Continuum of mammography use among US women: classification tree analysis.

In order to achieve the morbidity and mortality reduction benefits of breast cancer screening, there must be high rates of mammography utilization across all sectors of the screening-eligible population. The Healthy People 2020 goal is for 81.1% of women age 50-74 to have at least one mammography in the past 2 years (age adjusted to the year 2000).1 Yet, even though population utilization rates have become relatively high in the past 20 years, the increase seems to have plateaued.2-4 The plateau has been attributed to a small decrease in the percent of recent mammography among women who were considered to be early adopters of mammography (women age 50-64, insured women, women with higher incomes and white women).4,5 In addition, pockets of low utilization remain, such as the uninsured, those who have no source of usual care or use an emergency department as their main source of care, women with less than a high school education, and women living in the United States for less than 10 years.6 Precise specification of groups with low utilization allows us to identify their salient barriers and facilitators, which better informs intervention audience targeting and the content of intervention messages and materials. Surveillance of crude utilization rates and multivariable logistic regression have been typical ways to identify groups in the population with underutilization, as well for identifying the correlates of lower utilization. There are, however, constraints to this approach for population segmentation and intervention targeting. Crude-rate surveillance often uses only 1- or 2-variable classifications for specifying at-risk populations. Logistic regression produces refined (adjusted) estimates of the associations between independent variables and screening status, controlling for correlations among the independent variables. However, logistic regression output does not directly indicate how to combine variables, except additively, to specify low utilization groups using several variables concurrently; nor does the output specify which variables to combine. Non-linear associations with a dependent variable, such as U-shaped, inverted U-shaped (quadratic), or functions with asymptotes (those whose association plateaus after a “threshold” value on an independent variable), are also not routinely identified by logistic regression analysis. In addition, main-effects logistic regression results are based on a logic of “ors.” That is, the main effects associations denote the risk of underutilization for persons with ‘characteristic #1,’ or ‘characteristic #2,’ or ‘characteristic #3’. In practice, however, interventions are conducted with priority populations who are defined by several characteristics simultaneously, which is a logic of “ands” (eg, urban, low-income women, and are members of one or more racial/ethnic minorities, and are without a regular source of care). For intervention planning, the specification of barriers and facilitators is most effective when it can be done for persons who have combinations of characteristics. It is therefore important to have a methodology that takes advantage of information available from crude screening rates and from identification of groups with low utilization based on a logic of “ands,” so that the resulting classification of priority populations is based on several characteristics in combination. An analytic method that can be used to identify groups with low utilization is Classification Tree Analysis (CTA).7,8 Classification tree analysis is a non-parametric multivariable technique, sometimes referred to as signal detection, which is designed to identify combinations of variables efficiently. The results of a classification tree inherently show combinations among independent variables as a function of where they are most relevant in the process of tree creation. CTA also automatically combines categories of an independent variable that have similar status on a dependent variable, which can account for non-linear relationships such as U-shaped, inverted-U, or threshold associations. In addition, unlike discriminate analysis, CTA does not require continuous-valued independent variables and allows for non-linearity in its segmentation scheme. The objective of this study was to identify groups based on health status, health access, health behaviors and demographic characteristics in combination along the range of mammography utilization. In relation to breast cancer screening compliance, CTA has been used to identify subgroups of women age 50-75 who were not up-to-date on mammography screening,6,9 to prospectively identify those who did not adhere to mammography screening guidelines,10 to identify subgroups of the National Cancer Institute's Cancer Information Services users,9 and to predict mammography using population-based data in Portugal.11 However, CTA has not been frequently used either with national-level mammography data specifically, or cancer screening behavior more broadly.