Applying Recursive Partitioning to a Prospective Study of Factors Associated with Adherence to Mammography Screening Guidelines

Lisa Calvocoressi1,2, Marilyn Stolar1, Stanislav V. Kasl1, Elizabeth B. Claus1 and Beth A. Jones1

1 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT
2 Department of Medical Education, Griffin Hospital, Derby, CT

Correspondence to Dr. Lisa Calvocoressi, Department of Medical Education, Griffin Hospital, 130 Division Street, Derby, CT 06418 (e-mail: lisa.calvocoressi{at}yale.edu).

Received for publication February 9, 2005. Accepted for publication July 8, 2005.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Although a number of predictors of adherence to mammography screening guidelines have been identified using traditional statistical methods, many women are not screening according to these guidelines. Recursive partitioning may aid in developing novel intervention strategies to promote this screening behavior by identifying subgroups of women that differ on adherence across predictor variables. In a prospective study of 1,229 African-American and White women in Connecticut whose adherence to mammography screening guidelines was ascertained over a 26-month follow-up period from initial screening in 1996–1998, recursive partitioning selected six of 22 candidate predictors and identified subgroups that differed on adherence across predictors by age (40–49 and 50–79 years). Among the five subgroups identified for women aged 50–79 years, the subgroup most adherent to screening guidelines during follow-up included four predictors: a history of adherence, annual family income of $15,000 or more, a belief that mammograms were very useful, and low or moderate perceived breast cancer susceptibility. Among the three subgroups identified for women aged 40–49 years, the most adherent subgroup included only one predictor: receipt of a health-care provider's recommendation to obtain a mammogram. These findings suggest that recursive partitioning may be a useful statistical tool and may aid in developing interventions to promote adherence to mammography screening guidelines.

age factors; epidemiologic methods; mammography; mass screening; risk assessment


Abbreviations: CART, classification and regression tree


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Most medical and public health organizations in the United States recommend that women obtain annual or biennial screening mammograms beginning at 40 years of age (1Go, 2Go). Compared with less frequent use, regular screening increases the likelihood of detecting breast tumors at an early and more treatable stage (3Go), and it is key to maximizing the efficacy of this early detection method.

Across studies, predictors of adherence to regular screening include demographic variables such as younger age (4Go–6Go), White race (7Go–9Go), higher income and more education (7Go–11Go), and being married (7Go, 11Go, 12Go). Additional predictors include having health insurance (8Go, 9Go, 13Go), having a regular health-care provider (12Go–14Go), receiving a provider's recommendation or reminder notice to obtain a mammogram (4Go, 6Go, 12Go, 14Go–17Go), having better self-reported health (14Go, 18Go), having a family history of breast cancer (8Go, 13Go, 19Go, 20Go), participating in regular physical exercise (21Go, 22Go), not smoking (12Go, 16Go), having knowledge of breast screening guidelines (4Go, 6Go, 20Go, 23Go), and having a past history of screening (24Go–26Go). Psychosocial predictors of adherence include a belief that mammography is beneficial (4Go, 18Go, 26Go), confidence in one's ability to obtain a mammogram (26Go), and perceived control over the effects of breast cancer (4Go). High perceived susceptibility to breast cancer has predicted adherence in most studies (6Go, 13Go, 15Go) but has been found to adversely affect adherence in some investigations (27Go–30Go). In addition, past mammography experiences marked by embarrassment, pain, or anxiety have been implicated as screening barriers (18Go, 23Go, 31Go). Notwithstanding the many predictors, however, adherence to screening guidelines is suboptimal (32Go, 33Go). A comprehensive review between 1990 and 2001 found that only 46 percent of eligible women were screening according to established guidelines (34Go). Further work is thus needed to understand how predictors of adherence may be utilized to develop effective strategies to promote this screening behavior.

Effective utilization of predictors to promote adherence may be compromised because knowledge of these predictors is confined to their individual and independent influences on adherence, derived from standard bivariate and multivariate analyses. Identifying a manageable number of pertinent predictors and investigating whether, and for whom, certain combinations of predictors are associated with adherence may further our understanding of factors motivating adherence and enhance our ability to develop effective intervention strategies. Recursive partitioning, a nonparametric technique used with increasing frequency in clinical research and epidemiologic studies (35Go), is well suited to these tasks. Also known as tree analysis, recursive partitioning can distinguish a subset of important variables from a larger pool of "candidate predictors" and can classify subjects into well-separated subgroups with respect to the outcome of interest across these predictor variables (36Go–38Go). The results of a recursive partitioning analysis are presented in the form of an inverted "tree" in which the sample has been repeatedly split into binary subgroups based on examining all candidate variables and, for each split, selecting the predictor that best partitions the sample into relatively homogeneous subgroups based on the outcome. When tested on learning samples, tree analysis has produced models with better predictive accuracy than parametric methods (36Go, 38Go, 39Go). For a detailed explication of the recursive partitioning method and its statistical underpinnings, we refer the reader interested to reports by Breiman et al. (36Go) and Zhang and Singer (37Go).

We examined data from the prospective cohort study, Race Differences in the Screening Mammography Process, which included a broad range of potential predictors of adherence to screening guidelines. By use of recursive partitioning, we sought 1) to identify a subset of variables that predicted adherence to screening guidelines and 2) to delineate subgroups across these predictors that differed in the proportions of adherent women. In keeping with the exploratory nature of recursive partitioning, we allowed the forms of relations among predictors and outcome to become manifest (40Go) and did not specify hypotheses concerning these relations in advance.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Study population, procedures, and participation
The study, Race Differences in the Screening Mammography Process, was a prospective cohort study described in detail elsewhere (27Go). The study population included 1,451 women aged 40–79 years (African American, 43.8 percent; White, 56.2 percent) who presented for a screening mammogram ("index screening") at five urban hospital-based screening facilities in Connecticut between October 1996 and January 1998. We included all eligible African-American women who presented for screening We then randomly selected an equal number of White women, frequency matched to the African Americans on mammography date and screening facility. We excluded women with a previous history of breast malignancy, cyst aspiration, or breast biopsy and those who obtained the index examination for diagnostic purposes.

Participants completed a baseline telephone interview on average 1.5 months after the index screening (standard deviation, 0.85 month; range, 1–6 months), as well as a follow-up telephone interview on average 29.4 months thereafter (standard deviation, 1.42 months; range, 27–41 months). The baseline questionnaire included sociodemographic, health history, medical care, behavioral, and psychosocial factors. The follow-up questionnaire included information on mammograms obtained subsequent to the index screening as well as other variables. Approvals of institutional review boards of this institution and of participating hospitals were obtained to conduct the study. Oral consent for participation in the study interviews was obtained following prospective respondents' review of a study information sheet and discussion with a trained study interviewer.

Of the 1,451 women who completed baseline interviews (73 percent participation), 1,249 participated in follow-up interviews (86 percent). Twenty women who completed a follow-up interview were excluded because they provided insufficient information to determine their adherence to guidelines during follow-up, or because they were diagnosed with cancer following study entry and did not subsequently adhere to a regular screening schedule. The total number of women thus available for this analysis was 1,229 (39.4 percent African Americans and 60.6 percent Whites).

Variables
We assessed the outcome, adherence to mammography screening guidelines, at follow-up based on American Cancer Society guidelines in effect in 1996, at the onset of this study's data collection period: annual screening of women aged 50 years or more; one screening of women aged 40–49 years every 1–2 years (41Go). We thus considered women aged 50–79 years adherent if they obtained at least two screenings within 26 months (2 years + 2 months) of the index examination. We considered women aged 40–49 years adherent if they obtained at least one screening within 26 months (2 years + 2 months) of that examination.

For the tree analysis, we selected 22 candidate predictors to examine in relation to adherence to screening guidelines, based on factors associated with adherence in the published literature (4Go–26Go, 31Go). These included the following: 1) sociodemographics (race/ethnicity, age, marital status, education, annual family income); 2) health-care factors (full annual mammography insurance coverage, having the same (i.e., usual) health-care provider over the past year, receipt of a health-care provider's recommendation to obtain a mammogram subsequent to the index screening, receipt of a reminder notice to obtain a mammogram subsequent to that screening); 3) health status and behaviors (self-rated health, history of breast cancer in a first- or second-degree relative, participation in any form of aerobic exercise at least once a week, pack-years of smoking); 4) mammography-related factors (knowledge of mammography screening guidelines, past history of adherence to mammography screening guidelines prior to the index screening); and 5) psychosocial factors (perceived susceptibility to developing breast cancer in one's lifetime, perceived usefulness of mammograms for detecting breast abnormalities, embarrassment experienced during the index screening, pain experienced during index screening compared with expectations, anxiety experienced during index screening compared with expectations, confidence in one's ability to make arrangements to obtain a future mammogram, perceived control over cancer recovery). With the exception of receipt of a provider's recommendation and receipt of a reminder notice to obtain a mammogram subsequent to the index screening that were assessed at follow-up, information on the candidate predictors was obtained during the baseline interview. Specific coding of each predictor is shown in table 1. For the logistic regression analysis shown in that table, we combined categories that included few subjects (e.g., somewhat/a little/not at all useful/don't know). For the recursive partitioning analysis, classification and regression tree (CART) software automatically dichotomized variables with multiple categories.


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TABLE 1. Frequency distributions and bivariate logistic regression analysis of candidate predictors of adherence to mammography screening guidelines, Connecticut, 1996–2000

 
Data analysis
We used logistic regression to generate odds ratios and 95 percent confidence intervals to examine the bivariate relation between each candidate predictor and adherence to mammography screening guidelines. These tests were two sided. The analyses were conducted with SAS, version 8.2, software (SAS Institute, Inc., Cary, North Carolina). To conduct the tree analysis, we used automated CART software (36Go, 38Go).

To form the classification tree, CART repeatedly partitioned or split the study population into binary subgroups (i.e., nodes). To determine which variable to use for each split, CART examined all possible binary splits of the sample by each candidate predictor. CART then selected the predictor (and its particular dichotomization) that split the sample into the most homogeneous binary subgroups based on adherence status (yes/no). The Gini impurity criterion (38Go) that measures and ranks the extent to which each split departs from complete homogeneity (i.e., where all subjects in one branch of the split have the outcome under study and all subjects in the other branch do not have the outcome) was used for this purpose. For each split, CART selected the variable with the lowest impurity score. For variables with multiple categories, we elected to have CART examine every possible binary combination of those categories to determine the best split. This approach may provide evidence of a relation between predictor and outcome that is not linear (e.g., if the predictor is split into two categories: low/high vs. moderate).

CART fully partitioned or "grew" the tree until the default lower limit of 10 subjects in a node was reached. CART then derived a number of smaller "pruned" trees, based on a 10-fold cross-validation procedure (36Go, 38Go) that identified from among trees of a given size those with the lowest misclassification of subjects on the outcome variable. CART gives the investigator the option of choosing the tree for study with the overall lowest cross-validated misclassification of subjects regardless of size, or the smallest tree with cross-validated misclassification within one standard error of that tree. To obtain a tree of manageable size, we chose the latter. At the bottom of the pruned tree are "terminal" nodes that represent relatively well-separated and homogeneous subgroups across the predictors included in the tree. CART provided the percentages of adherent and nonadherent women in each of these subgroups. We calculated 95 percent confidence intervals for the percentages of adherent women.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Overall, 52.2 percent of women adhered to mammography screening guidelines in the 2 years following receipt of the index screening examination. In addition, most candidate predictors were associated with adherence at the bivariate level (table 1). Adherent women tended to be White, younger, married, and of higher socioeconomic status. There was no difference in age distribution by race (data not shown). In addition, women with a regular health-care provider and those who received a provider's recommendation or a reminder notice to obtain a mammogram were more adherent than were women without these medical system cues (table 1). However, women with full, annual screening mammography insurance were no more adherent than were women without this coverage. Women with better adherence were more likely to correctly identify their age-specific screening guidelines and to have a history of following mammography screening guidelines before the index examination. They also reported better health, exercised regularly, smoked less, and more often had a family history of breast cancer. Further, they thought mammograms were very useful, had confidence in their ability to obtain a future mammogram, and reported greater perceived control over cancer recovery. Women reporting moderate breast cancer susceptibility (i.e., somewhat susceptible) were more adherent than those reporting high or low susceptibility (i.e., very or a little/not susceptible). Embarrassment, pain, and anxiety were not significant screening barriers.

In the recursive partitioning analysis (figure 1), the total sample (n = 1,229) comprised the "root" node at the top of the classification tree. CART selected age to split this sample into adherent and nonadherent subgroups and dichotomized the four age categories into the following: 1) a group (n = 786) that included the age categories 50–59, 60–69, and 70–79 years (i.e., ages 50–79 years) with 44.0 percent adherence and 2) a group (n = 443) that included women aged 40–49 years with 66.8 percent adherence.



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FIGURE 1. Classification tree of predictors of adherence to mammography screening guidelines by 1,229 women, Connecticut, 1996–2000.

 
Shown to the left in figure 1, CART split the group of women aged 50–79 years by whether they had a past history of adherence to mammography screening guidelines. Among those with a history of adherence (n = 596), 49.2 percent adhered to screening guidelines assessed prospectively, following the index screening. However, among women not adherent by history (n = 190), only 27.9 percent adhered to guidelines after the index examination. The latter comprised the first terminal subgroup (group 1). CART split the remaining women (n = 596) by annual family income. Adherence to guidelines after the index screening was 52.9 percent among women with incomes of $15,000 or more (n = 454) but was only 37.3 percent among those with incomes under $15,000 (n = 142, group 2). CART partitioned the remaining women by perceived usefulness of mammography. Among those who believed that mammograms were very useful (n = 396), 55.8 percent were adherent, but of those who found mammograms less than very useful (i.e., somewhat/a little/not at all useful/don't know) (n = 58) only 32.8 percent were adherent (group 3). CART split the remaining women (n = 396) by their perceived breast cancer susceptibility. Among women in group 4 (n = 14) who believed that they were very susceptible, only 21.4 percent adhered to screening guidelines subsequent to the index examination. Women in group 5 (n = 382) reported lower perceived susceptibility and had much higher adherence (57.1 percent).

Shown to the right in figure 1 are the three subgroups CART identified for women aged 40–49 years. Group 8 (n = 327; 74.3 percent adherence) included only the predictor, receipt of a health-care provider's recommendation to obtain a mammogram. Group 7 (n = 37; 67 percent adherence) included women who did not receive a recommendation and believed that they were moderately susceptible to breast cancer. Group 6 (n = 79; 35.4 percent adherence) included women without a recommendation who reported high or low susceptibility.

Tables 2 and 3 summarize the results of the classification tree for each age group. Among women aged 50–79 years (table 2), those in group 5 were most adherent (57.1 percent). Moreover, the 95 percent confidence interval (52.1 percent, 62.1 percent) did not overlap with that of any other group. In that group, women reported a history of adherence to screening guidelines, had higher family incomes, considered mammography very useful, and believed that their susceptibility to breast cancer was low or moderate. Among women aged 40–49 years (table 3), those who received a provider's recommendation to obtain a screening showed the highest adherence (74.3 percent, 95 percent confidence interval: 69.6 percent, 79.0 percent).


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TABLE 2. Percentages and associated confidence intervals of subgroups of 786 women aged 50–79 years who adhered to mammography screening guidelines across predictors identified by the classification tree, Connecticut, 1996–2000

 

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TABLE 3. Percentages and associated confidence intervals of subgroups of 443 women aged 40–49 years who adhered to mammography screening guidelines across predictors identified by the classification tree, Connecticut, 1996–2000

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Most of the 22 candidate predictors included in this analysis were bivariate predictors of adherence to mammography screening guidelines, with a few exceptions. First, women who reported that they had full insurance coverage for screening mammograms were no more adherent than were women without such coverage. Post hoc analysis (data not shown) found that a majority of women without full insurance coverage either had insurance that covered a part of the cost or qualified for programs that provided free or low-cost mammograms, thus reducing the financial burden of obtaining a mammogram and minimizing the between-group difference. Second, psychosocial barriers to screening (i.e., pain, embarrassment, and anxiety associated with the procedure) did not predict lower adherence. As Champion (4Go) suggested, these variables may be weaker barriers to adherence than to one-time screening, where their adverse influences were first noted.

Notwithstanding the above exceptions, most of the variables examined in this analysis were significant individual predictors of adherence to mammography screening guidelines, thus confirming previous work (4Go–23Go, 26Go). However, several investigators have called for research that goes beyond identifying individual predictors of cancer screening to acknowledging and uncovering the multiplicity of variables, and their interrelations, that impact this health behavior (42Go). In addition, investigators have suggested that cancer screening might be more effectively promoted if interventions were targeted to homogeneous population subgroups with distinctive patterns of cancer screening (43Go), and if individually tailored messages based on a limited number of salient variables could be developed (44Go). By honing in on a subset of important predictors of adherence from a larger candidate pool and by partitioning the study population into subgroups across these predictors, tree analysis may have a role in meeting these research challenges.

In this analysis, CART identified six predictors of adherence, thus identifying a manageable subset of variables on which to intervene. These predictors included demographics and other characteristics that could be used to target population subgroups (i.e., age, income, and history of adherence) and psychosocial variables that might be used to develop tailored interventions (e.g., perceived usefulness of mammography). Because the study population included a broad age range (ages 40–79 years), as well as a large proportion of African-American women in addition to White women, the results of this analysis may apply across these demographic characteristics. However, sampling was limited to urban, hospital-based mammography facilities in Connecticut and may not generalize to women receiving screenings in other settings.

In this sample, age was the demographic characteristic that split the entire sample (i.e., 40–49 and 50–79 years). Moreover, of the six predictors included in the tree, CART selected largely different ones for each age group. For example, although generally regarded as among the strongest predictors of mammography screening (16Go, 45Go), a health-care provider's recommendation to obtain a mammogram predicted adherence in the classification tree only among women aged 40–49 years. Among women aged 50–79 years, history of adherence to screening guidelines instead predicted adherence at follow-up. This may suggest that older women were more likely than younger women to have made a habit of screening (46Go) and were less dependent on a provider's external cue. Additionally, usefulness of mammography and annual income were selected as predictors of adherence only for women aged 50–79 years. One variable selected by CART for both age categories was perceived susceptibility to breast cancer, but the dichotomization of this categorical variable differed by age group. Lower adherence was associated with high perceived susceptibility in women aged 50–79 years and with high or low perceived susceptibility in women aged 40–49 years, suggesting a curvilinear relation between these variables in younger women. By limiting their samples to women aged 50 years or not examining potential age-modifying effects (7Go, 12Go, 13Go, 15Go, 16Go, 18Go, 24Go–26Go), prior studies may have overlooked these and other differences in predictors of influence for younger and older women.

This recursive partitioning analysis suggests age-specific intervention strategies. In women aged 40–49 years, for example, adherence substantially differed among those who did not receive a provider's recommendation to obtain a mammogram, according to their perceptions of susceptibility to breast cancer. While this finding provides insights into the relation between these variables, for intervention purposes it is key that the most adherent women in this age group were those who received a provider's recommendation to obtain a mammogram, regardless of their perceptions of susceptibility. Therefore, the most feasible and potentially efficacious intervention to promote screening among women aged 40–49 years may involve a single variable, that is, mobilizing providers to recommend mammograms.

In women aged 50–79 years, on the other hand, the classification tree suggests several possible intervention strategies. First, among women in this age group who do not have a history of adherence to mammography screening guidelines, a relatively small percentage adhered to guidelines in the 2 years following the index screening. Through review of medical records or by history obtained during the primary care examination, women without a history of adherence could be identified and targeted for intervention. The tree also indicated that relatively few low-income women, regardless of screening history, adhered to screening guidelines during follow-up. As with women without a history of adherence, this finding suggests that low-income women as a group may require particular attention. Among older women with a history of adherence and higher incomes, the tree identified two psychosocial variables that impacted adherence and could be used to develop tailored messages: perceived usefulness of mammography and perceived susceptibility to breast cancer. However, whereas higher perceived usefulness was associated with better adherence, high perceived susceptibility was associated with lower adherence. This finding is consistent with a few prior studies, including our logistic regression analysis, one of a few to prospectively assess the relation between these variables (27Go). It suggests that interventions designed to increase perceptions of susceptibility may not effectively promote adherence and that caution should be exercised when intervening on this variable.

That the psychosocial variables identified by the tree were important predictors of adherence only among women with a history of adherence and higher incomes supports the view that some theories and models of behavior change that include these variables (e.g., the Health Belief Model (47Go)) were developed using, and may be most relevant to, middle-class women (48Go, 49Go). Among low-income women and those without a history of adherence, the tree did not select additional predictors that might have aided in refining interventions for specific segments of these target groups. Additional variables that may have effectively partitioned these groups were not included in this data set.

Furthermore, although this study population included a substantial proportion of African-American women and although race was a significant predictor of adherence in the unadjusted logit analysis, CART did not include this variable in the classification tree. Compared with other candidate predictors, this suggests that race had relatively less impact and that intervening on other features of the study population (e.g., age and income), regardless of race, may more effectively promote adherence to screening guidelines. This is in agreement with prior research, where White (in relation to African-American) race was as an independent predictor of adherence in some (7Go, 9Go, 13Go, 22Go), but not all (15Go, 24Go), previous studies, whereas income has predicted adherence quite consistently (4Go, 5Go, 7Go, 9Go–13Go, 24Go).

However, the investigator may still wish to examine whether predictors of adherence differ for particular subgroups (e.g., race). Because the sample was initially split on age and not race, predictors of adherence for each racial group could not be identified in this analysis. With the automated CART procedure, the variable selected to split the total sample will influence subsequent partitioning and determine the terminal subgroups in a given tree. It is possible that other potentially important individual predictors and relations among these predictors may have been overlooked in this process. To assist the investigator in identifying variables that differentiate the outcome nearly as well as the chosen split, CART provides information on "competing splits" for each partitioning of the data. Furthermore, with CART, the investigator can perform separate analyses by each subgroup of interest (e.g., by race). Alternatively, one can use recursive partitioning software, such as RTREE (37Go), that allows the investigator to force variables of interest into the model.

Despite some limitations, recursive partitioning can perform several functions with relative ease, compared with traditional statistical methods. For example, although recursive partitioning dichotomizes variables with multiple categories and may obscure more complex curvilinear effects, it can easily discern U-shaped relations between ordinal predictors (e.g., perceived susceptibility among younger women) and the outcome of interest. In addition, because the output of a recursive partitioning analysis provides a visual overview of the data, structures in the data that may be less apparent with traditional analyses may become manifest. For example, the striking age difference in predictors of importance to adherence could only have been observed with traditional analyses through the laborious testing of multiple two-way interactions between age and each candidate predictor. Moreover, the subgroups across predictors within each age group, so-called "local interactions" (50Go), could only have been identified through higher order interactions that may not be detected due to insufficient statistical power and may also be difficult to interpret. Thus, recursive partitioning is a potentially useful technique that may provide a more complete understanding of predictors of adherence to mammography screening guidelines and that may aid in developing more effective interventions.


    ACKNOWLEDGMENTS
 
This study was supported by grants RO1-CA-CA70731 from the National Cancer Institute (B. A. J.), RO3 HS11603 from the Agency for Healthcare Research and Quality (L. C.), and 5T32-MH-14235 from the National Institute of Mental Health (L. C.).

The authors wish to thank the following hospitals in Connecticut that allowed access to their patients and medical records: Bridgeport Hospital, Lawrence and Memorial Hospital, St. Francis Hospital and Medical Center, Waterbury Hospital, and Yale-New Haven Hospital. They also wish to thank Lisa Schlenk, project coordinator, for her invaluable assistance.

Conflict of interest: none declared.


    References
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

  1. Von Eschenbach AC. NCI remains committed to current mammography guidelines. Oncologist 2002;7:170–1.[Free Full Text]
  2. Smith RA, Cokkinides V, Eyre HJ. American Cancer Society guidelines for the early detection of cancer, 2005. CA Cancer J Clin 2005;55:31–44.[Abstract/Free Full Text]
  3. Michaelson JS, Satija S, Kopans D, et al. Gauging the impact of breast carcinoma screening in terms of tumor size and death rate. Cancer 2003;98:2114–24.[CrossRef][ISI][Medline]
  4. Champion VL. Compliance with guidelines for mammography screening. Cancer Detect Prev 1992;16:253–8.[ISI][Medline]
  5. Phillips KA, Kerlikowske K, Baker LC, et al. Factors associated with women's adherence to mammography screening guidelines. Health Serv Res 1998;33:29–53.[ISI][Medline]
  6. Halabi S, Skinner CS, Samsa GP, et al. Factors associated with repeat mammography screening. J Fam Pract 2000;49:1104–12.[ISI][Medline]
  7. Ulcickas Yood M, McCarthy BD, Lee NC, et al. Patterns and characteristics of repeat mammography among women 50 years and older. Cancer Epidemiol Biomarkers Prev 1999;8:595–9.[Abstract/Free Full Text]
  8. Strzelczyk JJ, Dignan MB. Disparities in adherence to recommended followup on screening mammography: interaction of sociodemographic factors. Ethn Dis 2002;12:77–86.[Medline]
  9. Rahman SM, Dignan MB, Shelton BJ. Factors influencing adherence to guidelines for screening mammography among women aged 40 years and older. Ethn Dis 2003;13:477–84.[ISI][Medline]
  10. Fox P, Arnsberger P, Owens D, et al. Patient and clinical site factors associated with rescreening behavior among older multiethnic, low-income women. Gerontologist 2004;44:76–84.[Abstract/Free Full Text]
  11. Sabogal F, Merrill SS, Packel L. Mammography rescreening among older California women. Health Care Financ Rev 2001;22:63–75.[Medline]
  12. Rakowski W, Breen N, Meissner H, et al. Prevalence and correlates of repeat mammography among women aged 55–79 in the Year 2000 National Health Interview Survey. Prev Med 2004;39:1–10.[ISI][Medline]
  13. Lee JR, Vogel VG. Who uses screening mammography regularly? Cancer Epidemiol Biomarkers Prev 1995;4:901–6.[Abstract/Free Full Text]
  14. Bobo JK, Shapiro JA, Schulman J, et al. On-schedule mammography rescreening in the National Breast and Cervical Cancer Early Detection Program. Cancer Epidemiol Biomarkers Prev 2004;13:620–30.[Abstract/Free Full Text]
  15. Lerman C, Rimer B, Trock B, et al. Factors associated with repeat adherence to breast cancer screening. Prev Med 1990;19:279–90.[CrossRef][ISI][Medline]
  16. Rimer BK, Trock B, Engstrom PF, et al. Why do some women get regular mammograms? Am J Prev Med 1991;7:69–74.[ISI][Medline]
  17. Quinley J, Mahotiere T, Messina CR, et al. Mammography-facility-based patient reminders and repeat mammograms for Medicare in New York State. Prev Med 2004;38:20–7.[CrossRef][ISI][Medline]
  18. Elwood M, McNoe B, Smith T, et al. Once is enough—why some women do not continue to participate in a breast cancer screening programme. N Z Med J 1998;111:180–3.[ISI][Medline]
  19. Hitchcock JL, Steckevicz MJ, Thompson WD. Screening mammography: factors associated with adherence to recommended age/frequency guidelines. Womens Health 1995;1:221–35.[Medline]
  20. Bastani R, Marcus AC, Hollatz-Brown A. Screening mammography rates and barriers to use: a Los Angeles County survey. Prev Med 1991;20:350–63.[CrossRef][ISI][Medline]
  21. Ostbye T, Greenberg GN, Taylor DH Jr, et al. Screening mammography and Pap tests among older American women 1996–2000: results from the Health and Retirement Study (HRS) and Asset and Health Dynamics Among the Oldest Old (AHEAD). Ann Fam Med 2003;1:209–17.[Abstract/Free Full Text]
  22. Pearlman DN, Rakowski W, Ehrich B, et al. Breast cancer screening practices among black, Hispanic, and white women: reassessing differences. Am J Prev Med 1996;12:327–37.[ISI][Medline]
  23. Marshall G. A comparative study of re-attenders and non-re-attenders for second triennial National Breast Screening Programme appointments. J Public Health Med 1994;16:79–86.[Abstract]
  24. Song L, Fletcher R. Breast cancer rescreening in low-income women. Am J Prev Med 1998;15:128–33.[CrossRef][ISI][Medline]
  25. Mayne L, Earp J. Initial and repeat mammography screening: different behaviors/different predictors. J Rural Health 2003;19:63–71.[ISI][Medline]
  26. Lechner L, de Vries H, Offermans N. Participation in a breast cancer screening program: influence of past behavior and determinants on future screening participation. Prev Med 1997;26:473–82.[CrossRef][ISI][Medline]
  27. Calvocoressi L, Kasl SV, Lee CH, et al. A prospective study of perceived susceptibility to breast cancer and nonadherence to mammography screening guidelines in African American and White women ages 40 to 79 years. Cancer Epidemiol Biomarkers Prev 2004;13:2096–105.[Abstract/Free Full Text]
  28. Cole SR, Bryant CA, McDermott RJ, et al. Beliefs and mammography screening. Am J Prev Med 1997;13:439–43.[ISI][Medline]
  29. Facione NC. Perceived risk of breast cancer: influence of heuristic thinking. Cancer Pract 2002;10:256–62.[CrossRef][ISI][Medline]
  30. Lindberg NM, Wellisch D. Anxiety and compliance among women at high risk for breast cancer. Ann Behav Med 2001;23:298–303.[CrossRef][ISI][Medline]
  31. Orton M, Fitzpatrick R, Fuller A, et al. Factors affecting women's response to an invitation to attend for a second breast cancer screening examination. Br J Gen Pract 1991;41:320–2.[ISI][Medline]
  32. Carney PA, Harwood BG, Weiss JE, et al. Factors associated with interval adherence to mammography screening in a population-based sample of New Hampshire women. Cancer 2002;95:219–27.[CrossRef][ISI][Medline]
  33. Jones BA, Patterson EA, Calvocoressi L. Mammography screening in African American women: evaluating the research. Cancer 2003;97(suppl):258–72.[CrossRef][Medline]
  34. Clark MA, Rakowski W, Bonacore LB. Repeat mammography: prevalence estimates and considerations for assessment. Ann Behav Med 2003;26:201–11.[CrossRef][ISI][Medline]
  35. Lemon SC, Roy J, Clark MA, et al. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med 2003;26:172–81.[CrossRef][ISI][Medline]
  36. Breiman L, Friedman J, Olshen R, et al. Classification and regression trees. Pacific Grove, CA: Wadsworth, 1984.
  37. Zhang H, Singer B. Recursive partitioning in the health sciences. New York, NY: Springer-Verlag, 1999.
  38. Steinberg D, Colla P. CART: tree-structures non-parametric data analysis. San Diego, CA: Salford Systems, 1995.
  39. Allore H, Tinetti ME, Araujo KL, et al. A case study found that a regression tree outperformed multiple linear regression in predicting the relationship between impairments and Social and Productive Activities scores. J Clin Epidemiol 2005;58:154–61.[CrossRef][ISI][Medline]
  40. Holford T. Multivariate methods in epidemiology. New York, NY: Oxford University Press, 2002.
  41. Leitch AM. Controversies in breast cancer screening. Cancer 1995;76(suppl):2064–9.[ISI][Medline]
  42. Rakowski W, Breslau ES. Perspectives on behavioral and social science research on cancer screening. Cancer 2004;101(suppl):1118–30.[CrossRef][ISI][Medline]
  43. Pasick RJ, Hiatt RA, Paskett ED. Lessons learned from community-based cancer screening intervention research. Cancer 2004;101(suppl):1146–64.[CrossRef][ISI][Medline]
  44. Ryan GL, Skinner CS, Farrell D, et al. Examining the boundaries of tailoring: the utility of tailoring versus targeting mammography interventions for two distinct populations. Health Educ Res 2001;16:555–66.[Abstract/Free Full Text]
  45. Fox SA, Stein JA. The effect of physician-patient communication on mammography utilization by different ethnic groups. Med Care 1991;29:1065–82.[ISI][Medline]
  46. Miller S. Applying cognitive-social theory to health protective behavior: breast self-examination in cancer screening. Psychol Bull 1996;119:70–94.[CrossRef][ISI][Medline]
  47. Rosenstock IM. Why people use health services. Milbank Mem Fund Q 1966;44(suppl):94–127.[ISI][Medline]
  48. Duke SS, Gordon-Sosby K, Reynolds KD, et al. A study of breast cancer detection practices and beliefs in black women attending public health clinics. Health Educ Res 1994;9:331–42.[ISI][Medline]
  49. Glanz K, Resch N, Lerman C, et al. Black-white differences in factors influencing mammography use among employed female health maintenance organization members. Ethn Health 1996;1:207–20.[Medline]
  50. Banerjee M, Biswas D, Sakr W, et al. Recursive partitioning for prognostic grouping of patients with clinically localized prostate carcinoma. Cancer 2000;89:404–11.[CrossRef][ISI][Medline]




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