The impact of a national program to provide free
mammograms to low income uninsured women on
breast cancer mortality rates
David H. Howard (Emory University)
Donatus U.Ekwueme (CDC)
James G. Gardner (CDC)
Florence K.Tangka (CDC)
Chunyu Li (CDC)
Jacqueline W. Miller (CDC)
Trends in program measures
Data sources
Mortality: multiple cause of death files
Program screening rates: CDC administrative
data
Controls for insurance, unemployment, income:
Census, Regional Economic Accounts (BEA),
Local Area Unemployment Statistics, BLS
Empirical strategy
Screening rate
VA
IL
Time
Empirical strategy
Screening rate
Mortality
VA
IL
IL
Time
VA
Time
Empirical strategy
Screening rate
Mortality
VA
IL
Time
VA
IL
Time
Empirical specification
y st − ys = β ( xst − xs ) + other stuff + ε st
Empirical specification
y st − ys = β ( xst − xs ) + other stuff + ε st
Breast cancer
mortality per
100K women
40-64
Empirical specification
y st − ys = β ( xst − xs ) + other stuff + ε st
Breast cancer
mortality per
100K women
40-64
Program screening
rate per 100K
women 40-64
Empirical specification
Controls for % uninsured, %
Medicaid, unemployment rate,
income, year dummies
y st − ys = β ( xst − xs ) + other stuff + ε st
Breast cancer
mortality per
100K women
40-64
Program screening
rate per 100K
women 40-64
Baseline results
Dep. Variable: Breast cancer
mortality rate in women 40-64
Model 1
B [95% CI]
Proportion of women screened
State has a screening program
Percent uninsured
Percent on Medicaid
Unemployment rate
Per capita income
-60.9 [-117.2, -4.7] **
Year effects
States
Years
N
*p<0.10, **p<0.05
0.37
0.22
0.03
-0.05
Yes
51
15
765
[-0.10, 0.84]
[-0.21, 0.65]
[-0.60, 0.67]
[-1.24, 1.14]
Baseline results
Dep. Variable: Breast cancer
mortality rate in women 40-64
Model 1
B [95% CI]
Proportion of women screened
State has a screening program
Percent uninsured
Percent on Medicaid
Unemployment rate
Per capita income
-60.9 [-117.2, -4.7] **
Year effects
States
Years
N
*p<0.10, **p<0.05
0.37
0.22
0.03
-0.05
Yes
51
15
765
[-0.10, 0.84]
[-0.21, 0.65]
[-0.60, 0.67]
[-1.24, 1.14]
Model 2
B [95% CI]
-1.18
0.36
0.22
0.04
-0.07
Yes
51
15
765
[-2.49, 0.13]
[-0.11, 0.82]
[-0.21, 0.64]
[-0.60, 0.67]
[-1.27, 1.12]
*
Interpreting the results: 3 questions
No confounding
Reasonable
magnitude
Temporality
Interpreting the results: 4 questions
No confounding
Reasonable
magnitude
Temporality
Are there
omitted/confounding
variables?
Interpreting the results: 4 questions
No confounding
Reasonable
magnitude
Temporality

Are there
omitted/confounding
variables?
Probably not.
Falsification test for screening rate
Age group
40-64
65+
Breast cancer (base model)
Colon cancer
Heart disease
Homicides/Suicides/Accidents
*p<0.10, **p<0.05
-60.9 **
-27.3
6.2
14.6
-45.0
-49.5
42.0
-136.0 *
Falsification test for program dummy
Age group
40-64
65+
Breast cancer (base model)
Colon cancer
Heart disease
Homicides/Suicides/Accidents
*p<0.10, **p<0.05
-1.18 *
-0.86 *
-1.03 *
0.20
0.87
2.76
10.61 **
-2.37
Interpreting the results: 4 questions
Is the magnitude of the
effect plausible?
No confounding
Reasonable
magnitude
Temporality

Interpreting the results: 4 questions
Is the magnitude of the
effect plausible?
No confounding
Reasonable
magnitude


Yes: Our estimates
suggest the Program
avoided 0.6 deaths per
1,000 women screened
Temporality
Other studies estimate 1.8
to 3 deaths per 1,000
women screened
Interpreting the results: 4 questions
No confounding
Reasonable
magnitude
Temporality


Is the temporal nature of
the relationship
reasonable?
Models with lagged screening rate
Interpreting the results: 4 questions
No confounding

Reasonable
magnitude

Temporality

Is the temporal nature of
the relationship
reasonable?
No: To the degree that
screening is responsible
for reduced mortality, we
shouldn’t see an
immediate effect.
Conclusions
There is some evidence that the Program is
effective in reducing mortality, but it is hard to
draw definitive conclusions.
Given the size of the program, detecting an
effect, if there is one, is difficult.