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A Simulation Model to Estimate Local Prevalence Based on Screening Data.

Katherine M Cooper1, Leah Ramella2, Esther Boama-Nyarko3

  • 1University of Massachusetts Chan Medical School, UMass Memorial Health, 222 Maple Ave- Chang Building, Shrewsbury, MA, 01545, USA.

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Summary
This summary is machine-generated.

Developing effective screening guidelines requires assessing test accuracy and consequences. This study used simulation modeling to evaluate perinatal depression screening, revealing data limitations and context-specific challenges in estimating prevalence.

Keywords:
Evidence-based practiceMeta-analysisPerinatal depressionScreeningSystems modeling

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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.