men—since men’s life expectancy is generally less than women. Third, this model assumes disease is exogenous. In many cases this is true, but in some cases individual behavior could increase the likelihood of having a disease. For instance, would citizens want to discount treatments for diseases that are preventable (e.g., lung cancer due to smoking, diabetes due to poor eating habits/exercise), even if treatments for these diseases reduced inequality. Typically, there are no diseases that are fully exogenous or fully at fault of the individual, so this is a slippery slope.

What the Love-Koh paper contributes is an easy to implement method for quantifying how inequality preferences should affect the value of different treatments. What the paper does not answer is whether this approach should be implemented.

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