User acceptability profiles of vaginal treatments among women with genitourinary syndrome of menopause - Mohammed Hassanein; Hasniza Zaman Huri; Abduelmula Abduelkarem (PLoS ONE, 2025)
Background This study aimed to identify patient segments of acceptability to vaginal dosage forms in women with the genitourinary syndrome of menopause and to identify the most important predictors and mediators of each segment using latent segment analysis. Methods A cross-sectional study included 351 peri- and postmenopausal women from two tertiary care hospitals in the United Arab Emirates. To select the best model, we ran a finite-mixture partial least squares segmentation (FIMIX-PLS). The number of resulting segments was used to run a partial least squares-predicted-oriented segmentation to assign cases to segments and maximize the segment-specific explained variance (R2) across all groups. Multi-group analysis was then performed to examine whether differences between segments were significant. Analyses were performed using the SmartPLS Software version 4.9, Results A two-segment model was identified (Entropy > 0.8, Corrected-Akaike’s Information Criterion = 1977.11; Bayesian Information Criterion = 1960.11), which indicated adequate and well-separated segments. The first and second segments had 179 and 171 of the cases, respectively, which were considered substantive representations of both segments. The model resulted in a weighted average R2 greater than that of the original sample, indicating better predictive relevance of the model. The multi-group analysis showed that the differences between the two segments were significant. Being on vaginal treatment was observed to be the variable that drove the partition of the segments. Perceived effectiveness for those who were not on treatment (segment 1) and those who were (segment 2) was predicted by affective attitude and intervention coherence, respectively, Conclusion The acceptability of vaginal treatments differs between experienced and anticipated users. Considering patient-related factors and previous treatment experiences can serve as a benchmark to improve patient acceptability of treatment.