The Economics of Treatment Disparities in Healthcare

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The Economics of Treatment Disparities in
Healthcare

• Amitabh Chandra
• Harvard and the NBER

Douglas Staiger Dartmouth and the NBER
PRELIMINARY DO NOT CITE OR CIRCULATE
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• There is a MASSIVE literature in medicine and
  public health on treatment disparities in
  healthcare.
• The Institute of Medicines (IOM) report Unequal
  Treatment summarizes the key findings of this
  literature and concludes

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• Racial and ethnic minorities tend to receive a
  lower quality of healthcare than non-minorities,
  even when access-related factors, such as
  patients insurance status and income, are
  controlled. The sources of these disparities are
  complex, are rooted in historic and contemporary
  inequities, and involve many participants at
  several levels, including health systems, their
  administrative and bureaucratic processes,
  utilization managers, healthcare professionals,
  and patients. Consistent with the charge, the
  study committee focused part of its analysis on
  the clinical encounter itself, and found evidence
  that stereotyping, biases, and uncertainty on the
  part of healthcare providers can all contribute
  to unequal treatment.

Smedley, B. D., A. Y. Stith, and A. R. Nelson,
eds. 2003. Unequal treatment Confronting racial
and ethnic disparities in health care.
Washington, DC National Academies Press.
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Lets look at some examples from the literature
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Jha, A. K. et al. N Engl J Med 2005353683-691c
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Lets look at some facts from our own tabulations
of AMI Treatments
Every first heart-attack in Medicare since
1992. Approximately 210,000 such patients per
year. Each AMI is matched to Part A claims data
at 30 days and 1 year after admission, and if
relevant, death certificate data.
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Question

• Do these disparities represent prejudice against
  women and minorities, or statistical
  discrimination?
• Under statistical discrimination, physicians are
  trying to maximize benefit to patient, but
  gender/race are statistically related to the
  benefit.

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Two Different Views of the World
Prejudice Patients with identical benefit
treated differently in one group.
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Two Different Views of the World
EFFICIENT ALLOCATION
Statistical discrimination patients with
identical benefit treated the same, but benefits
higher for one group
Prejudice Patients with identical benefit
treated differently in one group.
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Key Idea

• Take two patients with the same propensity to
  get the treatment (so who are treated the same)
  and then test whether net benefits are the same
  (statistical discrimination).

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Model

• NB Net Benefit of treatment
• H Hurdle that NB must exceed to receive care
• Treatment1 if NB gt H
• NB Xb0 femaleb1 e
• H a0 femalea1 v
• Pr(Treatment1) Pr (NB gt H)
• Pr (Xb0 femaleb1 e gt a0 femalea1
  v)
• Pr (Xb0 female(b1-a1)a0 gt v e)
• Pr (Igtv-e)

a1gt0 reflects prejudice (females must overcome
larger hurdle on average to get treatment)
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Model

• NB Net Benefit of treatment
• H Hurdle that NB must exceed to receive care
• Treatment1 if NB gt H
• NB Xb0 femaleb1 e
• H a0 femalea1 v
• Pr(Treatment1) Pr (NB gt H)
• Pr (Xb0 femaleb1 e gt a0 femalea1
  v)
• Pr (Xb0female(b1-a1)a0 gt v e)
• Pr (I gt v-e)
• But we want treatment effect on the treated (TT)
• E(NB Treatment1) Xb0 femaleb1 E(e
  Igtv-e)
• E(NB Treatment1) I a0 femalea1 E(e
  Igtv-e)
• g(I) femalea1

a1gt0 reflects prejudice (females must overcome
larger hurdle on average to get treatment)
Implication 1 In the absence of prejudice
(a10), two people with the same propensity to
get treatment (same I) will have the same
expected net benefit from treatment.
Implication 2 If there is prejudice (a1gt0), then
higher net benefit (conditional on I) in minority
group.
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Defining Net Benefit

• NB (S)urvival ?.(C)ost,
• where ? is survival per 1000 dollars
• What are BIG and small values for ??
• Some might use ? 0 (physician should ignore
  costs of care infinite value of life)
• BIG value for ? implies small value of life
• One survivor at 1 year realizes about 5 years of
  life.
• Minimum value of life year would be 20k,
  implying ? 0.01
• More reasonable value of life year would be
  100k, implying ? 0.002
• Our sense is that reasonable values of ? lie
  between 0.01 and 0.002

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For two people with the same propensity
(I) E(NB T1,male,I) E(?ST1,male,I)
?.E(?C T1,male,I) g(I) E(NB
T1,female,I) E(?ST1,female,I) ?.E(?C
T1,female,I) g(I) a1 Let f(I) be the
pdf of I for women. Then integrating both of the
above over f(I) and taking the difference between
women and men gives
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In other words, in two populations with the same
distribution of cath propensity (I), any
difference in net benefits of treatment is
evidence of prejudice in treatment
decision. With estimates of treatment effects on
survival and costs for women and men, we can
construct the set of all ? for which we cannot
reject H0 a10
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What about Estimation?

• Estimate ?S and ?C from
• S a0 a1Treat a2(Treatfemale) Xa3 e,
  where a1 and a2
• C ß0 ß1Treat ß2(Treatfemale) Xß3 e,
  where ß1 and ß2
• For all ? between 0.0-0.1, we test
• H0 a2- ?ß2 0
• 95 CI is set of all ? that cannot reject at
  p.05
• if a2- ?ß2 gt 0 ? a1gt0 ? prejudice against women
• if a2- ?ß2 lt 0 ? a1lt0 ? prejudice against men

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Nitty Gritty Details

• Estimation method
• OLS (very good Xs)
• IV (using diffdist, difdistfemale as IVs)
• Weighting
• Unweighted estimation ? we compare OLS/IV
  returns to CATH for males and females. But this
  produces treatment effects integrated over
  different distributions of treatment propensity.
• For testing our model, we need same distribution
  of propensity in both groups.
• Reweight men using Barsky, et al. (JASA, 2002) so
  that distribution of cath propensity is same as
  women
• Find 1st, 2nd, ., 99th percentile of female
  distribution of cath propensity
• Reweight men by .01 over fraction of men in each
  range

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Reweighting works!
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Empirical Work

• Test predictions of both models using data from
  the Cooperative Cardiovascular Project (CCP)
• Chart data on 140,000 Medicare beneficiaries
  (over 65) who had heart-attacks matched to Part
  B claims.
• Sample is restricted to fresh-AMIs we exclude
  transfers from another ER, or nursing home
  facilities.
• Use CATH as marker for intensive treatment
• Use DIFFERENTIAL-DISTANCE to CATH hospital as IV
  for Catheterization.

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Construction of Clinical Appropriateness for
Aggressive Treatments Pr(CATH1X)
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Acute Myocardial Infarction
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Primer on Cardiac Catheterization
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Table 1 Means by sex and race, CCP data
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Table 2 Probit Coefficients marginal effects
of the effect of Sex and Race on Catheterization
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Do Race-Specific Models Explain Disparities in
Treatments after AMI? Jha, Lee, Staiger and
Chandra (2006)
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Do Race-Specific Models Explain Disparities in
Treatments after AMI? Jha, Lee, Staiger and
Chandra (2006)
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Table 3 Wald Estimates
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Conclusions

• If anything, women blacks are getting lower
  returns, even after we adjust for costs.
• Our IV estimates are imprecise, but we plan to
  update with 1992-2003 claims data (about 20x the
  sample).
• Key question is why are the benefits of care
  different?
• Genes? Contentious explanation for race
  differences
• Geography? Cant explain sex differences.
• Follow-up care?