Introduction barrier to get screened. The PPACA healthcare reform


cancer is a major public health problem of social, biomedical, epidemiologic
and economic interest. It is the most commonly diagnosed cancer in women in
Texas with more than 17,000 cases expected to be diagnosed in 2017.1 Literature
suggests that there are various factors independently affecting breast cancer
screening rates amongst women. 2,3,4 The objective of this paper is
to examine the association between socioeconomic and health insurance status and
mammogram screening utilization amongst women in Texas. This might have a
significant impact on expanding the use of the available breast cancer
screening services within the state amongst heterogeneous groups of women, on assessing
current policies and might initiate efforts for review and expansion for better
outcomes in the future.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

Mammography is an x-ray imaging method used to examine the breast
for the early detection of cancer and other breast diseases. It is used both as
a diagnostic and screening tool.5 The official guidelines from 2009 to 2015 recommend
biennial and annual breast cancer screening for women after the age of 40 and
women at high risk. 6,7,8,9 Although the state provides
screenings at no cost to eligible and low-income Texan women and more women, in general, can get these services
starting from 2016 (Healthy Texas Women program)10, this paper will
address the effects of these provisions, by exploring the current trends in
screening rates.

factors impact breast screening services utilization, which influences the stage of diagnosis and mortality
from this disease.  Aging is
the biggest risk factor for breast cancer; 11 the longer people
live, the bigger the probability of
genetic damage (mutations) in the body. It is clear
from this argument and widely supported by the literature, that
screening rates amongst women tend to rise with age.11 However, health
insurance and socioeconomic status (SES) have constantly been related to having
a major impact on health status in general and cancer screening rates in extension, by multiple studies.12,13,14,15

     The lack of
health insurance is a significant barrier to
get screened. The PPACA healthcare reform expanded coverage for diagnostic
services by requiring private health insurers to provide and cover mammograms
and prohibiting cost-sharing, to increase access.16 Expanded
coverage could increase accessibility and positively affect the use of
preventive services. However, women without federal or private health insurance
might face financial barriers to obtain mammograms.

differences in screening rates amongst women cannot only be attributed to age
and health insurance. Women with low SES might have substantial difficulties to
seek and access healthcare services. Low income
might lead to less utilization of breast cancer screening services because of
financial barriers to obtain these services.17 Women with less education might also be more
vulnerable due to difficulties in understanding the value and benefits of
preventive medicine and screening in particular or unable to translate physicians’
advice and guidelines into action.13,18

disparities have also been related to lower breast cancer screenings in the
literature. Minorities may have a negative attitude towards health-services due
to cultural factors shaping diverse opinions about health, bad experiences with their delivery and physicians’
approach and advising.13,19 Time constraints and difficulty in
access must also be considered as determinants when measuring screening
percentages as significant barriers.20 Some researchers have also concluded
that smoking and risky health behaviors are negatively associated with the use
of breast cancer screening.17 These behaviors might differentiate
outcomes between women, even if they belong in a highly homogeneous group.

studies have pointed at different factors as key for varying rates amongst
women. Keeping in mind that each state and the Nation in total has unique
characteristics, this paper will try to explore the association between certain
identifiers, which are most commonly cited in the
literature, and breast cancer screening rates in Texas. It is very
important to be aware of the causality in this relationship, to assess current
policies and address future changes for improvement.



Study Population

     The sample of the population studied were
women aged 18 and older living in the State of Texas including 6,403 women for
the year 2016.


2016 Behavioral Risk Factor Risk Surveillance System (BRFSS) data for public
use in Texas were used for this study. The BRFSS is a survey based on
self-reported answers on questions about health-related risk
behaviors, chronic health conditions, and use of preventive services. Landline
and cellular telephone numbers are used to gather information about Texas
residents at random.21


     The model of this study suggests that the utilization
of breast cancer screening services (mammograms) is determined by personal
characteristics of women, such as age and race; by socioeconomic factors that could
possibly impede access, such as income and education; by factors that measure
access to services, such as health insurance, employment status and disabilities;
and others, such as perceived self-reported health status, marital status and
smoking, which could be attributed to individual behaviors influencing women’s
choices and preferences.

Variables & Hypotheses

Dependent Variable

dependent variable was whether a woman ever had a mammogram, a dichotomous
nominal variable. Breast cancer screening rates were obtained in the BRFSS
survey by asking women “Did you ever have a mammogram?”, with the answer being
Yes or No (0=No, 1=Yes).

Independent Variables

this analysis 10 independent variables were taken into account, which were categorized
in 4 groups, accounting for SES, Demographic characteristics, access to
services and personal behavioral indicators. Income and education were used to
describe the socioeconomic status (SES) of women, both as ordinal variables. Income
was coded in 3 groups with values 1 to 3 and representing income categories of
<25,000$, 25,000$ to 75,000$ and >75,000$. Education was categorized into 4 groups as 65 as an ordinal
variable. Race was a nominal variable in
the model categorized into four groups.
These were representing Black, White, Hispanic, Other race valued from 1 to 4. Age,
as described in the introduction, is directly related to breast cancer. The
pathophysiology of the disease itself is based on mutations in genes because of
aging.11 Having this in mind and although the relationship between
age and breast cancer is well known, it has still to be included in this model,
to maintain a realistic outcome in my findings; older women will have higher
rate of breast cancer screening (Hypothesis

studies during the past decade have proven a racial pattern amongst screening
rates. Hispanic, Black and other racial groups and minorities consistently
produce different results than White women.23 Particularly, research
concludes that White women tend to have significantly higher screening rates
percentages than the prementioned racial groups.4,15 I anticipate
finding these results in my analysis as well, assuming that White women will
have bigger ratings in mammogram screening than the other racial/ethnic groups in
the data (Hypothesis 4).

access and utilization of health services are very complex and difficult to
capture, 3 additional variables were used to incorporate the impact of
differences of this nature in my model; health insurance status (variable 5),
employment (variable 6) and disability status (variable 7).  All these variables were included as nominal
dichotomous variables, with “No” valued as 0 and “Yes” as 1. The general
benefits of having health insurance are well-established. Some researchers have
proven that having health insurance or not was a strong indicator of screening
utilization rates, even related to women’s breast cancer morbidity.24
Similarly, employment is inherently linked with income and health insurance;
being employed or not might lead to opposing results when measuring
accessibility and utilization of services. Unemployment can turn out to be a
major barrier on breast cancer screening services. A positive relationship is
expected between health insurance, employment
and screening. (Hypotheses 5 & 6).    

     On the
other hand, some researchers have concluded that women with a disability are less likely to adhere to
guidelines for mammograms since these
conditions could solely stand as barriers for women to obtain services.25
Some others however claim that fear and anxiety related to poor health status
might have the exact opposite results.26,27 I expect to find that
there is a relationship between these two variables, but the direction is yet
to be examined (Hypothesis 7).

     Finally, 3 more
variables were added to explain the role of personal behaviors and preferences
towards mammograms. These were marital status (variable 8), smoking (variable 9),
and self-reported health status (variable 10). Marital status and smoking were
computed as dichotomous variables. Self-reported health status was valued from
1 to 5, with 1 representing Poor status and 5 Excellent; an ordinal variable. The literature associating health and marriage is
voluminous. Marriage positively affects health by increasing the likelihood of
healthy behaviors and by improving access to services and resources through
higher combined income capacity or the spouse’s health insurance policy.28
I expect to find the same relationship in my analysis (Hypothesis 8).

     Higher rates of screening have been
attributed to greater concerns and protective behavior.26 The better
people feel about themselves, the more invincible they feel; the term “invincibles”
is common for adults who believe that they face no risk, due to their current
health status. They are more indifferent towards services like these, as they
feel that they do not need them.27 Women with worse self-perceived
health status are more likely to adhere to mammography guidelines, to monitor
and improve their health (Hypothesis 9).

     Finally, some studies have linked smoking
with lower screening adherence and rates and their results indicate a negative
association between cigarette smoking and breast cancer screening rates, the
relationship I expect to find as well (Hypothesis


     Multivariate logistic regression analysis
was used to test the relationship between breast cancer screening and the
independent variables possibly affecting it. Bivariate correlations were also
examined. The analysis was performed using Stata v14.2 for Windows.



     From the overall sample of women, 81.09 % reported
that they had at least one mammogram. Table 1 presents the descriptive
statistics results for each variable included in my analysis. As stated in the
theoretical part, screening rates are directly related to age and women between
45 to 64 and >65 reported screening rates at 93.22% and 96.84%; the
difference with the other two subgroups of younger women is obvious.

     Socioeconomic status in which this paper
is focused is measured by income and
educational level. Women with income less than $25,000 were less likely to undergo
screening, as 23.53% of those were never screened for breast cancer. In
contrast, the non-screening percentages for women with an income between
$25,000 -$75,000 were 16.77% and with more than $75,000 the result was 19.56%.
While the differences regarding income are clear, this is not the same with
education; women with some college and high school education were more likely
to have been screened than women who were college graduates. These results do
not point at a specific pattern between education and mammography and do not
follow researchers’ finding.22 On the other hand, mammography was positively
related to health insurance; women with health insurance had an 84.54% of
screening, whereas the absence of insurance was associated with an adherence
percentage of 55.66%. Similarly, women who did not report a disability had
lower breast-cancer screening rates; 23.2% of these women never had a
mammogram. In contrast, this percentage was 11.13% for women who claimed to
have a disability, a result similar to the part of the literature that points
at this direction.26,27

     Racial disparities are another outcome of
this table, with White women more likely to be screened (86.2%) than all three
other race groups, (Black-80%, Hispanic-67.4%, Other race-69%). Smoking seems
to have the proposed negative association that some scholars identified, with
women who smoke having bigger percentages of non-screenings than non-smokers
(22.32% versus 18.52%).17 Recalling the prementioned term
invincibles, self-reported health status follows the expected trends. Women who
described their health as Excellent had the smallest percentage of mammograms
at 74.84%, whereas women claiming their health was
Poor had the biggest percentage at 89.59%, a negative relationship
consistent with the theory.27 Marriage
produced almost similar results regardless the status, with a married woman having slightly bigger rates of
screening. Finally, employment revealed surprising results. In contrast to my
hypothesis and the literature, employed Texan women are less likely to have
been screened than unemployed women (71.65% for employed and 86.88% for
unemployed), which revealed an unexpected negative association.


I'm Neil!

Would you like to get a custom essay? How about receiving a customized one?

Check it out