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3T Arbitrage and Solving Cancer

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Cancer sucks.

 According to the CDC:

“In the United States in 2020, 1,603,844 new cancer cases were reported and 602,347 people died of cancer. For every 100,000 people, 403 new cancer cases were reported and 144 people died of cancer. 2020 is the latest year for which incidence data are available.”

If you’re an adult today, there’s a nearly 40% chance you will be diagnosed with cancer in your life.

No matter who you are or where you live, it’s almost guaranteed someone you know has been affected by cancer. But what if there was a solution to eradicate cancer by 80% or more?

Moreover, this panacea would reduce the cost of cancer care by over 90%. This solution wouldn’t require intense chemotherapy, side effects, or ongoing nursing care.

If there were a ‘magic pill’ what would it cost?

To solve that, you have to understand that there’s a massive economic burden once someone is diagnosed. The number of variables looks something like this:

In a scenario where ‘you’ don’t have cancer – ‘you’ are likely paying for care of the sick. Someone pays for the sick care – we just spread out the costs over the working populace.

In fact, less than 10% of a patient’s costs are borne by themselves – with the majority of spending coming from insurance and government programs. This may seem wrong, but cancer care is incredibly expensive.

So the real question is, of the 1.6 million Americans diagnosed each year and around 5 million going diagnosed and going through treatment – what is the actual cost when we add these groups in total.

To figure this out, let’s look to the Southeast Asian country of Malaysia. They came out with an incredible study looking into the breakdown of costs related to cancer patients.

What they find is that direct non-medical costs and indirect costs account for ~80% of the costs. While Malaysian is a socialized healthcare system, we can understand that:

a) Indirect and direct non-medical costs are ~2-3x the direct medical costs

b) Medical care costs associated with cancer survivorship is ~$183B

While we don’t want to handwave high numbers, there’s a low-end cost in the US that we could assume are $330B. This makes numbers easy.

There are 330M people living in the US, therefore the cost borne out each year is ~$1,000 per person. If we adjust this data for paying individuals, we first change the denominators to 131M (households) and then 25% of that number (25% of HH pay 80% of taxes).

Therefore, let’s do the following:

$330B / (131M x 25%) = ~$10k / household

This is staggering. I haven’t thus far found anyone put these figures together.

While people protest social programs, military budgets, and education costs – most would be comfortable to spend it on healthcare for cancer. But that’s not the point.

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Could we spend money now to reduce money spent tomorrow?

To solve this, let’s think about buying a boatload of 3T MRI scanners. These MRIs can detect cancer at stage 1 and 2 along with pre-screening for strokes, aneurysms, and other complications.

These also cost ~$1M used or about $2M new.

Let’s use $1.5M when buying in bulk and pause – that’s our direct cost per site.

Next, let’s focus on getting the most bang for our buck. We should first look at state specific cancer rates, household income, and population density (low density states are difficult to deploy).

The best performing states looking across these are NJ, CT, MD, NY, NH, MA, and IL. Interestingly enough, GA, MN, PA, WI, and OH are the next best states. Let’s use these 14 states plus DC to get a really powerful use case.

Also, we’d want to go directly to the top 20-50 cities but that will come later.

Let’s provide 1 for every million people. That would have the top 15 states and DC see 107 machines. This would cost taxpayers ~$160M.

But now, could we charge $250 / year for this per person? Currently, the top two companies in NY charge $2,500 per scan. Regular MRIs cost closer to $500 per scan BUT they are needed ad-hoc.

Regular MRIs have about a 15% profit margin – with capex about 14%.

I'm going to go out on a limb and assume that with a subscription model and high utilization, we could achieve better margins. Let's say we can achieve a 25% profit margin. With 1 million people paying $250 per year, that's $250 million in revenue per machine annually. At a 25% margin, that's $62.5 million in profit per machine per year.

Now, let's consider the potential impact:

1. Early Detection: By providing widespread access to 3T MRI scans, we could dramatically increase early detection rates for cancer. Studies have shown that early detection can significantly improve survival rates and reduce treatment costs.

2. Cost Savings: If we can detect cancer at stages 1 and 2, we can potentially avoid the enormous costs associated with late-stage cancer treatment. Remember, we estimated the total cost of cancer care in the US at around $330 billion annually.

3. Additional Health Benefits: These scans could also detect other health issues like aneurysms and potential strokes, providing even more value and potentially saving more lives.

4. Economic Impact: By reducing the overall burden of cancer care, we could potentially save thousands of dollars per household annually.

Let's run some numbers:

• Initial investment for 107 machines: $160.5 million

• Annual revenue (assuming full utilization): $26.75 billion

• Annual profit: $6.69 billion

Even if we only achieved 50% utilization, the annual profit would be $3.34 billion. This means the initial investment would be recouped in less than a month, even at half capacity.

Now, let's consider the potential savings:

If we could reduce cancer care costs by even 30% through early detection, that would save approximately $99 billion annually. This is a conservative estimate, as early detection often leads to much more significant cost reductions.

The societal impact could be immense:

1. Reduced financial burden on families dealing with cancer

2. Decreased strain on the healthcare system

3. Improved quality of life for millions of people

4. Potential for reinvestment of savings into further medical research and prevention

Challenges and Considerations:

1. Implementation: Rolling out this many MRI machines would require significant logistical planning and coordination.

2. Training: We'd need to train technicians and radiologists to handle the increased volume of scans.

3. False Positives: We'd need to consider the potential for false positives and how to manage them without causing undue stress or unnecessary procedures.

4. Privacy Concerns: Handling this much medical data would require robust privacy protections.

Conclusion:

While this proposal might seem ambitious, the potential benefits far outweigh the costs. By leveraging technology and taking a proactive approach to cancer detection, we could potentially save millions of lives and billions of dollars. The initial investment is significant, but the returns - both financial and in terms of human wellbeing - could be staggering.

This approach represents a paradigm shift in how we think about healthcare, moving from reactive treatment to proactive prevention. It's not just about saving money; it's about saving lives and improving the quality of life for millions of people.

The question isn't whether we can afford to do this. Given the current costs of cancer care, the real question is: can we afford not to?

Source:

1. https://medlineplus.gov/cancerlivingwithcancer.html

2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191884/

3. https://progressreport.cancer.gov/after/economic_burden

4. https://pubmed.ncbi.nlm.nih.gov/32522832/

5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9514601/