Estimating the number of people with diabetes impacted by the insulin co-pay cap bills
With over twenty states working on legislation, it's time to arm ourselves with more data.
Hi and welcome to the latest edition of Data for Insulin.
If you’re new, please 👇.
Over six months ago, I created an analysis showing the preliminary impact insulin co-pay caps would have if implemented at the state level. Back then, my main focus was outlining insurance plans that would be impacted. Many of us, myself included before diving into this, didn’t understand the nuances of the insurance market and just how fractured it is. We didn’t necessarily understand that states actually don’t have the authority to regulate insurance plans for everyone who lives or works in their state.
As a movement, we now understand that 18% (Puerto Rico) to 34% (Utah) of people have plans that would be impacted by a co-pay cap bill depending on the state implementing, with an average across all states equal to 27%.
We needed to know the impact of these bills and it’s a real shame those impacts weren’t communicated until after Colorado passed the first bill. And, it’s a real shame that this analysis has to come from me, your random data activist and not from policy creators in the states themselves. What I’ve learned after having multiple state legislators use my original analysis is that even the people proposing this legislation don’t understand the impacts these bills will have.
If the people with enough social clout and wits to get elected to public office don’t understand that these co-pay cap bills have limited impact, how could the average person with diabetes?
It turns out, the bad messaging out of Colorado did have the consequence of leading people to believe they could show up to the pharmacy counter and have their co-pays be $100 a month by simply living in the state. A prediction I explicitly made in July 2019 and have otherwise been talking about since May 2019. I didn’t want to be vindicated.
10 months later, we’ve seen a huge uptick in other states following suit and modelling their bill directly after Colorado’s. To my current count, from my own records and crowd-sourcing from other advocates, the count is at least 35 states that have at least proposed an insulin co-pay cap bill. Now, it’s an uphill battle to improve the messaging and the bills themselves.
My original analysis helped advocates demand better bills and helped state legislators understand the impacts their bills would have. The result has been better bills and better messaging in the media. As a movement, we’re better educated and prepared to fight with having data on our side.
Number of People Impacted
But, the one thing missing from my original analysis was getting to a concrete number of people with diabetes who will actually be impacted by this bill in each state.
While there is little diabetes information available at the state level, I believe it’s incredibly important to estimate the number of people with diabetes impacted. I’m proposing two methodologies to calculate the number of insulin dependent people with diabetes who will be impacted by these co-pay cap bills in each state. These estimates would be considered the ceiling, i.e. an estimate of the maximum number of people who would be impacted.
These methodologies do require generalization.
Methodology #1
Under the first methodology, we can take the number of insulin dependent people with diabetes in the U.S. (widely considered to be 7 Million) and divide that by the number of people in the U.S.
This would get to the percentage of insulin dependent people with diabetes. We would then apply this percentage to each state’s population and to the percentage of people on impacted plan types.
Methodology #2
Alternatively, we can extrapolate the number of insulin dependent people with diabetes by applying a percentage rate of insulin dependency to each state’s population of people with diabetes.
The Center for Disease Control (CDC) publishes a “Diabetes Report Card” that provides the percentage of people in a state who have diabetes. This report card was last published in 2017.
Finding the rate of insulin dependency within the diabetes population proved to be more challenging. But, in a 2006 study, the National Institute of Diabetes and Digestive and Kidney Diseases found that among adults with diabetes 14% take insulin only and an additional 13% take insulin and oral medications. So, we can estimate an insulin dependency rate of 27% and apply that to each state’s population of people with diabetes and to the percentage of people on impacted plan types.

The numbers vary for each state under both methodologies, however, with applying generalizations to each individual state, it’s important to have a range of possibility. These are purely estimates made with the best data we have available to get a sense of the magnitude of impact a state co-pay cap bill would have.
Additional Considerations
The most important thing to note is that these figures are ceilings, i.e. just because someone with diabetes has a plan type that is impacted does not mean that this co-pay cap bill will actually do anything for them.
A positive impact would only apply to a person with an impacted plan under the following situations:
The plan co-pays are over $100 a month (or, the co-pay proposed by the state) or insulin is subject to a deductible,
A person can afford the co-pay proposed by the state but not the amount they pay now, and
Cash flow is their biggest concern or they wouldn’t otherwise meet their deductible or out of pocket maximum.
For many people with diabetes having to pay for supplies, comorbidities and doctor’s visits, they are already meeting their deductibles and maybe their out of pocket max without factoring in insulin. In these cases, the co-pay cap bill really only impacts cash flow and not cash expenditure during a year.
Another update since the first co-pay cap analysis is insurance companies and pharmacy benefit managers are starting to voluntarily introduce policies that cap insulin co-pays to $0. The biggest examples of this are Blue Cross Blue Shield of Minnesota and CVS Caremark (employers must opt-in). With CVS Caremark having a 30% market share, we can argue that the co-pay cap bill doesn’t impact an additional 30% of people who would otherwise be impacted. Of course, employer opt-in is the biggest hurdle.
Because of the limited types of plans impacted and the other factors outlined, it’s hard to say that these bills have a big impact on the diabetes community actually on the ground. However, without being knee deep into this work, that’s difficult conclusion to come to.
One of the biggest fights of our lives will be combating the long term consequences of the co-pay cap bills. Just like we fight back against “what about Walmart insulin”, we’ll have to do the same thing here when legislators move on to other issues because they “capped the insulin co-pays.” We’ll face another round of public education that yes, insulin prices are still a problem. We hopefully won’t lose momentum as some people’s situations improve and they decide that they’ve got theirs and there’s no need to fight anymore. The American Diabetes Association will continue their pattern of taking credit and communicating to the public that they’ve solved the crisis by astroturfing us.
There’s a long road ahead of us to 1. make these bills better, 2. create effective and accurate messaging of the impacts and 3. not lose sight or otherwise give up on hitting true #Insulin4All. We will get there but it won’t be because of these co-pay cap bills.
Special thanks to Sarah Ferguson for trading ideas with me on how to estimate these figures , Hilary Koch who finally convinced me to perform this analysis and Allison Bailey for confirming the number of states with bills introduced.
Edits:
2/21/2020 to reflect the number of states with bills to 35, from 22 per Allison Bailey
2/21/2020 to clarify $100 co-pay cap language to account for states that have introduced bills but their co-pay caps are lower
The 2016 National Diabetes Survey also provides an insulin dependency rate of 27%. https://www.niddk.nih.gov/health-information/health-statistics/diabetes-statistics/national-diabetes-survey