Health Economics: Pharma’s Favorite Crystal Ball

Health economics is to pharma what a crystal ball is to a fortune teller—draped in mystique, full of promise, and often wrong in hilarious and consequential ways. Ostensibly, health economics is the rigorous study of how resources are allocated in healthcare. In practice, it’s the art of making highly specific predictions about deeply uncertain things, all while pretending the numbers mean something unassailable.

From Quality-Adjusted Life Years (QALYs) to Incremental Cost-Effectiveness Ratios (ICERs), the metrics of health economics are supposed to guide rational decision-making. Instead, they often serve as a linguistic shield for corporate agendas, political wrangling, and downright wishful thinking. Let’s take a critical, slightly wry look at the metrics that health economists and pharma executives love—and the absurdities they occasionally produce.

QALYs: The Unicorn of Healthcare Metrics

The Quality-Adjusted Life Year (QALY) is the bread and butter of health economics. It claims to distill a year of perfect health into a single number between 0 (death) and 1 (perfect health). QALYs are then used to determine whether a new treatment offers "value for money."

$$
\text{QALY} = \text{Length of Life} \times \text{Quality of Life Index}
$$

This looks great in theory. A treatment that extends life by 3 years at 0.8 "quality" yields 2.4 QALYs. If the cost of the treatment is $100,000, the cost per QALY is:

$$
\text{Cost per QALY} = \frac{\text{Cost}}{\text{QALYs}} = \frac{100,000}{2.4} \approx 41,667 \, \text{USD/QALY}
$$

The Problem?
The "quality of life index" is notoriously subjective. Is 0.8 the right number for someone on chemotherapy? Who decides? Worse, the math assumes that people value a year of life equally at all stages. Do you value a year at 25 the same as a year at 85? No one asked, but the QALY doesn’t care.

ICER: The Metric Pharma Loves to Game

The Incremental Cost-Effectiveness Ratio (ICER) measures the additional cost of achieving an additional unit of benefit (usually one QALY). It’s calculated as:

$$
\text{ICER} = \frac{\text{Cost of New Treatment} - \text{Cost of Current Standard}}{\text{Effectiveness of New Treatment} - \text{Effectiveness of Current Standard}}
$$

If the ICER falls below a predetermined threshold, the treatment is deemed "cost-effective."

The Problem?
ICERs are highly sensitive to the assumptions baked into the model. Pharma companies are experts at tweaking these inputs to get favorable outcomes. Want a lower ICER? Inflate the effectiveness of your drug just slightly, or choose a "current standard" that’s woefully ineffective. Voilà—your drug is now a bargain!

Budget Impact Models: Optimism Meets Reality

Pharma companies use budget impact models to show that their drugs won’t bankrupt the healthcare system. These models forecast how much a new drug will cost a health system over time, often using assumptions so optimistic they make startup founders look dour.

$$
\text{Budget Impact} = \text{Population Eligible for Drug} \times \text{Uptake Rate} \times \text{Cost per Patient}
$$

The Problem?
The "uptake rate" is a wild guess, and the population size is often exaggerated. Pharma also conveniently ignores indirect costs (e.g., rehospitalizations due to side effects). The result? A rosy picture that bears little resemblance to reality.

Net Present Value (NPV): A Metric for Pharma Accountants

Net Present Value (NPV) is a financial metric that evaluates the profitability of an investment by discounting future cash flows to their present value:

$$
\text{NPV} = \sum_{t=1}^{N} \frac{CF_t}{(1 + r)^t}
$$

Where:

• CFt​ is the cash flow in year t,
• r is the discount rate,
• N is the time horizon.

Pharma uses NPV to decide whether a drug development project is worth pursuing.

The Problem?
NPV depends heavily on the discount rate and projected revenues. Overestimate revenues by just 10%, and suddenly your $500 million dud looks like a blockbuster. Worse, NPV doesn’t account for tail risks—like regulatory rejections or catastrophic side effects—that can wipe out all future cash flows overnight.

Conclusion

Health economics is a powerful tool, but it’s only as good as the assumptions behind it. In its current state, the field often serves as a veneer of scientific rigor over what are essentially guesses. By acknowledging its limitations—and incorporating more robust models—we can make health economics a genuinely useful tool for patients, payers, and policymakers alike.

Until then, let’s take every QALY, ICER, and budget impact model with a healthy dose of skepticism—and maybe a pinch of humor.