DRAFT
This in turn may affect the never screeners group with younger women in the cohort
being more likely to have never screened.
A categorical variable for women from a non-English speaking background was included
to determine whether these women face greater barriers to obtaining information about
the program compared to their English speaking counterparts. A categorical variable for
women who were born overseas was included in the model. It is anticipated that these
women are at greater risk of not being recruited by the Breast Screen program because a
larger proportion of them may not be on the electoral roll.
SES was included in the model using household income and a woman’s education
attainment. We also included a category for those who did not state their income, which
accounted for 22% of the sample. These variables will be used to test for systematic
variation and highlight potential inequities in screening rates. The number of hours
worked by a woman in the last week was included in the model as a potential measure of
the opportunity cost of time. Women who did not have a job in the last week were
assigned zero hours.
Regional variables were based on the boundaries of the seventeen AHS in NSW, shown
in Figure 1. The Breast Screen program is regionally organised with similar boundaries to
the AHS.
In addition, women were categorised as living in one of five geographic locations based
on the “Accessibility/Remoteness Index of Australia” (ARIA). ARIA defines remoteness
on a geographical basis and calculates accessibility to some 201 service centres based on
road distances (see Table I for variable definitions). The ARIA variable enables us to
examine the impact of remoteness as a proxy for the opportunity costs of screening. It
will also allow us to more clearly isolate the potential role of AHS organisational aspects.
In reporting the results, AHS are grouped into inner metropolitan, outer metropolitan and
rural areas to determine whether any systematic variation remains in these regions. The