Elsevier

Preventive Medicine

Volume 45, Issue 4, October 2007, Pages 252-261
Preventive Medicine

Review
Tailored interventions to promote mammography screening: A meta-analytic review

https://doi.org/10.1016/j.ypmed.2007.06.009Get rights and content

Abstract

Objective

To evaluate the effectiveness of tailored interventions, designed to reach one specific person based on her unique characteristics, for promoting mammography use.

Method

This systematic review used meta-analytic techniques to aggregate the effect size of 28 studies published from 1997 through 2005. Potential study-level moderators of outcomes (sample, intervention, and methodological characteristics) were also examined.

Results

A small but significant aggregate odds ratio effect size of 1.42 indicated that women exposed to tailored interventions were significantly more likely to get a mammogram (p < 0.001). The type of population recruited and participants' pre-intervention level of mammography adherence did not significantly influence this effect. Tailored interventions that used the Health Belief Model and included a physician recommendation produced the strongest effects. Interventions delivered in person, by telephone, or in print were similarly effective. Finally, defining adherence as a single recent mammogram as opposed to regular or repeated mammograms yielded higher effect sizes.

Conclusion

Tailored interventions, particularly those that employ the Health Belief Model and use a physician recommendation, are effective in promoting mammography screening. Future investigations should strive to use more standardized definitions of tailoring and assessments of mammography outcomes.

Introduction

Although one in eight women will develop breast cancer during their lives, only one in thirty-three will die from this disease (American Cancer Society, 2006). Early detection of breast cancer through mammography screening is partially responsible for decreasing breast cancer mortality rates (Humphrey et al., 2002). Mammograms are recommended either every year (American Cancer Society, 2006) or every 1 to 2 years after the age of 40 (National Cancer Institute, 2006) and more frequently and beginning at an earlier age for women with a family history of breast cancer (American Cancer Society, 2006). Mammography screening has increased from approximately 30% in 1987 to 70% in 2003 for both White and African American women (Smigal et al., 2006). A variety of interventions have been designed to promote breast cancer screening (Ryan et al., 2001). Recently, tailored interventions, designed to reach an individual based on her unique characteristics, have shown promise (Rimer et al., 1999).

Tailored interventions have assessment-based individually focused messages (Kreuter et al., 1999). The assessment involves a closed-ended measure of individual differences. This enables the message, tailored to an individual's answers, to be pre-established. This scripted message can then be delivered by a person (not necessarily a health professional), a letter, or a computer. Although the communication may involve in-person contact, it is not interactive. Thus, tailored interventions are not limited by the number and cost of trained professionals (Kreuter et al., 1999), but some information important to a participant could be lost by the closed-ended format for assessment and feedback.

Tailored interventions are distinct from personalized and targeted interventions. Personalized interventions can be as simple as directing a generic letter to a specific person by using her name (Kreuter et al., 1999). Targeted interventions are directed at a particular population as opposed to a particular individual, and thus involve less personally relevant content. Tailoring's effectiveness is explained by the elaboration likelihood model, which proposes that messages are more actively processed if they are considered personally applicable (Kreuter and Wray, 2003, Petty and Cacioppo, 1986). Such messages “are more likely to be read and remembered, rated as attention catching, saved and discussed with others” (Kreuter and Wray, 2003, p. S229).

Interventions are tailored to a variety of characteristics such as age, ethnicity, risk, and barriers to care, or according to theoretical models. Three theoretical models commonly used are the Health Belief Model (HBM); the Transtheoretical Model (TTM), sometimes referred to as the stages of change model; and the concepts related to motivational interviewing. The HBM proposes that perceptions of risk, benefits, severity, barriers, cues to action and self-efficacy are related to behavior such as getting a mammogram (Becker, 1974, Glanz et al., 1997). The TTM proposes that a series of stages is involved in changing behavior (precontemplation, contemplation, preparation, action, maintenance, and relapse) and that effective messages take these stages into account (Prochaska et al., 1992). Motivational interviewing is patient-centered and bases the information transmitted on what they are motivated to receive (Miller and Rollnick, 1991).

Prior reviews of tailored interventions promoting mammography screening suggest that they are effective (Legler et al., 2002, Skinner et al., 1999, Stoddard et al., 2002, Wagner, 1998). However, they have been limited by their narrative approach (Skinner et al., 1999), grouping tailored and non-tailored interventions together (Legler et al., 2002), or considering interventions implemented in print or by telephone exclusively (Skinner et al., 1999, Stoddard et al., 2002, Wagner, 1998). These reviews also did not examine moderators of tailored interventions' effectiveness. The current systematic review of tailored interventions to promote mammography screening comprises more recent research (published since 1997 where the most recent review left off); includes interventions implemented in person, by telephone, and in print; and uses meta-analytic techniques. It also considers potential moderators related to sample and intervention characteristics and outcome assessment.

The income and ethnicity of the women studied may influence the effectiveness of interventions. Low-income and minority women have historically lower rates of mammography (Legler et al., 2002). Because of the array of reasons for this disparity, we hypothesize that directing an intervention to women in these groups will result in a lower effect size.

Some studies specifically recruit nonadherent women whereas others include adherent and nonadherent women. Women who have had at least one mammogram before are more readily influenced by mammography-promoting interventions (Champion et al., 2003). Including regularly adherent women could leave little room for improvement, but we expect that it is more likely that women who are nonadherent or have never had a mammogram before will be especially difficult to influence.

Some interventions are individualized by demographic variables (age, ethnicity, risk factors), whereas others are based on psychological variables such as barriers to care, or those included in the HBM, TTM, or motivational interviewing. Since the success of tailoring is theoretically based on how relevant information is to a recipient (Kreuter and Wray, 2003) we expect that the more individualized a message is, the greater will be its effect. Although tailoring by demographic variables may make messages relevant, we hypothesize that interventions tailored by psychological variables will be more likely to influence behavior.

Tailored interventions vary in the level of personal contact involved in their implementation. For example, delivering a message in person involves more personal contact than by telephone or in print. Participants prefer face-to-face contact (Cohen et al., 2005) and it can positively influence their level of compliance (Spittaels et al., 2006). We hypothesize that the more personal contact involved, the more effective the intervention will be.

Physician recommendations are influential in promoting mammography adherence (Legler et al., 2002). This ability to persuade patients based on their trust in authority is referred to as “expert power” (Elder et al., 1999). We hypothesize that, consistent with previous interventions to promote mammography screening, incorporating a physician's recommendation will improve the effectiveness of tailored interventions.

Some studies compare the outcome of an intervention to a no-treatment control, while others use an active control, such as non-tailored information or a reminder phone call (*Lipkus et al., 2000, *Valanis et al., 2004). We hypothesize that there will be a larger effect size when interventions are compared to a no-treatment control than to an active control.

Repeat and regular mammography screenings are conceptually different outcomes from recent mammography adherence. According to Stoddard et al. (2002), a woman's mammography adherence is regular “if she reported a mammogram within 24 months of the survey and a prior mammogram within 24 months of the most recent mammogram” and recent “if she had had a mammogram within 24 months of the survey but had not had a prior mammogram within 24 months of the most recent.” This classification is used by the Breast Cancer Screening Consortium (*Andersen et al., 2000, *Costanza et al., 2000, *Duan et al., 2000, *Lipkus et al., 2000, *Messina et al., 2002, *Stockdale et al., 2000), but not by others (Rakowski et al., 2003). Despite this variation, we hypothesize that repeat/regular mammography is a more stringent outcome than recent mammography, that will result in lower effect sizes.

Mammography adherence is typically measured by self-report, a review of medical records, or both. Self-report is easily accessible, and is generally thought to be accurate (*Kreuter et al., 2005, *Rimer et al., 2002, Saywell et al., 1999). Studies found that over 90% of women accurately reported their mammography screening in the past 12 months as validated by medical records (Barratt et al., 2000, King et al., 1990). However, this consistency is less evident in older age groups, low-income households, different ethnicities, or participants with co-morbid conditions (Bancej et al., 2004, Champion et al., 1998, Lawrence et al., 1999). Conversely, medical records may not be up-to-date, making this measure less accurate (Jibaja-Weiss et al., 2003). Due to inconclusive support for the superiority of either method, we hypothesize that there will be no systematic difference between the two methods.

Section snippets

Study selection

This meta-analysis included publicly available reports in English on tailored interventions to promote mammography screening. Potentially eligible studies were identified by searching PubMed, PsycINFO, and Dissertation Abstracts International using the keywords mammography, psychology, adherence, intervention, screen, and tailored. Because it was the most common source of identified studies, the journal, Preventive Medicine, was hand searched for overlooked studies.

Examining the articles,

Results

The final sample comprised 28 independent study populations (see Table 1). The mean age of participants was 60.05 years (SD = 5.51). The samples were mostly not from underserved populations and were both nonadherent or mixed samples of women. The most commonly applied targets of tailoring were barriers to care and the TTM. Telephone and print were more frequently used than was in-person delivery. Physician recommendations were only included in 5 of the interventions. Active versus no-treatment

Discussion

The results of this meta-analysis supported the notion that tailored interventions are an effective method of promoting mammography adherence. The very small aggregate effect size is similar to effect sizes found from meta-analyses of patient letter reminders for cervical cancer screening (OR = 1.64; Tseng et al., 2001) and of tailored self-help materials promoting smoking cessation (OR = 1.42; Lancaster and Stead, 2006). The small size may be due to an increase in mammography rates in the general

Acknowledgments

The authors offer great thanks to Dr. Marci Lobel (Stony Brook University) and Dr. Fred Friedberg (Stony Brook University) for their valuable feedback on an earlier version of the manuscript. Preparation of the manuscript was supported by a seed grant from Stony Brook University and meta-analytic training and software was partially supported by a grant from the National Cancer Institute (R01 CA100810). An earlier version of this work was previously presented at the Annual Meeting of the Society

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