BMC Cancer 2007, 7:201 http://www.biomedcentral.com/1471-2407/7/201 tained) because their screening habits are likely influenced by their prior diagnoses and routine medical surveillance. We excluded 22 women for whom mam- mography status was not ascertained. Thus, our final ana- lytic sample (N = 1521) included women with the following ethnic identifications: Chinese (n = 382), Japa- nese (n = 275), Filipino (n = 269), Korean (n = 244), South Asian (n = 125), and Vietnamese (n = 226). The South Asian subgroup includes women of self-reported descent from South Asian countries such as India, and Pakistan. Outcome and explanatory variables for recursive partitioning RP [41,42] was used to identify mutually exclusive sub- groups, with variations in outcome, delineated by combi- nations of explanatory characteristics. The dichotomous outcome variable represented categorization of responses to the question "How long ago did you have your most recent mammogram?". We selected a cut-off of two years in reference to the Healthy People 2010 goal of having 70% of women in the United States receive a mammo- gram within the past two years [43]. In addition, women were considered to have received a screening mammo- gram within the past two years if it was conducted "as part of a routine physical exam or screening"; other reasons (7% of respondents), including "because of a specific breast problem", "as a follow-up to a previously identified breast problem", and "as a result of a baseline or initial mammogram" were considered mammograms that were conducted for diagnostic purposes. The 25 explanatory variables submitted into the RP proce- dure are presented in Table 1. These variables were selected from among the CHIS questions because they have previously been shown or were hypothesized to be associated with mammography screening. Two of us (SLG & ST) independently reviewed the CHIS variables and selected those to be included in the RP analysis; discrep- ancies were resolved by discussion. Prior to RP analysis, several variables were modified from their original CHIS format, including income, which was adjusted for house- hold size, and percentage of lifetime in the U.S., which was derived by dividing the number of years lived in the U.S. by age in years. Categories of English-speaking profi- ciency (speaks English only, very well, well, or not well) and education were grouped (Table 1) according to the distributions for each subgroup to overcome sparse num- bers in the sub-categories. We also included a variable des- ignating eligibility for public programs, including public housing subsidies, general assistance and relief, food stamps, disability, or Women, Infants, and Children (WIC). We included two variables characterizing pre- existing health conditions: 1) having had pre-existing health issues, which included self-report of at least one of the following: arthritis, asthma, diabetes, or high blood pressure; 2) having women's health issues, which included at least one of the following: taking hormone supplements, history of osteoporosis, or hysterectomy. We created a variable indicating ever having had a Pap exam to screen for cervical cancer based on the question "Have you ever had a Pap smear test to check for cervical cancer." We created another variable indicating ever hav- ing been screened for colorectal cancer based on the ques- tions "Have you ever had a Sigmoidoscopy, Colonoscopy, or a Proctoscopy to look for signs of cancer or other prob- lems in your colon" and "Have you ever done a blood stool test, using a home test kit?"; women were considered to have ever been screened for colorectal cancer if their most recent colorectal exam was conducted "as part of a routine physical exam or screening test"; other reasons including "because of a specific problem" or "as a follow- up to an earlier test or screening exam" were considered procedures done for diagnostic purposes. We also com- bined the Pap and colorectal cancer screening variables into one variable indicating ever use of other cancer screening tests. For all of the explanatory variables, "refused", "don't know", or "not ascertained" responses to survey questions were coded as missing in the analysis. Statistical analysis & recursive partitioning We used RP, a non-parametric method that produces a classification tree in which subjects are assigned to mutu- ally exclusive subsets according to a set of explanatory var- iables. Unlike traditional regression methods, highly correlated variables can be entered simultaneously because RP manages variables individually. The first step in RP involves examining each explanatory variable and selecting one binary split across the sample on one explanatory variable that minimizes the within-group var- iance in the outcome variable in the two resultant nodes. This process is repeated for subsequent explanatory varia- bles until further partitioning is not possible; however, the final tree, with small numbers of subjects in the termi- nal nodes (minimum of 5, per our stopping rule), is sub- ject to high misclassification errors. Therefore, the next step involves "pruning" the tree by sequentially cutting away terminal nodes. The optimal tree, selected via cross- validation (ten-fold in our analysis), has the most splits but the lowest misclassification rate [44]. Additional details about RP are available elsewhere [42,44-46]. RP analyses were conducted using the RPART routine in the R statistical software program [45]. For the variables English proficiency and education, for which two versions of each variable are created depending on the Asian subgroup, the values are set to "missing" for individuals for whom the variable does not apply. For example, for the variable edu- cation (among only Vietnamese), this variable is set to missing for women who are not Vietnamese. One of the most powerful advantages of the RPART procedure is its Page 3 of 12 (page number not for citation purposes)