Looking Beyond Benefit-Cost Analysis to Measure the Value of Life-Saving Drugs

Close to 9 million people in India suffer from hepatitis C. If left untreated, the virus leads to cirrhosis or liver damage, which eventually causes death from organ failure or cancer. On average, a 50-year-old man in India with asymptomatic liver damage who doesn’t receive treatment is expected to live a little more than a decade.

Until recently, the typical treatment for chronic hepatitis C in India was a 24-week course of peginterferon injections combined with pills to combat side effects. The treatment’s efficacy was relatively low: It cured 40 to 80% of patients. There was a need for more effective treatments, not just in India but for the 58 million people worldwide with chronic hepatitis C.

About a decade ago, Gilead Sciences developed Sovaldi and other drugs, new antiviral pills that can cure most cases of hepatitis C. The California-based pharmaceutical company planned to expand access to the new medications in India through a combination of branded and generic versions. “It is in that context when I was contacted,” recalls V. “Seenu” Srinivasan, professor emeritus of marketing at Stanford Graduate School of Business. “Gilead wanted to know how much value they were creating in India both by the branded drugs and the generic ones.”

In the marketing research field, Srinivasan is known for co-creating conjoint analysis, a statistical method for assessing how consumers prioritize multiple attributes of a product. Although measuring the social and economic value of health care was new to him, Srinivasan soon found that his background was a good fit for this type of research.

In 2016, about nine months after Gilead introduced Sovaldi in India, Srinivasan and two colleagues from Harvard University launched a study to assess the potential impact of this new class of antivirals. After developing a novel way to calculate this, they forecasted that the rollout of these life-saving treatments would create $11.5 billion of incremental value over five years — a significant figure in a country with an annual per capita GDP of around $1,940.

A Broader Measure of Value

Getting new medicines to low-income populations in developing countries is an important health care priority, but it can be frustratingly slow. Faced with tight budgets, governments must prioritize which drugs are licensed and subsidized. If policymakers balk at the cost, the distribution of effective drugs may go underfunded, leaving the most vulnerable people untreated.

For many years, researchers have assessed the potential impact of new drugs through two lenses. Cost-effectiveness analysis measures the life-years gained or saved. The second method is benefit-cost analysis, which measures the monetary value of a drug’s benefits and subtracts treatment costs.

Yet these approaches don’t fully reflect how drugs are used in real-life situations or how doctors prescribe them. In the case of hepatitis C, doctors’ choice of prescription depends on which genotype of the virus a patient has, their level of liver damage, and whether they have end-stage renal disease. Even for the same patient type, physicians’ prescriptions differ.

Together with David Bloom and Alex Khoury from Harvard’s Chan School of Public Health, Srinivasan developed a statistical model to forecast what fraction of physicians in India would prescribe the new drugs. It also calculated the value generated for patients, taking into account the likely number of years added to their lives minus the costs of the treatment and side effects. They quantified each of these in dollar terms and arrived at a net value.

They proposed the measurement of incremental value, which compares the outcomes of two scenarios: one in which patients are treated only with previously existing drugs, and another in which they have access to a wider range of treatments as new drugs are introduced. It was important to capture doctors’ sometimes unexpected decision-making in these scenarios. The study found that some doctors still preferred traditional hepatitis C treatments even after new treatments were available. Doctors were also found to weigh non-medical considerations, such as whether they think a patient can afford a particular treatment. “That was an important lesson for me,” Srinivasan says.

Modeling Real-World Impact

To get a representative sample of doctors and build realistic scenarios, Srinivasan’s team brought in the market measurement firm Nielsen, which collected data from 500 Indian physicians. The hypothetical time horizon for the simulation started before March 2015, when none of Gilead’s drugs was available in India. In that scenario, about 19% of patients would receive no treatment. In another scenario in which a handful of new medications were introduced, the untreated population dropped to 13%.

Between May 2017 and November 2020, when all the Gilead drugs had been introduced, the proportion of untreated patients dropped to less than 6%. A 50-year-old male suffering from the less severe type of cirrhosis would be cured by the treatment and could expect to live 13 more years to be 74.

The incremental value of the drugs was substantial. Combining the value of a cured patient with the value of not infecting others, the researchers forecasted that the new antiviral pills would generate between $6.5 billion and $22.5 billion of social value. Under the most realistic scenario, it would be $11.5 billion, with $1.6 billion of that amount generated by the addition of generics. These findings bolster the Indian government’s 2018 announcement that it would distribute the new hepatitis C drugs for free.

For Srinivasan, this project opened his eyes to how a consumer goods approach to measuring preferences could be adapted to medical research. He would like to see more researchers utilize this approach to forecast the benefits of providing access to new drugs. “I hope also that this will spark a conversation among academic researchers around the world about incremental value rather than value,” he says. He also thinks the model can be applied widely. “I think it’s potentially useful in both developing and developed countries as one criterion in deciding prioritization of licensing drugs.”