A Simulation Model to Estimate Local Prevalence Based on Screening Data

Area of Science:

• Health Services Research
• Clinical Epidemiology
• Decision Science

Background:

• Screening guidelines require context-specific adaptation, particularly for perinatal depression.
• The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) Evidence to Decision (EtD) framework guides guideline development by assessing test accuracy and downstream consequences.
• Methods are needed to evaluate the generalizability of assumptions underlying screening recommendations in diverse settings.

Purpose of the Study:

• To develop and apply a simulation model to estimate local perinatal depression prevalence.
• To assess the feasibility of using existing data to inform screening guideline adaptation.
• To explore the impact of local context on screening accuracy and prevalence estimates.

Main Methods:

• Systematic literature search for meta-analyses on perinatal depression prevalence, screening accuracy, and implementation.
• Development of a simulation model linking prevalence, sensitivity, specificity, and positive screening rates.
• Participatory simulation modeling and sensitivity analyses to estimate local prevalence and explore uncertainties.

Main Results:

• Meta-analyses provided data on prevalence and screening accuracy, but 14 screening studies showed insufficient data for key questions.
• Simulation models revealed significant heterogeneity in estimated local prevalence, with some implausible values.
• Sensitivity and specificity estimates were not stable, suggesting context-dependent variations in symptom disclosure.

Conclusions:

• Current screening data are insufficient for accurate local prevalence estimation.
• Sensitivity and specificity of screening tools are influenced by study-level contextual factors, not just inherent properties.
• Simulation modeling offers a valuable approach for evidence synthesis and informing context-specific screening guideline decisions.