Effectiveness and costs of a new framework for selecting absorbent urinary incontinence products compared with current practice: a cohort study.

OBJECTIVES: To improve continence care in the Netherlands, a new framework has been developed in which a shift has been made from incontinence severity as the sole criterion for selecting incontinence products to a focus on patient need for daily life activities. The impact of the framework on daily care has not been assessed. We aimed to compare treatment effectiveness and costs between participants who did and did not undergo re-evaluation according to the new framework. DESIGN: Cohort study SETTING: Twelve pharmacies in the Netherlands PARTICIPANTS: Existing users of absorbent incontinence materials for urinary incontinence. INTERVENTIONS: Participants were offered the option to have their incontinence products re-evaluated within the new framework at their pharmacy. PRIMARY AND SECONDARY OUTCOME MEASURES: Effectiveness and cost outcomes were assessed at 3 and 6 months through questionnaires. These questionnaires included the International Consultation on Incontinence Questionnaire (ICIQ) Lower Urinary Tract Symptoms Quality of Life Module, ICIQ Urinary Incontinence Short Form, ICIQ Absorbent Pads, questions about satisfaction and the iPCQ and iMCQ. RESULTS: 303 users consented and 279 completed the baseline questionnaire. Of these participants, 72 agreed to a re-evaluation of their incontinence materials. There was a small improvement at 3 months in the re-evaluation group compared with the other group on most outcomes. However, these improvements were not clinically relevant. Moreover, these differences did not change much from 3 to 6 months. Small differences were also observed in the changes in costs, but with very wide CIs on both sides of zero (€99.38; 95% CI -633.48 to 832.23). CONCLUSIONS: The current study showed no clinically relevant effect of a newly implemented framework for selecting incontinence materials in pharmacies when compared with an existing method. Given that the study also showed no differences in effectiveness and costs, introducing the new framework in pharmacies may not lead to better incontinence care.

Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid.

OBJECTIVE: To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI). DESIGN: A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial. SETTING: Dutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018. PARTICIPANTS: Adult women were eligible if they had ≥2 episodes of UI per week, access to mobile apps and wanted treatment. Of the 350 screened women, 262 were eligible and randomised to app-based treatment or care as usual; 195 (74%) attended follow-up. PREDICTORS: Literature review and expert opinion identified 13 candidate predictors, categorised into two groups: Prognostic factors (independent of treatment type), such as UI severity, postmenopausal state, vaginal births, general physical health status, pelvic floor muscle function and body mass index; and modifiers (dependent on treatment type), such as age, UI type and duration, impact on quality of life, previous physical therapy, recruitment method and educational level. MAIN OUTCOME MEASURE: Primary outcome was symptom severity after a 4-month follow-up period, measured by the International Consultation on Incontinence Questionnaire the Urinary Incontinence Short Form. Prognostic factors and modifiers were combined into a final prediction model. For each participant, we then predicted treatment outcomes and calculated a Personalised Advantage Index (PAI). RESULTS: Baseline UI severity (prognostic) and age, educational level and impact on quality of life (modifiers) independently affected treatment effect of eHealth. The mean PAI was 0.99±0.79 points, being of clinical relevance in 21% of individuals. Applying the PAI also significantly improved treatment outcomes at the group level. CONCLUSIONS: Personalising treatment choice can support treatment decision making between eHealth and care as usual through the practical application of prediction modelling. Concerning eHealth for UI, this could facilitate the choice between app-based treatment and care as usual. TRIAL REGISTRATION NUMBER: NL4948t.