Monday, September 30, 2013

Fwd: qotd: Is tailoring enrollment strategies to match dynamics of uninsured a solution?

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-------- Original Message --------
Subject: qotd: Is tailoring enrollment strategies to match dynamics of
uninsured a solution?
Date: Mon, 30 Sep 2013 09:02:19 -0700
From: Don McCanne <>
To: Quote-of-the-Day <>

Health Affairs
September 2013 (online)
Understanding State Variation In Health Insurance Dynamics Can Help
Tailor Enrollment Strategies For ACA Expansion
By John A. Graves and Katherine Swartz


The number and types of people who become eligible for and enroll in the
Affordable Care Act's (ACA's) health insurance expansions will depend in
part on the factors that cause people to become uninsured for different
lengths of time. We used a small-area estimation approach to estimate
differences across states in percentages of adults losing health
insurance and in lengths of their uninsured spells. We found that nearly
50 percent of the nonelderly adult population in Florida, Nevada, New
Mexico, and Texas—but only 18 percent in Massachusetts and 22 percent in
Vermont—experienced an uninsured spell between 2009 and 2012. Compared
to people who lost private coverage, those with public insurance were
more likely to experience an uninsured spell, but their spells of
uninsurance were shorter. We categorized states based on estimated
incidence of uninsured spells and the spells' duration. States should
tailor their enrollment outreach and retention efforts for the ACA's
coverage expansions to address their own mix of types of coverage lost
and durations of uninsured spells.

Policy Implications

The planning for implementing the ACA's coverage expansions has largely
focused on the percentage of people in each state who are uninsured at a
certain point in time. In particular, attention has centered on
decomposing these state percentages into people eligible for Medicaid
coverage and people eligible for premium subsidies if they purchase
plans in an exchange. Two states with the same percentage of uninsured
people could have quite different proportions of people who are eligible
for Medicaid, for premium subsidies, or neither.

Two states with similar uninsurance rates could also have very different
uninsured populations in terms of how long adults had been without
coverage. Differences in length of time without insurance are just as
important as differences in income for policy makers. They must
determine whether to focus more on efforts to minimize possible Medicaid
churning or on efforts to reduce potential adverse selection—that is,
efforts to encourage healthy uninsured people to buy exchange plans, so
the plans do not have a disproportionate share of enrollees who require
expensive medical care in the near future. States with a high incidence
of people losing public coverage, for example, could have more reason to
worry about Medicaid churning than states with a high proportion of
uninsured spells that last more than two years, where possible adverse
selection in their exchanges would be a more likely problem.

Comment: This study shows that it is not only the income level but it
is also the length of time that an individual has been uninsured that
affects the probability of whether the individual would be eligible for
Medicaid or for the state insurance exchanges instead. Since there is
considerable variation between states, the authors suggest that
enrollment strategies be tailored to target the uninsured based not just
on the types of coverage lost but also on the duration of being
uninsured. What?

The dynamics of public or private insurance eligibility are forever
changing. Eligibility depends on employment, income, age, geographical
location, level of state participation in Medicaid, immigration status,
and other factors that are frequently in flux. The navigator program was
established to help individuals sort out the eligibility criteria in
order to move them into appropriate health care coverage.

It does not seem logical that states should measure durations of being
uninsured and then change enrollment strategies simply because people
who previously had been in public programs had shorter periods of being
uninsured. Navigators help all uninsured (except the tens of millions
not able to enroll in any program) whereas targeted programs are aimed
at only selected populations of the uninsured, tacitly acknowledging the
difficulties of getting everyone covered and retaining that coverage.

We are making this too complicated. Much of this is due to the fact that
we are merely expanding a highly fragmented and dysfunctional system of
financing health care that is too costly and doesn't work very well. It
is ridiculous that we should consider looking at all of the fluid
variables and then target some individuals while leaving others behind.
Instead of trying to sort all of this out we should merely switch to a
single payer national health program in which everyone is simply
enrolled once, for life.

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