Article

Mediating Factors in the Relationship between Income and Mammography Use in Low-Income Insured Women

Urban Indian Health Institute, Seattle Indian Health Board, Seattle, Washington, USA.
Journal of Women's Health (Impact Factor: 2.05). 10/2008; 17(8):1371-8. DOI: 10.1089/jwh.2007.0625
Source: PubMed

ABSTRACT

We used secondary data from a prospective randomized mammography recruitment trial to examine whether attitudinal and facilitating characteristics mediate the observed relationship between annual household income and mammogram receipt among women in an integrated health plan.
We compared 1419 women due for a screening mammogram based on the 1995 annual household income poverty definition for a family of four (<$15,000 vs. >$15,000). A telephone survey was used to collect information on household income, demographics, health behavior, attitudinal and facilitating variables. Administrative databases were used to document mammography receipt. We used Cox proportional hazards models to estimate the hazards ratio (HR) and 95% confidence interval (CI) of subsequent mammography use separately for women with and without a prior mammogram.
Several variables, including employment, living alone, believing that mammograms are unnecessary, having friends supportive of mammography, and ease of arranging transportation, completely mediated the effect of income on mammography use. In multivariable models, the direct predictive effect of income on mammography was reduced to nonsignificance (HR 1.13, 95% CI 0.82-1.54 in women with previous mammogram and HR 0.91, 95% CI 0.41-2.00 in women without previous mammogram).
Providing insurance does not ensure low-income populations will seek screening mammography. Efficacious interventions that address attitudes and facilitating conditions may motivate mammography use among low-income women with insurance.

Full-text

Available from: Stephen H Taplin
JOURNAL OF WOMEN’S HEALTH
Volume 17, Number 8, 2008
© Mary Ann Liebert, Inc.
DOI: 10.1089/jwh.2007.0625
Mediating Factors in the Relationship between Income
and Mammography Use in Low-Income Insured Women
Alice N. Park, M.P.H.,
1
Diana S.M. Buist, Ph.D., M.P.H.,
2
Jasmin A. Tiro, Ph.D.,
3
and Stephen H. Taplin, M.D., M.P.H.
4
Abstract
Aim: We used secondary data from a prospective randomized mammography recruitment trial to examine
whether attitudinal and facilitating characteristics mediate the observed relationship between annual house-
hold income and mammogram receipt among women in an integrated health plan.
Methods: We compared 1419 women due for a screening mammogram based on the 1995 annual household
income poverty definition for a family of four ($15,000 vs. $15,000). A telephone survey was used to collect
information on household income, demographics, health behavior, attitudinal and facilitating variables. Ad-
ministrative databases were used to document mammography receipt. We used Cox proportional hazards mod-
els to estimate the hazards ratio (HR) and 95% confidence interval (CI) of subsequent mammography use sep-
arately for women with and without a prior mammogram.
Results: Several variables, including employment, living alone, believing that mammograms are unnecessary,
having friends supportive of mammography, and ease of arranging transportation, completely mediated the
effect of income on mammography use. In multivariable models, the direct predictive effect of income on mam-
mography was reduced to nonsignificance (HR 1.13, 95% CI 0.82-1.54 in women with previous mammogram
and HR 0.91, 95% CI 0.41-2.00 in women without previous mammogram).
Conclusions: Providing insurance does not ensure low-income populations will seek screening mammography.
Efficacious interventions that address attitudes and facilitating conditions may motivate mammography use
among low-income women with insurance.
1371
Introduction
B
REAST CANCER IS THE
leading incident cancer and the sec-
ond leading cause of cancer death in women.
1
Recent
trends indicate a decline in deaths, attributable to uptake of
screening mammograms and treatment advances.
1,2
Mortal-
ity reduction has not been universally shared, however.
Low-income women are at higher risk for breast cancer mor-
tality partially because of lower screening rates.
3
Cost is one
important barrier
4
; however, intervention studies show low-
income women are less likely to receive a mammogram even
with insurance coverage or offers of free mammograms.
5–6
Additionally, an analysis of seven integrated health plans
showed that 53% of women who had late-stage breast can-
cers also had not been recently screened, and they were more
likely to be from low-income neighborhoods.
7
Therefore, ad-
dressing insurance access alone may not increase mammog-
raphy use.
Meta-analyses indicate that access-enhancing interven-
tions are most effective at increasing mammography among
low-income populations, but most of these studies have been
conducted with populations lacking health insurance.
8–10
Thus, less is known about interventions that are effective
among low-income women who have insurance. We are un-
aware of behavioral epidemiological studies in the United
States that have examined whether attitudinal and facilitat-
ing variables explain the differential patterns of mammog-
raphy use by income among women with insurance.
1
Urban Indian Health Institute, Seattle Indian Health Board, Seattle, Washington.
2
Group Health Center for Health Studies, Seattle, Washington.
3
Cancer Prevention Fellowship Program, Division of Cancer Prevention, and
4
Applied Research Program, Division of Cancer Control
and Population Sciences, National Cancer Institute, Bethesda, Maryland.
This research was supported by National Cancer Institute cooperative agreement, grants U01CA63731 and CA63188. D.S.M.B. was sup-
ported in part by American Cancer Society grant CRTG-03-024-01-CCE.
Page 1
We reported previously that low-income women in an
integrated health plan, where mammograms are a covered
benefit provided free-of-charge, were less likely than higher
income women to receive a mammogram within a year fol-
lowing invitation to receive a mammogram.
5
We reexam-
ined data from the original trial to evaluate whether atti-
tudinal or facilitating variables mediated the effect of
income on mammography use. We are aware of no other
longitudinal studies that evaluate mediators along the
causal pathway between income and mammography use
among women within a managed care organization with
access to mammography at no direct cost.
Materials and Methods
Study population
The current report uses data from a randomized trial of
screening mammography among 5062 randomly selected
women aged 50–79 years who were enrolled in a breast can-
cer screening program, were due for a screening mammo-
gram in 1995, and did not schedule the examination after a
mailed reminder.
5
The trial was previously described in de-
tail and is summarized in Figure 1.
5
The 1765 women who
were nonadherent 2 months after being sent a letter recom-
mending screening were randomized into three intervention
groups: a mailed reminder, a telephone reminder, or a mo-
tivational telephone call. Group Health’s (GH) Institutional
Review Board reviewed and approved this analysis. Data
were collected from 1996 to 1997.
Survey
The trial was based on a heuristic conceptual framework
that combined aspects of the theory of reasoned action, so-
cial learning theory, and the precede/proceed mode.
5
In a
baseline telephone survey, we collected information on de-
mographic characteristics (age, education, race, employ-
ment, marital status, and living situation), health practices
(previous mammography or Pap smear, smoking, and health
status), attitudinal beliefs about mammography and breast
cancer (e.g., perceptions of what others want them to do;
anxiety – discomfort, radiation and embarrassment or about
waiting for the test; and affect), and conditions that facilitate
mammography uptake (e.g., transportation method, park-
ing, scheduling, and courtesy of staff).
Items measuring attitudes and facilitating conditions were
assessed on a 5-point Likert scale with varying anchors (e.g.,
strongly disagree to strongly agree or very easy to very dif-
ficult). Don’t know was used to describe the midpoint. For
highly skewed items, we collapsed responses for strongly to
somewhat and excluded Don’t know responses if 3 people
chose the response.
PARK ET AL.1372
FIG. 1. Study design: The 1419 women with income information comprised the study population for the analysis.
Page 2
Participants recruited for the trial were a random sample
of 11,570 women due for a mammogram based on adminis-
trative records during a 15-month rolling recruitment period
in 1995–1996. An introductory letter was sent to women, with
the option to exclude themselves from a subsequent tele-
phone contact. At the time of the telephone contact, women
were given information about the study, and informed con-
sent was obtained. We approached 5062 women, and 3743
(74%) agreed to participate and completed a 15-minute tele-
phone survey (Fig. 1). There were no financial incentives. Af-
ter the survey, women were sent a reminder letter saying
they were due for a mammogram. This study occurred
among the women who did not schedule an examination
within 2 months of when the reminder letter was sent
(Fig. 1).
Income
We collapsed self-reported annual household income into
two categories: low income (at or below the U.S. 1995 poverty
level of $15,000/year for a family of four) and high income
(above the poverty line).
11
Outcome
Our outcome was receipt of a mammogram within 12
months of an invitation to schedule a mammogram and 10
months of being randomized for the parent trial. We ascer-
tained the date of the mammogram from an automated ad-
ministrative database that has records on all mammograms
received at GH as well as those received outside GH if a
claim was submitted.
Analysis
The purpose of this analysis was to determine if attitudi-
nal or facilitating variables mediated the effect of income on
mammography use. To evaluate mediation, we modified
Baron and Kenny’s three-step sequential analytic method
12
to require that (1) the independent variable is associated with
the dependent variable, (2) the independent variable is as-
sociated with the mediator, and (3) the mediating variable is
associated with the dependent variable when both the inde-
pendent variable and the mediator are simultaneously re-
gressed on the dependent variable. Complete mediation is
indicated if the association between independent and de-
pendent variables is reduced to nonsignificance.
Evidence of the first step, that the independent variable
(income) significantly predicted the dependent variable
(mammography receipt), was shown in a previous paper.
5
Therefore, our analysis began with the second step, deter-
mining if the independent variable, income, was significantly
associated with the hypothesized mediators. We computed
chi-square tests to compare the distributions of all attitudi-
nal and facilitating variables by income level. We also ex-
amined the association between attitudinal and facilitating
variables and the dependent variable, mammography re-
ceipt. Because the parent trial stratified on prior mammog-
raphy use, we maintained this stratification in this and sub-
sequent analyses.
5
To evaluate Baron and Kenny’s third stem,
12
we used sep-
arate Cox proportional hazards models to estimate the haz-
ards ratio (HR) and 95% confidence intervals (CIs) of mam-
mography receipt within 12 months of an invitation to sched-
ule a mammogram, adjusted for randomization group and
including one mediator variable at a time (base model). All
mediators were modeled as categorical variables using the
groups shown in Table 1, with the first category as the ref-
erent. We stratified all analyses based on past mammogra-
phy status, adjusted all analyses for randomization group,
and used time since randomization as the time axis. Follow-
up time ended at the first of the following events: mam-
mography receipt, death, disenrollment from the health plan,
or end of the study period. These Cox models allowed us to
assess the individual contribution of each mediator.
We calculated the percent of the relationship between in-
come and receipt of mammography that could be accounted
for by accounting for individual mediator variables (excess
risk).
13,14
Excess risk was calculated as follows:
(HR income adjusted for randomization group HR income
adjusted for randomization group explanatory factor)/
(1 HR income adjusted for randomization group)*100
13,14
The percent excess risk measures the percent of the HR be-
tween income and mammography receipt that can be ex-
plained by the mediator variable. The methods used to cal-
culate excess risk for each mediator ignore variables that are
collinear (e.g., age, employment). As a result, two factors that
are correlated could individually account for a similar per-
centage of excess risk but, when examined in combination,
explain less than their sum.
To examine the combined effect of all significant media-
tors, we constructed a final multivariable model including
all mediators from the bivariate analyses that accounted for
10% of the excess risk for income and mammography re-
ceipt, adjusting for randomization group.
15
For example, a
variable could strongly predict mammogram use but not be
associated with income; such a variable would not change
the beta-estimate for income and, therefore, was not in-
cluded. The same is true for variables associated with income
but not with mammography. This method assures that only
variables associated with both the exposure (income) and
outcome (mammography) are included in the multivariable
model.
15
We repeated this process adjusting for age and living sit-
uation in the base model to determine if there were any dif-
ferent mediators identified after adjusting for age in all the
base models. All analyses were conducted in Stata 9.2 (Col-
lege Station, TX).
Results
Among the 1765 potentially eligible women, 346 women
(19.6%) provided no information about their income and
were excluded, leaving a total sample of 1419 women in our
study. The proportion of nonresponse to the income item ob-
served in this study was similar to that in national surveys,
such as the National Health Interview Survey (NHIS).
16
Fif-
teen percent of the women (n 207) reported low income.
Mammography use was lower among low-income (31.9%)
compared with high-income women (48.9%). Lower-income
women were older; less educated; more likely to be non-
white, unemployed or retired, unmarried, living with non-
relatives; and less likely to have had a prior mammogram or
a recent Pap smear than high-income women (Table 1). There
MAMMOGRAPHY USE AND INCOME 1373
Page 3
T
ABLE
1. C
HARACTERISTICS OF
W
OMEN BY
A
NNUAL
H
OUSEHOLD
I
NCOME
a
AND
M
AMMOGRAPHY
E
XPERIENCE AND BY
W
HETHER
OR
N
OT
W
OMEN
R
ECEIVED A
M
AMMOGRAM WITHIN
12 M
ONTHS OF AN
I
NVITATION TO
S
CHEDULE A
M
AMMOGRAM
Previous mammogram No previous mammogram
Did not Did not
Low High Received receive Received receive
income
a
income
a
mammogram mammogram mammogram mammogram
(n 207) (n 1212) (n 578) (n 448) (n 81) (n 312)
Characteristic Column % Column % Column % Column % Column % Column %
Demographics
Group
Postcard 27.5 35.2 26.6 43.3 17.3 38.8
Reminder call 30.9 32.8 36.2 27.9 44.4 29.2
Motivational call 41.6 32.1 37.2 28.8 38.3 32.1
Annual household income
a
Low income ($15,000) N/A N/A 9.7 15.8 12.3 22.4
High income ($15,000) N/A N/A 90.3 84.2 87.7 77.6
Age, years
b
50–59 14.5 55.5 50.2 50.4 64.2 43.3
60–69 30.9 26.9 28.4 25.4 23.5 29.8
70–79 54.6 17.6 21.5 24.1 12.3 26.9
Education
b,c
High school 65.7 31.6 34.1 37.5 35.8 39.9
Some college 25.1 35.0 30.7 33.5 43.2 36.7
College graduate 9.2 33.4 35.2 29.0 21.0 23.5
Race
b
White 86.5 89.8 90.5 90.2 87.7 86.2
Native American 1.9 0.7 1.0 0.9 1.2 0.3
African American 6.3 3.7 3.5 4.2 4.9 4.8
Asian 1.5 4.7 4.2 3.1 4.9 5.8
Other 3.9 1.2 0.9 1.6 1.2 2.9
Employment
b,d
Employed 15.0 56.9 51.8 48.9 63.2 48.3
Unemployed 17.0 10.9 10.3 13.3 11.8 12.3
Retired 65.0 30.0 34.7 35.4 25.0 38.0
Disabled/other/don’t 3.1 2.2 3.1 2.4 0.0 1.3
know/refuse
Marital status
b,e
Married 17.0 61.4 62.2 52.3 63.6 53.0
Unmarried 83.0 38.6 37.9 47.7 36.4 47.0
Living situation
b,f
Alone 12.6 45.5 46.2 37.5 43.2 34.7
Living with relatives 25.2 31.4 27.9 31.7 39.5 31.5
Living with nonrelatives 62.1 23.0 26.0 30.8 17.3 33.8
Health practices
Prior mammogram
b
61.4 74.2 N/A N/A N/A N/A
Pap smear in last 4 years
b,g
65.4 76.7 83.7 72.9 72.8 62.5
Attitudinal beliefs
Mammogram very/somewhat 13.0 6.9 2.8 5.8 13.6 18.3
frightening
b,c
Mammogram very/somewhat 70.1 85.2 93.9 82.8 90.1 61.2
beneficial
b
Finding breast cancer early is 95.2 98.3 100.0 97.0 98.0 93.0
very/somewhat beneficial
b
Inconvenience of getting a 70.1 79.6 91.1 76.1 71.7 54.5
mammogram is very/somewhat
acceptable
b
Mammogram important even 64.3 76.3 86.3 75.9 81.5 49.0
without cancer signs
(strongly/somewhat agree)
b
Mammogram might detect cancer 84.1 93.3 97.1 92.2 95.1 81.4
clinician can’t find during phyiscal
examination (strongly/somewhat
agree)
b
Mammogram unnecessary at your 34.3 16.0 8.1 18.8 11.1 40.1
age (strongly/somewhat agree)
b
Regular healthcare provider feels 70.1 84.2 90.5 82.6 84.0 65.7
you should have a mammogram
(strongly/somewhat agree)
b
Page 4
were many differences between lower-income and higher-
income women for variables measuring attitudinal beliefs.
Lower-income women were more likely to believe that hav-
ing a mammogram is frightening, feel the inconvenience of
getting a mammogram is unacceptable, believe that having
a mammogram is unnecessary at their age, not want to know
if they had cancer, and be confused by contradictory infor-
mation about mammography (Table 1). There were no dif-
ferences by income in attitudes reflecting anxiety about the
mammogram (e.g., physical discomfort, radiation, embar-
rassment) or about waiting for results (data not shown). The
only facilitating condition associated with income was ease
in arranging transportation; there were no differences in
transportation method, issues related to making an ap-
pointment, or perceived courtesy of staff (data not shown).
Twenty-eight percent of the women (n 393) reported no
previous mammogram, and these women were significantly
less likely than women who had had a previous mammogram
to receive a mammogram during the study period (Table 1).
When stratified by past mammography behavior, mammog-
raphy receipt was associated with being in one of the tele-
phone intervention groups, higher income, younger age, em-
ployment status, and having a recent Pap test (Table 1).
Women who did not get a mammogram during the study pe-
riod were more likely to report negative attitudes (Table 1).
Higher-income women were more likely to get a mam-
mogram when it was recommended, regardless of their pre-
vious screening behavior (Table 2). For women who had a
previous mammogram, employment, living alone, recent
Pap test, and four attitudinal or facilitating conditions (be-
lieving mammography is unnecessary at her age, whether
women friends thought she should have a mammogram,
knowledge of the likelihood of getting breast cancer, and
ease of arranging transportation) each independently ex-
plained part of the relationship between income and mam-
mography, with significant percent excess risks for individ-
ual mediators ranging from 13.8% (Pap smear in past 4 years)
to 20.7% (employment). After accounting for all seven me-
diators identified in the base models in the multivariable
model, there was no association between income and mam-
mography receipt for women with a prior mammogram (HR
1.13, 95% CI 0.82-1.54), explaining 77.6% of the relationship
between income and mammography.
For women with no previous mammogram, five of the
seven variables described (employment, living alone, mam-
mogram unnecessary at her age, whether women friends
MAMMOGRAPHY USE AND INCOME 1375
T
ABLE
1. C
HARACTERISTICS OF
W
OMEN BY
A
NNUAL
H
OUSEHOLD
I
NCOME
a
AND
M
AMMOGRAPHY
E
XPERIENCE AND BY
W
HETHER
OR
N
OT
W
OMEN
R
ECEIVED A
M
AMMOGRAM WITHIN
12 M
ONTHS OF AN
I
NVITATION TO
S
CHEDULE A
M
AMMOGRAM
(C
ONT
D
)
Previous mammogram No previous mammogram
Did not Did not
Low High Received receive Received receive
income
a
income
a
mammogram mammogram mammogram mammogram
(n 207) (n 1212) (n 578) (n 448) (n 81) (n 312)
Characteristic Column % Column % Column % Column % Column % Column %
Women friends feel you should 38.7 58.5 65.1 52.5 60.5 39.4
have a mammogram
(strongly/somewhat agree)
b
Didn’t want mammogram because 16.4 9.0 4.3 8.1 15.2 21.7
didn’t want to know if had cancer
(just/somewhat like you)
b,e
Didn’t want mammogram because 22.2 14.0 5.9 13.4 18.5 34.1
too confused with contradictory
information (just/somewhat like
you)
b,e
Knowledge of the likelihood 13.3 22.1 24.7 17.8 15.0 19.5
women will get breast cancer
b
1 in 9
1 in 9 37.0 44.6 47.2 44.2 30.0 39.1
1 in 9 23.7 22.5 19.2 23.9 38.8 23.1
Don’t know 26.1 10.8 8.9 14.0 16.3 18.2
Facilitating conditions
Very/somewhat easy to arrange 68.1 90.1 92.9 85.9 83.8 77.7
transportation
b
Very/somewhat easy to park
h
66.9 69.4 61.1 60.5 45.7 51.6
a
Annual household income collected from self-report in 1995 and classified into low (at or below the U.S. 1995 poverty level of $15,000/year
for a family of four) and high income ($15,000/year).
11
b
0.05 based on chi-square test for difference between low vs. high income.
c
Missing 2 responses.
d
Missing 31 responses.
e
1 Don’t know response excluded.
f
Missing 1 response.
g
19 Don’t know/refuse responses excluded.
h
Among those who came to their appointment by car.
N/A, not applicable.
Page 5
T
ABLE
2. H
AZARDS
R
ATIOS
(HR)
AND
95% C
ONFIDENCE
I
NTERVALS
(CI)
FOR
M
AMMOGRAPHY
R
ECEIPT
WITHIN
12 M
ONTHS OF
I
NVITATION TO
S
CHEDULE A
M
AMMOGRAM AMONG
H
IGHER
, I
NCOME
R
ELATIVE TO
L
OWER
-I
NCOME
a
W
OMEN
: U
NIVARIATE
A
NALYSIS
, B
ASE
M
ODEL
b
M
EDIATOR
A
NALYSES
,
AND
O
VERALL
M
ULTIVARIABLE
M
ODELS
S
TRATIFIED BY
P
RIOR
M
AMMOGRAPHY
U
SE
Previous mammogram No previous mammogram
(n 1026) (n 393)
Univariate model HR (95% CI) for mammography use among higher-income vs.
lower-income women adjusting for randomization group
Risk of receiving a mammogram among 1.58 (1.20–2.08) 2.02 (1.04–3.92)
higher vs. lower-income women
Base model: Potential mediators Base model HR (95% CI) for mammography use among higher-
income vs. lower-income women adjusted for randomization
group and including one potential mediator variable
b
% Excess % Excess
risk
c
risk
c
Demographics
Age 1.59 (1.19–2.13) 1.7% 1.48 (0.74–2.97) 52.9%
Education 1.55 (1.17–2.05) 5.2% 2.04 (1.03–4.04) 2.0%
Race 1.55 (1.17–2.05) 5.2% 2.00 (1.03–3.90) 2.0%
Employment 1.46 (1.09–1.95) 20.7% 1.50 (0.73–3.08) 51.0%
Living alone 1.49 (1.12–1.99) 15.5% 1.57 (0.79–3.12) 44.1%
Past preventive behavior
Pap smear in last 4 years 1.50 (1.14–1.98) 13.8% 2.03 (1.04–3.94) 1.0%
Attitudes
Mammogram frightening 1.57 (1.19–2.07) 1.7% 1.71 (0.87–3.35) 30.4%
Mammogram beneficial 1.55 (1.18–2.05) 5.2% 1.42 (0.72–2.78) 58.8%
Finding breast cancer early is beneficial 1.56 (1.19–2.06) 3.4% 1.99 (1.02–3.87) 2.9%
Inconvenience of getting a mammogram 1.56 (1.18–2.06) 3.4% 1.93 (0.99–3.78) 8.8%
acceptable
Mammogram important even without 1.56 (1.18–2.05) 3.4% 1.66 (0.85–3.24) 35.3%
cancer signs
Mammogram might detect cancer clinician 1.54 (1.17–2.03) 6.9% 1.75 (0.90–3.43) 26.5%
can’t find during physical examination
Mammogram unnecessary at your age 1.48 (1.12–1.95) 17.2% 1.46 (0.75–2.87) 54.9%
Do what people important to you think 1.58 (1.20–2.09) 0.0% 1.98 (1.02–3.85) 3.9%
you should do
Regular healthcare provider thinks you 1.53 (1.16–2.02) 8.6% 1.79 (0.91–3.52) 22.5%
should have a mammogram
Women friends think you should have a 1.49 (1.13–1.96) 15.5% 1.79 (0.91–3.51) 22.5%
mammogram
Didn’t want mammogram because didn’t 1.57 (1.19–2.06) 1.7% 1.93 (0.99–3.75) 8.8%
want to know if had cancer
Didn’t want mammogram because too 1.60 (1.21–2.11) 3.4% 1.82 (0.93–3.55) 19.6%
confused with contradictory information
Knowledge of the likelihood women will 1.48 (1.12–1.96) 17.2% 2.05 (1.04–4.02) 2.9%
get breast cancer
Facilitating conditions
Easy/difficult to arrange transportation 1.43 (1.08–1.89) 25.9% 1.90 (0.95–3.80) 11.8%
Easy/difficult to park 1.57 (1.19–2.08) 1.7% 2.08 (1.07–4.06) 5.9%
Multivariable model Multivariabale HR
d,e
(95% CI) for mammography use among
higher-income vs. lower-income women for income and
mammography use
Multivariable model 1.13
d
(0.82–1.54) 77.6% 0.91
e
(0.41–2.00) 100.%
a
Annual household income collected from self-report in 1995 and classified into low-income (at or below the U.S. 1995 poverty level of
$15,000/year for a family of four) and high-income ($15,000/year).
11
b
Each base model includes income and randomization group with one potential mediator variable. All mediators were modeled as cate-
gorical variables using the groups shown in Table 1, with the first category as the referent. For example, among women with no previous
mammogram, high-income women were 2.02 times more likely to receive a mammogram than lower-income women (HR 2.02, 95% CI
1.04–3.92). After including age as a mediator variable, higher-income women were 1.48 times more likely to receive a mammogram compared
with lower-income women (HR 1.48, 95% CI 0.74–2.97).
c
Excess risk is the proportion of the relation between income and receipt of mammogram explained by individual mediators. Excess risk [(HR
income adjusted for randomization group HR income adjusted for randomization group mediator)/(1 HR income adjusted for random-
ization group)]*100.
13,14
The methods used to calculate excess risk for each mediator ignore variables that are collinear. As a result, two factors that
are correlated could individually account for a similar percentage of excess risk but, when examined in combination, explain less than their sum.
d
Mediators included in the multivariable model for women with a previous mammogram. Any mediator that accounted for 10% of the
relation between income and mammography use was included in the multivariable model: employment; living alone; Pap smear in the last 4
years; belief that a mammogram is unnecessary at your age and women friends think you should have a mammogram; knowledge of the like-
lihood women will get breast cancer; and easy/difficult to arrange transportation.
e
Mediators included in the multivariable model for women who never had a previous mammogram. Any mediator that accounted for
10% of the relation between income and mammography use was included in the multivariable model: age; employment; living alone; belief
that mammograms are frightening, mammogram is beneficial, mammogram is important even without cancer signs, mammogram might de-
tect cancer clinician can’t find, mammogram is unnecessary at your age; regular healthcare provider feels you should have a mammogram;
women friends feel you should have a mammogram; don’t want mammogram because too confused with contradictory information; and
easy/difficult to arrange transportation.
Page 6
thought she should have a mammogram, and ease of arrang-
ing transportation) mediated the effect of income on mam-
mography (Table 2). Other mediators included age and sev-
eral attitudes (believing that mammograms are frightening,
beneficial, important even without cancer signs; may detect
cancer that a clinician cannot find; provider recommendation;
being confused by contradictory information about mammog-
raphy). Mediators appeared to have a greater impact on ex-
plaining the income-mammography receipt relationship
among women with no prior mammography, with seven vari-
able individually having excess risk values 30%; age, em-
ployment, and believing that mammography is beneficial and
necessary at a given age individually accounted for 50% of
the relationship. After accounting for individual mediators in
our multivariable model in women with no previous mam-
mogram, there was no relationship between income and mam-
mography receipt (HR 0.91, 95% CI 0.41-2.00).
Our results were the same for women who had a previ-
ous mammogram with or without age adjustment and liv-
ing status adjustment in the base model. The relationship be-
tween income and mammography was attenuated when age
adjustment and living status adjustment were included in
the base model, but the mediators identified did not change.
Discussion
We identified several attitudes and facilitating conditions
that mediated the effect of income on mammography use in
a population of women who have insurance coverage. Re-
gardless of a woman’s past behavior, three variables
emerged as important targets for future interventions: belief
in the necessity of mammograms, recommendations from
friends, and ease of arranging transportation to the ap-
pointment. Women with no prior mammography experience
reported several additional negative beliefs. If interventions
are able to change these attitudes and facilitating conditions,
mammography use may increase among low-income women
whose direct cost barrier has been removed. Interventionists
should direct their resources at addressing negative attitudes
and transportation issues when designing programs targeted
at low-income women with insurance.
This study also demonstrates that there can be disparities in
the uptake of mammography, and likely other preventive
healthcare, even among individuals with access to medical
care. Providing coverage for mammograms cannot be expected
to be sufficient to remove all barriers to its use; addressing the
importance of early detection and variables related to ease of
getting a mammogram must also be addressed to improve
screening among low-income populations. Addressing these
issues needs some careful thought, however, as the parent trial
explicitly addressed attitudes and facilitating conditions in mo-
tivational interviews that did not result in higher participation
than with simple reminder calls.
5,17
There are some limitations to our findings. First, the re-
sults may not be generalizable to women seeking care in the
general community who do not have health insurance or a
usual source of healthcare.
18
Low-income women with these
types of barriers to accessing healthcare probably have more
structural barriers to obtaining a mammogram. A second
limitation is our measure of income. We did not ask if these
women had additional assets. However, a study among
Medicare beneficiaries showed that low-income people
tended to have minimal assets; thus, our population is un-
likely to have substantial savings that might cause misclas-
sification and invalidation of the study findings.
19
Income
information was missing for 19.6% of women, although non-
response was not related to mammography use. Thus, non-
response bias is not likely to threaten the validity of our find-
ings. Misclassification may result in women living alone or
in families both being included in the low-income group, but
this potential misclassification would tend to reduce the
strength of the relationship between income and mammog-
raphy use if women had more resources than we attributed
to them through our income level classification.
The age of the data might be of concern. Although some
variables may have changed since the data were collected,
the findings about beliefs, transportation, and provider rec-
ommendation are still important barriers to receiving mam-
mography among low-income women, as supported by
more recent literature.
8,20–23
A study in the U.K.
24
found that
cognitive variables (benefits, barriers, fears, and fatalism)
eliminated differences by socioeconomic status in intention
for colorectal cancer screening, which supports our findings
in the United States. We know of no prior studies examin-
ing mediators that may be manipulated to encourage low-
income women with insurance to get screened.
Strengths of the study include a population-based sample
because it included a random sample of the entire popula-
tion of enrollees in a managed care plan as well as individ-
ual-level data on women’s perceptions, attitudes, and beliefs.
Additionally, longitudinal data were used to test associations
prospectively, which is important for designing interven-
tions, as prospective data have been shown to serve as a
richer set of predictors of future behavior than cross-sectional
data.
25
Conducting the study in an integrated health plan en-
abled an examination of barriers beyond cost. The study was
conducted in a closed healthcare setting where the outcome
could be ascertained through administrative files, eliminat-
ing recall bias and overestimation of the outcome commonly
found when using self-reported data.
26
Conclusions
The results of this study should be used to inform future
mammography interventions, especially efforts to reach low-
income women in managed care settings. Recent work
23
sug-
gests that even today lower income women are under-
screened, so increasing mammography use in low-income
populations may be an important step to addressing socioe-
conomic disparities in breast cancer mortality. An efficacious
tailored approach to a woman’s past behavior that addresses
specific beliefs about the importance of mammography and
facilitates transportation to the appointment may also in-
crease use among all groups of women.
Acknowledgments
We extend our appreciation to Sue Curry, Ph.D., Evette
Ludman, Ph.D., and William Barlow, Ph.D., who helped de-
velop and conduct the original study.
Disclosure Statement
No competing financial interests exist.
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Address reprint requests to:
Diana S.M. Buist, Ph.D., M.P.H.
Group Health Center for Health Studies
1730 Minor Avenue, Suite 1600
Seattle, WA 98101
E-mail: buist.d@ghc.org
PARK ET AL.1378
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    • "Second, in order to examine potential mediators between race/ethnicity and mortality we followed a method specific to Cox models www.ccsenet.org/gjhs Global Journal of Health Science Vol. 8, No. 2; 2016 (Park, Buist, Tiro, & Taplin, 2008; Barron & Kenny, 1986). First we estimated a Cox model for race alone to ascertain the hazard ratios on race. "
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  • [Show abstract] [Hide abstract] ABSTRACT: Breast cancer is a significant cause of mortality and morbidity. A strong association exists between survival and early detection through regular mammography. Impoverished women underuse this life-saving screening, resulting in a disproportionate cancer burden. The study purpose is to discover the process of rarely or never-screened women's mammography-screening decisions. The sample consists of five rural, low-income, uninsured, and rarely or never-screened women. Grounded theory methodology is used to generate a new theoretical explanation of mammography-screening decision making. Findings include the central conceptual categories, intuitive dominance and intuitive certainty, which contribute toward an intuitive decision-making default. This intuitive thinking style weaves throughout two interrelated categories: (a) scarce, supportive, relational resources for learning and (b) dichotomous health care-seeking behaviors. Implications focus on a nontraditional client assessment whereby nurses can facilitate relational-based knowledge construction. Recommendations for future research include examination of the process of integrating intuition with reasoned thought for more fully informed decisions.
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    [Show abstract] [Hide abstract] ABSTRACT: The expectation that insurance coverage mitigates health disparities and equalizes use of healthcare assumes that services are equally accessed; however, the insured low-income target population in this research had a mammography rate of 23.4%, well below the general population. Our objective was to determine the most effective intervention to improve mammography use in low-income women insured by a managed care organization (MCO). The study was a randomized controlled trial. Participants were 2,357 women noncompliant with screening mammography randomly assigned to one of three groups: control (n = 786) received usual care; simple intervention (n = 785) received prompt letter from the MCO medical director; and stepwise intervention (n = 786) received the same prompt letter from the MCO; if noncompliant, a second prompt letter from their primary care physician and, if still noncompliant, counseling from lay health workers. Outcome was completion of screening mammography extracted from medical records. Screening rates were 13.4% for the control, 16.1% for the simple intervention, and 27.1% for the stepwise intervention. Compared with the control, the primary care physician letter in the stepwise intervention increased the likelihood of screening by 80% [Relative Risk (RR) = 1.80; P < 0.001], and counseling tripled the likelihood of screening (RR = 3.11; P < 0.001). Compared with the control and simple intervention, a stepwise intervention to increase mammography is effective in a target population of hard-to-reach, low-income, insured women. The research provides evidence for the impact of stepwise interventions to improve cancer screening in low-income insured populations, although the screening rates remain well below those of the general population.
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