According to a survey of more than 1,110 Wisconsin adults, conducted from June 22, 2022, to July
6, 2022, respondents are concerned about prescription drug costs and express a strong desire for
policymakers to enact solutions.
More than half (53%) of survey respondents reported being somewhat or very worried about
affording the cost of prescription drugs. Worry varies substantially by income group, with respondents
in households making less than $75,000 per year1 reporting the most worry (see Figure 1). However,
it is important to note that a large percentage of households making above $75,000 per year also
reported worrying about affording prescription drugs.
In addition to being worried about prescription drug affordability in the future, many Wisconsin
respondents reported experiencing hardship in the prior 12 months due to the cost of prescription
drugs. Indeed, 1 in 4 Wisconsin respondents (25%) reported that they did not fill a prescription, cut
pills in half or skipped a dose of medicine due in the last year due to cost (see Figure 2).
These hardships disproportionately impact certain populations in Wisconsin. People in lower-
income households, those with Wisconsin Medicaid and those who live in rural areas of the state all
reported higher rates of rationing medications (see Table 1). Similarly, Hispanic/Latinx respondents,
respondents of color and respondents with a household member with a disability also reported higher
rates of rationing medications (see Table 2).
As Table 1 shows, respondents living in households earning more than $100,000 per year reported
higher rates of rationing their prescription medicines (by not filling a prescription, cutting pills in half
or skipping a dose of medicine) than respondents living in lower-income households. These hardships
are alarmingly prevalent in middle-income households, as well. Respondents with Wisconsin Medicaid
reported the highest rates of rationing medication compared to other insurance types, followed by
those with private insurance. Finally, respondents living in households with a person with a disability
reported notably higher rates of rationing medication due to cost in the past 12 months compared to
respondents without a disabled household member (see Table 2).
In light of these prescription drug cost concerns—as well as concerns about high healthcare costs
generally2—it is not surprising that Wisconsin respondents were generally dissatisfied with the health
When given more than 20 options, the options cited most frequently as being a “major reason” for
high healthcare costs were:
When it comes to tackling high drug costs, Wisconsin respondents endorsed a number of prescription
Moreover, there is substantial support for government action on drug costs regardless of the
respondents’ political affliation (see Table 3).
While Wisconsin respondents are united in calling for the government to address high drug costs,
they also see a role for themselves:
The high burden of healthcare and prescription drug affordability, along with high levels of support
for change, suggest that elected leaders and other stakeholders need to make addressing this
consumer burden a top priority. Moreover, the COVID crisis has led state residents to take a hard
look at how well health and public health systems are working for them, with strong support for a
wide variety of actions. Annual surveys can help assess whether or not progress is being made.
The Altarum Healthcare Value Hub's Consumer Healthcare Experience State Survey (CHESS) is designed to elicit respondents’ unbiased views on a wide range of health system issues, including confidence using the health system, financial burden and views on fixes that might be needed.
The survey used a web panel from Dynata with a demographically balanced sample of approximately 1,196 respondents who live in Wisconsin. The survey was conducted in English or Spanish and restricted to adults ages 18 and older. Respondents who finished the survey in less than half the median time were excluded from the final sample, leaving 1,113 cases for analysis. After those exclusions, the demographic composition of respondents was as follows, although not all demographic information has complete response rates: