insulin

Getting Medical Interventions Approved By Government

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Having played for a few years at the nexus of academia, big pharma, and government, it is interesting to watch how these three bodies orbit each other and interact (for those of you familiar with the Three-Body Problem you know such a system is often unstable, but I digress). It is often frustrating, being a person with type 1 diabetes, knowing there are, for example, medications out there with overwhelming evidence behind them but which are not funded by government national health programs. GLP-1s are a great example. These are well established to provide benefit to people with type 1 diabetes but are still, certainly in Australia, not covered and financially out of reach for those who would benefit.

So, what does it take to get something approved for government subsidy? I see four major factors which are necessary, and I thought I would write this article to describe them. For context, I will use the subsidy of continuous glucose monitors (CGMs) for people with type 1 diabetes (T1Ds) in Australia as a case example.

The ‘Ask’

The first step is a clearly articulated ask: “We would like the government to fund/subsidise <x>”. In the case of CGMs for T1Ds, the ask is “We would like the government to fund/subsidise CGMs for T1Ds”. In reality, the ask for was for a specific subset, and the program was expanded to all T1Ds over time (for reasons explained later), but I will keep things simple to illustrate the point.

It is, at this stage, where we can answer why GLP-1s are not available for T1Ds in Australia. The fact is, in the case of Ozempic, Novo Nordisk has not asked because they have no need to. Demand is outstripping supply such that Novo can set the price as they wish and run their factories at full speed. Asking will create a new audience which they cannot serve and force a negotiation on price. There is no motivation to do this until general demand abates or supply can be scaled up.

The Medical Benefit

The ‘Ask’ needs to be justified by a tangible benefit which can encompass quality of life measures and overall health measures. In the case of CGMs for T1D, subsidy would allow more people to access the technology and significantly reduce the need for finger pricking. Near-real-time tracking of glucose levels would reduce the risk of serious hypoglycaemia and hospitalisation. All good things and a great improvement for the lives of people living with type 1 diabetes.

The Medical Benefit provides the “So What?” element to the request and can be very emotive. A piece of wisdom often used in sales is people make decisions on how they feel about the purchase and justify it with logic after the fact. The Medical Benefit is the door to stirring the heart of government to act and, in turn, makes for an emotive tale for voters for justify the spend and show the government cares.

The Evidence

A good tale still needs evidence. Aristotle’s ‘Art of Rhetoric’ speaks of three key elements necessary to persuade:

  • Ethos: The argument should be from a credible body
  • Pathos: The argument should stir the emotions of the receiver
  • Logos: The argument should be based in reason

I have taken it as given that, if a request is being made, for it to get the ear of the government, it will need to be from a credible source (Ethos). The Medical Benefit is the key to providing Pathos and clinical/world evidence is the key to Logos. Without evidence, it is a stirring tale but an unjustified one. A government needs to back decisions on reason otherwise the opposition will exploit the weakness.

In the case of CGMs for T1Ds, there was a wealth of clinical evidence showing improved health outcomes with the use of CGMs. I gave an example of this back in 2021 in the context of PWD pregnancy and the use of CGMs.

The Economic Benefit

Governments are required to be fiscally responsible because of scarce resources. In short, if the government is to spend a dollar, it needs to do so on whatever yields the most benefit. For medical interventions, economic justification is usually measured in cost per QALY (Quality-Adjusted Life Years). Basically, a threshold needs to be met where the cost generates a minimum level of benefit which, in this case is a longer and better quality of life for the individual.

The threshold varies from country to country and, while there is no official limit in Australia, a general rule of thumb is government will consider a medical intervention which costs less than $50,000 per quality adjusted life year.

In the case of CGMs for T1D, the economic numbers came out at around $30-35k per QALY and the request was approved.

Case Example: Automatic Insulin Delivery for T1Ds

Let us consider a couple of items being discussed in the diabetes community at this time. The first is equitable access to insulin pumps and, by extension, looping (Automatic Insulin Delivery aka AID) for all Australians. In Australia, while CGMs are subsidised by the government for T1Ds, pumps are not and need to either be purchased directly or obtained through private health insurance. Given a pump costs literally thousands of dollars this puts it out of the reach of many Australians and I recently helped co-author a petition to help get this changed which I encourage you to sign (QR code below).

So, based on the four elements mentioned above, how would we expect the request to fare if raised by a credible body to government as part of, say, the Inquiry Into Diabetes?

The ‘Ask’: This is relatively straightforward i.e. “AID systems should be subsidized for T1Ds”

The Benefit: Lots of qualitative and quantitative benefits to AID systems such as better overnight control, improved HbA1c and Time in Range, and less manual intervention by the T1D.

The Evidence: Lots of clinical and real-world evidence to support this from companies such as Medtronic.

The Economic Benefit: While it would be great to subsidise AID systems for all T1Ds, to get the appropriate cost per QALY, it may be necessary to pick a sub-group e.g. children (who consistently have higher HbA1cs than their older counterparts in trials), T1Ds who are pregnant and so on. Generally, the cohort selection will be driven by The Evidence as this informs the economic modelling. In other words, if there are no studies of AID systems helping pregnant T1Ds, it will be harder to economically justify their inclusion.

Case Example: CGMs for People with Type 2 Diabetes (T2Ds)

On the back of the benefits seen in T1Ds with the use of CGMs it makes sense to expand the program to T2Ds.

The ‘Ask’: Again, this is relatively straightforward: “CGMs should be subsidised for T2Ds”

The Benefit: T2Ds being able to see how food affects them will inform eating habits and improve health outcomes.

The Evidence: There is some evidence of benefit for insulin dependent T2Ds but the body of evidence for general benefit is still being gathered with limited long-term studies to justify The ‘Ask’. To quote this last paper, literally written this year, “…few studies reported on important clinical outcomes, such as adverse events, emergency department use, or hospitalization. Longer term studies are needed to determine if the short-term improvements in glucose control leads to improvements in clinically important outcomes”

The Economic Benefit: Without evidence to act as a foundation for the modelling, it is harder (not impossible) to determine a cost per QALY. The best hope would be to exclusively focus on where there is evidence (insulin-dependent T2Ds) with the hope that, as more evidence is acquired, the program can be expanded to the wider T2D population.

Conclusions

It is easy for us to be frustrated with the glacial movement of governments to get behind the latest advances in technology and medicine when it is clear, for those of us at the coalface, there are benefits to many, many people. However, as we can see, with competing priorities, it is important government spending is used as effectively as possible.

The inclusion of medical interventions in national health programs requires collaboration of all three bodies (big pharma to provide the intervention, academia to show it works, and government to provide the funding) and the will by all of them to drive it. Without this willingness and the elements mentioned above it is hard, if not impossible, to achieve success.

ATTD2024: Analysing and Understanding Complex CGM Data for Informed Clinical Decision Making

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I had the opportunity this weekend to catch up on some of the presentations I missed at ATTD2024. I struck gold with the first one I watched. Dr Pratik Choudhary spoke on interpreting CGM data and, as I am in the process of building a bot to do this, it was a talk of great interest. The tips and tricks he gave for reading Ambulatory Glucose Profiles (AGPs) and the rest of the CGM report, I believe, are useful to anyone running a CGM.

Structural vs Behavioral Factors

Dr Choudhary considers two broad levers to improve what he sees in the data: Structural and Behavioral Factors.

For Structural Factors, Dr Choudhary applies general formulae to give a guide on what the values should tend towards if there is an issue.

For people who bolus for meals, his general rule is a 50:50 split of daily units used for bolusing and daily units used for basal insulin. He mentioned exceptions to this rule such as people running hybrid loops and low-carbers who will have a much higher percentage of basal and those who micro-manage their glucose levels, giving themselves boluses many times a day, who will have a higher bolus percentage. In these cases, if it is working, he leaves it alone but if it is not, the 50:50 rule is his guide.

For his benchmark carbohydrate ratio he uses 350 / Total Daily Dose and for the correction factor, 120 / Total Daily Dose. He refers to these are rules but ‘guideline’ is probably a better term.

Dr Choudhary’s Flowchart

His position is, if the Bolus/Basal doses are as expected, the levers to pull are likely to be behavioral.

Showing an example, he presented a report for a person with diabetes (PWD) with top-heavy bolusing, based on the 50:50 rule. His experience is for top-heavy ratios, the PWD will often have a lot of variability. Considering the actual basal (32u/day) vs expected (39u/day) it suggests there is not enough basal, putting upward pressure on the CGM, rising between meals, which is confirmed in the graph.

The Insulin to Carb Ratio (ICR) was 5g/U whereas the predicted was 4.5 g/U suggesting a unit of insulin does not cover as much of a meal as the PWD thought. The Insulin Sensitivity Factor/Correction Factor was 2 mmol/L (1 unit of insulin drops blood glucose by 2mmol/L) whereas the predicted Correction Factor was 1.5 mmol/L suggesting insufficient insulin is being given corrections.

Sure enough, we see that the PWD is going high in the day and staying there, likely because they are not administering sufficient insulin due to bad factors/ratios, with the problem amplified by insufficient basal.

Behavioral Indicators

Dr Choudhary mentioned that for people without diabetes typical glucose variability is usually 15-20%. For PWDs between 20-35% they are generally looping or bolusing well. For people with a variability of 35-50% Dr Choudhary suggests this is a mismatch between food and bolus and so, in the case of high variability, he believes changing settings is not going to help. He also mentioned that any time a PWD can bolus 15 minutes before a meal is ‘free HbA1c/Time in Range and lower variability’.

The light blue area of the AGP relates to events which happen once every week or every fortnight, suggesting they are behavioral (or lifestyle driven e.g. an exercise day/Sunday roast/takeout night) more than systematic. Dark blue is more common and therefore more influenced by structural factors.

Where to Set the High Alarm

A great tip Dr Choudhary gave for setting the high alarm is at the top of the dark blue region on the AGP. When your blood sugar goes over this it is, by definition, an unusual event which may need intervention whereas anything below this is business as usual.

For low alarms, he warned setting them too high as it encourages the PWD to stay high, costing Time in Range. For myself, I set it to 3.3mmol/L (59mg/dL). For the five years post-diagnosis that I was insulin-independent, I wore a CGM and often dipped into the high 3s (high 60s) and got as low as 3.4mmol/L but never any lower. Therefore, below 3.4mmol/L, for me, is abnormal and worthy of attention.

Good Numbers but at What Cost?

Dr Choudhary also showed someone with excellent control due to frequent management. His recommendation for someone bolusing is to “scan, inject, eat and forget” meaning that once you have bolused and eaten, the trajectory of the blood glucose is set and there is very little which can change it. If the peak occurs around one hour after eating, you matched the bolus to the food. The level after two hours will indicate whether the bolus was enough to cover the food. After three hours may show rises influenced by protein and fats but he suggested there was little point in monitoring glucose movement around meals more frequently than this.

He also suggested that frequent management driven by fear of long-term complication may be ill-founded in some cases. From the DCCT study he cited that if a PWD has not had complication interventions in 20 years since diagnosis, their change of getting a life-changing complication for the rest of their life (loss of organ/sight/limb) is negligible. Therefore, if a person with this level of aggressive management is burning out after 20 years of micro-management the evidence supports the option of relaxing the regimen somewhat without significant consequence.

How Do I Fare?

As it happens I have just seen my Endo and my HbA1c has crept up to 5.9% so I am keen for suggestions on improvement (even though I am still well below the threshold for significant risk of complication ).

In terms of the structural settings, my Total Daily Dose for the last 3 months averaged 88U/day. However, as I run my CamAPS Ypsopump completely closed I do not bolus for meals and let the pump take care of it. Therefore, I cannot assess whether I am near the suggested 50:50 ratio. However, based on my profile numbers, informed by Autotune, I am a lot more ‘basal heavy’ than the 50:50 guideline would suggest, backing up Dr Choudhary’s position that it does not strongly apply to loops.

For 88U, the expected ICR is 4.0 g/U and the ISF is 1.4 mmol/L. Again, as CamAPS has automatically adjusted these values for me automatically as part of the loop I cannot check it directly but these values closely align to the default profile values in my pump which I determined using Android APS and Autotune, prior to switching to Ypso a couple of years ago.

My Glucose Variability is currently 31% which is in the typical range for someone looping but, as it is towards the higher end of the range suggests a slight mismatch between bolus and food. Given I let the algorithm do it all, this makes sense as I do not even declare I am eating, letting the loop figure it out for itself.

Looking at the dark blue, we can see a mismatch between insulin and dinner. As the family has roughly the same dinner on a given night of the week it would be interesting to see if the light blue is caused by “Lomo Saltado Lunes”, “Taco Tuesday”, or “Takeout Friday”. I will have to see if it is possible to generate AGPs for a given day of the week over a period on Nightscout…

My Endo has suggested providing some intervention to help the loop with dinner. CamAPS has a Boost function which I will activate at the start of dinner for two hours, giving the loop permission to be more aggressive, to see if this helps. Failing this there are plenty more levers to pull (restricting carb intake or, heaven forbid, giving a bolus).

As mentioned, my low alarm is set to 3.3mmol/L. You will see in the above stats that in the last 3 months it recorded 99 hypo values. These are, almost without exception, a new G6 sensor needing calibration. On the rare occasion I skip a meal and confuse the loop, I might skim the low alarm but it is nothing a few jelly beans cannot fix.

At the moment, my high alarm is set to 15 mmol/L (270mg/dL) but, based on Dr Choudhary’s advice, it should probably come down to 10mmil/L (180 mg/dL) if I was planning to intervene (at this stage I do not do correction doses either).