April 24, 2018
Interactive Tool: Disease Modifying Therapies Drove 82% of Total Increase in Health Care Spending for People with Multiple Sclerosis
Bill Johnson; John Hargraves; Sally Rodriguez
In a recent issue brief, HCCI found that the already high cost of care for people with multiple sclerosis (MS) rose dramatically over the past several years. The primary driver was the increasing cost of a small group of prescription drugs called Disease Modifying Therapies (DMTs). To illustrate the role prescription drug prices play in driving overall health care spending for people with MS, we developed an interactive tool that allows users to compare changes in prices and spending for specific DMTs.
Spending for the average person with MS grew 66% from $23,890 in 2009 to $39,628 in 2015, and DMTs accounted for 82% of this increase. Per person spending on DMTs more than doubled from 2009 to 2015, even as use of DMTs per person decreased slightly over the same period. Despite the introduction of new, oral DMT options, the price of every single DMT increased by at least 9% per year between 2009 and 2015.
Our interactive tool allows you to compare changes in prices between specific DMTs. For example, some notable DMT price trends include:
- Price increases were consistent for both new oral DMTs that came to market during the study period and older treatments.
- The cost of a month’s supply of Copaxone, a treatment launched in 1997, increased by 164% from 2009 to 2015 from $2,326 to $6,151.
- The rate of price increases for a month’s supply was similar across older, injectable DMTs, ranging from 24% per year for Avonex to 27% per year for Copaxone.
How to use the tool:
The top panel of the graph shows the total per person spending on DMTs for people with MS, demonstrating the rise from 2009 to 2015. The bottom panel tracks the average cost – or price – of a month’s supply for each DMT. Spending and price of individual drugs can be highlighted using the legend or clicking the chart. The trends of DMT prices can also be adjusted for inflation using the buttons located in the bottom right corner.
All the data used to construct these charts can be downloaded here.
Using HCCI claims data from 2009 to 2015, we flagged individuals diagnosed with multiple sclerosis (MS). Once flagged, individuals remained flagged for the duration of our sample. We omitted claims incurred prior to the initial diagnosis. We limited our sample to individuals flagged with an MS diagnosis with 12 months of continuous insurance enrollment and prescription drug coverage. Our sample is best thought of as a repeated cross section ranging from 42,279 people in 2009 to 58,608 in 2015. We aggregate total spending – the sum of payer spending and individuals’ out-of-pocket spending – on disease modifying therapies (DMTs). We categorized drugs as DMTs in accordance with Hartung et al. (2015), and following publications from the National Multiple Sclerosis Society. We group all other injectable DMTs taken by people in our sample but not separated in this analysis as “other injectable DMTs” (Extavia, Glatopa, and Plegridy). Consistent with our accompanying issue brief, we omit infused DMTs (Tysabri, Lemtrada, and Novantrone) from this analysis.
To measure average cost of each DMT, we computed total spending per month’s supply. Total spending refers to the sum of negotiated payer spending plus individuals’ out-of-pocket spending. Consequently, the average cost we report is best thought of as a measure of negotiated price. To address potential outliers, we limited the subset of our sample to claims for the most common amount of filled days, for each NDC code, for each DMT. To account for the possibility that drugs change formulations over time, for each DMT we used the most frequently found NDC code in our sample. For each claim, we divided total spending by the number of prescription-filled days. We subsequently used the quarterly average for each DMT to compute the average cost per filled day for each DMT. Finally, to compute the cost of a month’s supply we multiplied the average cost per filled day by the most common amount of filled days, for each NDC code, for each DMT.
We focus on total health care spending – the sum of payer spending and individuals’ out-of-pocket spending. Total health care spending on prescription drugs cannot account for any rebates received by payers for prescription drugs. We also cannot account for any out-of-pocket assistance received by patients which may affect their true out-of-pocket burden. Consequently, the changes in total spending we report here may overstate the true changes in the cost of care payers face in covering individuals with MS, and individuals may face to cover the cost of their own care.
We allow users to either view nominal changes in the cost of each DMT or to adjust for inflation. We adjusted for inflation by converting the cost of each DMT to 2015 dollars using the Consumer Price Index for All Urban Consumers: All Items. The data we used is available online from the Federal Reserve Bank of St. Louis.
February 21, 2018
How Common is Your Health Care Spending?
Bill Johnson; John Hargraves
It is well documented that Americans spend a lot on health care, but this issue is often discussed in terms of share of GDP, billions of dollars, or an astoundingly high hospital bill. These numbers can be hard to relate to; it’s hard to imagine billions of dollars, let alone a share of the national economy. A pricey bill may get our attention, but can seem like a special case that doesn’t relate to our own health care spending experience. Many people may not know how much they will end up spending on health care in an upcoming year, let alone how their spending compares to others’. To address some of these questions, HCCI developed a tool to show how common a given amount of annual health care spending is, for people under 65 with commercial health insurance.
We found that most people spend over $1,000 a year, and while health care spending above $20,000 in a year is rare, it was experienced by 10% of people in a three-year period. Our findings highlight the unpredictable nature of health care spending and build on the recent HCCI issue brief and accompanying NEJM Catalyst piece, which documented the substantial turnover among the group of top health care spenders. Within a single year, a majority of spending is incurred by a small number of people, but the individuals that make up this group change substantially over time.
We invite you to use our interactive graph tool to explore how common different levels of health care spending are by age group and gender. While the numbers in this graph tool are not meant to be interpreted as specific predictions, we hope they provide relatable examples of how individuals experienced substantial health care spending over various time periods.
How to use and interpret the chart:
- Select a spending threshold
- Select Gender
- Select Age
The first bar shows the percent of people who spent at least the threshold amount on health care in 2013. The second bar shows the percent who spent at least the threshold amount in 2013 and/or 2014. The third bar shows the percent who spent at least the threshold amount in 2013, 2014 and/or 2015.
Using the HCCI claims data, we compiled a panel of individuals with continuous enrollment in the same commercial insurance plan from 2013-2015. We restricted our analysis to plans in the small or large group insurance markets. We further limited our sample to people with three full years of prescription drug coverage. We aggregated all health care spending by individuals within each calendar year. All numbers here refer to the total health care spending by an individual – the sum of payer spending and an individual’s out-of-pocket spending. We define an individual’s age as it was in 2013. All dollar values are nominal.
February 14, 2018
Heart to Heart
A Valentine’s Day-Themed Health Care Spending Map
Amanda Frost, John Hargraves
Valentine’s Day is the second-most popular day to pop the question, and millions of couples are expected to get engaged today. According to The Knot’s 2017 Jewelry and Engagement Study, the average national cost for an engagement ring in the U.S. is $6,351, just under $1,000 more than average national health care spending per person ($5,407 in 2016). And just like average health care costs, the average cost of a ring varies by state. In honor of the holiday, we’ve compared how much people spend on health and how much they spend on engagement rings across all 50 states and the District of Columbia. Where are people spending more on a ring then on health care? The map below shows the difference between engagement ring and health care costs; the pink states spent more on rings, the blue states spent more on health.
Health care spending data is the average per person spending on all health care services in 2016 for people with employer-sponsored insurance (ESI) and ages 0-64. Spending data by state are from the 2016 Health Care Cost and Utilization Report and Report Appendix Table A58. Please see Report and Methodology document on the HCCI website for more information about the data and methods. Engagement ring data is from The Knot’s 2017 Jewelry and Engagement Study, state-based data gathered from Business Insider.
December 20, 2017
Workers in low income counties more likely to be long-term opioid users
Chao Zhou; Kevin Kennedy; John Hargraves
Past literature has found links between higher opioid use and local economic conditions for people enrolled in public health programs, but there has been little discussion of whether this relationship occurs among the privately insured. Using HCCI claims data and county level income data from the US Census Bureau, we examined how a county’s median household income relates to long-term opioid use among the county’s working adult population with private health insurance in 2015. In analyzing over 1,500 counties, we found that employed adults (over the age of 18) living in counties with lower median incomes had greater rates of long-term opioid use (having filled at least 6 prescriptions for opioids) compared to employed adults in higher income counties. Nationally, 2.4% of employed adults with private health insurance were long-term opioid users in 2015. In the lowest median income counties ($30k or less), 4.9% of the employed adult population were long-term opioid users, with some counties having rates as high as 15.8%. In contrast, just 1.3% of the same population in the highest median income counties ($100k or more) were long-term opioid users and the highest rate in these counties was 2.3%.
We limited our study population to patients in the HCCI commercial claims data who had continuous medical and drug coverage throughout 2015, were the employee or subscriber of their health plan (no spouses or dependents), and over the age of 18. We restricted our sample to counties with 200 or more claims for opioid prescriptions, resulting in a final population of 7.3 million people across 1,539 counties.We obtained county median household income for 2015 from the US Census Bureau’s American Community Survey, 2010–2015. Members’ county of residence was determined based on their ZIP code. For ZIP codes that cross county boundaries, members were attributed to the county that contains the majority of the ZIP code population.
From HCCI prescription drug claims we extracted all filled opioid prescriptions for our study population. Opioids were identiﬁed from the Centers for Disease Control and Prevention’s (CDC) compilation of opioid formulations. We define
long-term opioid users as people with at least 6 prescriptions in a year.
December 4, 2017
ER spending increased 85%, driven by price increases for the most severe cases (2009-2015)
Kevin Kennedy; John Hargraves
Medical bills from the Emergency Room (ER) are a mystery to many patients in the US health system. From incredibly high, varying charges to surprise bills resulting from in/out of network confusion, many Americans have no idea what to expect when it comes to the cost of this necessary service. Recently, Vox reporter, Sarah Kliff, has begun collecting ER bills in an attempt to “bring transparency to these extremely common but little understood fees”. To further the discussion, HCCI is releasing its first freely available data download! Using HCCI’s vast commercial claims dataset, we examined the 5, successive Current Procedural Terminology (CPT) codes for an ER visit which are designed to capture the level of severity and complexity of the ER visit. Analyzed collectively, we used these codes to explore how spending, utilization, and prices for ER visits have changed from 2009 through 2015.
Spending per member on all ER visits increased 85% between 2009 and 2015, largely due to the more than 100% growth in spending on high severity cases. ER spending growth was driven by price increases. High severity visits had the highest prices and the greatest price growth; the price for the most severe ER visit rose over $400, from $498 in 2009 to $900 in 2015. While overall use of the ER remained constant, there was a significant shift in case mix from low to high severity visits – further magnifying the effects of high severity price increases on spending.
The average price of an ER visit increased in every state. Some states saw price increases well over 100%, while price growth was half the national rate in others. The downloadable dataset alongside this post allows for all types of state-level comparisons across years, severities, and measures.
The measures reported are based on ER procedure codes (CPT codes 99281-99285) for HCCI’s ESI population. They do not include the costs of other services patients received during their visit to the ER such as the cost of an injected drug. Therefore, these price and spending measures may not capture the entirety of what is typically thought of as an ER visit. Patients may often receive multiple ER CPT codes during the same visit. The yearly proportion of visits with multiple codes remained constant throughout the study period. When aggregated to the visit level, with each visit classified as high, dual, or low severity depending on the code(s) billed, we continued to see an increase in high severity, decrease in low severity, alongside a decline in overall ER visits. Average spending and prices are calculated from the actual amount paid for each claim line containing one of the 5 ER CPT codes. For more information about the HCCI dataset and the methodology of the calculations, see the Methodology page on the HCCI website.
November 29, 2017
Price of insulin prescription doubled between 2012 and 2016
Amanda Frost; John Hargraves
In honor of National Diabetes Month, our inaugural blog post focuses on a topic of particular interest to people with diabetes: the price of insulin. Insulin is the hormone responsible for the body’s ability to use sugar and prevent dangerously high and potentially deadly levels of blood sugar. Diabetics are unable to make enough insulin to support their bodies’ needs, and thus many are dependent on prescription insulin for daily blood sugar maintenance or to control occasional blood sugar spikes Recent reports by CBS News, Business Insider, Vox, the Washington Post, and others voice concern about increasing insulin prices. Using HCCI’s health care claims data, we found that the average price of an insulin prescription nearly doubled nationally between 2012 and 2016, with a lot of price variation across states.
November 29, 2017
Welcome to the HCCI blog – Healthy Bytes!
We’ve launched a blog to expand the way in which we share findings and insights based on our data with the wider world. While we remain committed to our signature publications such as our annual Health Care Cost and Utilization Report and our Issue and Data Briefs, sometimes a finding is just too interesting to wait for a more formal publication. We would love feedback from readers on the numbers we are publishing (or not publishing) on the blog. If there’s a particular topic you think HCCI should focus on, please let us know. We can’t commit to following up on every suggestion but we would love to interact more with others who are equally passionate about health care spending and utilization data.
~ Niall Brennan, President