Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections.

Background: Spreadsheet software is increasingly used to implement systems science models informing health policy decisions, both in academia and in practice where technical capacity may be limited. However, spreadsheet models are prone to unintentional errors that may not always be identified using standard error-checking techniques. Our objective was to illustrate, through a methodologic case study analysis, the impact of unintentional errors on model projections by implementing parallel model versions.

Evaluating child welfare policies with decision-analytic simulation models.

The objective was to demonstrate decision-analytic modeling in support of Child Welfare policymakers considering implementing evidence-based interventions. Outcomes included permanency (e.g.

A simulation model investigating the impact of tumor volume doubling time and mammographic tumor detectability on screening outcomes in women aged 40-49 years.

Background: Compared with women aged 50-69 years, the lower sensitivity of mammographic screening in women aged 40-49 years is largely attributed to the lower mammographic tumor detectability and faster tumor growth in the younger women.

Methods: We used a Monte Carlo simulation model of breast cancer screening by age to estimate the median tumor size detectable on a mammogram and the mean tumor volume doubling time. The estimates were calculated by calibrating the predicted breast cancer incidence rates to the actual rates from the Surveillance, Epidemiology, and End Results (SEER) database and the predicted distributions of screen-detected tumor sizes to the actual distributions obtained from the Breast Cancer Surveillance Consortium (BCSC).