Interventions to promote repeat breast cancer screening with mammography: a systematic review and meta-analysis

Abstract

Background: Various interventions to promote repeat use of mammography have been evaluated, but the efficacy of such interventions is not well understood.

Methods: We searched electronic databases through August 15, 2009, and extracted data to calculate unadjusted effect estimates (odds ratios [ORs] and 95% confidence intervals [CIs]). Eligible studies were those that reported estimates of repeat screening for intervention and control groups. We tested homogeneity and computed summary odds ratios. To explore possible causes of heterogeneity, we performed stratified analyses, examined meta-regression models for 15 a priori explanatory variables, and conducted influence analyses. We used funnel plots and asymmetry tests to assess publication bias. Statistical tests were two-sided.

Results: The 25 eligible studies (27 effect estimates) were statistically significantly heterogeneous (Q = 69.5, I(2) = 63%, P < .001). Although there were homogeneous subgroups in some categories of the 15 explanatory variables, heterogeneity persisted after stratification. For all but one explanatory variable, subgroup summary odds ratios were similar with overlapping confidence intervals. The summary odds ratio for the eight heterogeneous reminder-only studies was the largest observed (OR = 1.79, 95% CI = 1.41 to 2.29) and was statistically significantly greater than the summary odds ratio (P(diff) = .008) for the homogeneous group of 17 studies that used the more intensive strategies of education/motivation or counseling (OR = 1.27, 95% CI = 1.17 to 1.37). However, reminder-only studies remained statistically significantly heterogeneous, whereas the studies classified as education/motivation or counseling were homogeneous. Similarly, in meta-regression modeling, the only statistically significant predictor of the intervention effect size was intervention strategy (reminder-only vs the other two combined as the referent). Publication bias was not apparent.

Conclusions: The observed heterogeneity precludes a summary effect estimate. We also cannot conclude that reminder-only intervention strategies are more effective than alternate strategies. Additional studies are needed to identify methods or strategies that could increase repeat mammography.

Figure 1

Literature search results and review process of publications by exclusionary criteria. The category “other” under excluded nonintervention studies includes measurement, risk assessment models, perceptions and knowledge, breast self-examinations, abnormal mammography results and postmammogram psychological reactions (eg, anxiety, worry), patient satisfaction, and genetic screening.

Figure 2

Forest plot comparing intervention groups to control groups on repeat mammography by author. Sizes of study-specific odds ratios (ORs) (in gray) are inversely proportional to the variance. The 95% confidence intervals (CIs) for the overall summary odds ratio were computed under random-effects model assumptions. Weights are from the random-effects analysis. Statistical tests were two-sided. Control % = odds of RM compared with odds of no RM in the control group; intervention % = odds of RM compared with odds of no RM in the intervention group; NA = not available; OR = ratio of the intervention odds to the control odds; RM/no RM = repeat mammography/no repeat mammography.

Figure 3

Begg funnel plot of all studies. The Begg test (39) is a measure of publication bias and tests whether the Begg rank correlation between effect size and its SE is zero. Pseudo confidence intervals are the points connected by the diagonal lines forming the “funnel” on the funnel plot; they are the expected 95% confidence intervals for a given SE (depicted as increasing along the x-axis).