Abstract Details
Activity Number:
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355
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 11:35 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #313988
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Title:
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Monte Carlo Simulation to Examine the Uncertainty in Autism Spectrum Disorder Prevalence Estimates Derived from Postcensal Population Estimates
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Author(s):
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Lin Tian*+ and Owen Devine
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Companies:
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CDC and CDC
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Keywords:
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Postcensal population estimates ;
uncertainty in prevalence estimates ;
Monte Carlo simulation
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Abstract:
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Postcensal population estimates (PPEs) are commonly used with case counts to estimate the prevalence of health outcomes. However, PPEs are subject to error and have an unknown level of uncertainty. We used Monte Carlo (MC) simulation to evaluate how a specified level of uncertainty could affect Autism Spectrum Disorder (ASD) prevalence estimates in 2008. We assumed that the uncertainty in the true population size followed a triangular distribution with a minimum value of the intercensal estimate, a maximum value of the PPE, and a mode of the liner growth population estimate in 2008. Based on 1000 simulated population sizes drawn from this distribution, we calculated 1000 ASD prevalence estimates and their 95% confidence intervals (CIs). The resulting distribution of possible values for the prevalence was summarized using the mean of the 1000 prevalence values. Uncertainty in the CIs was evaluated by defining an overall lower limit as the minimum of the lower CI values across simulations and the upper limit as the maximum of the CI upper limits. The results indicate that uncertainty in PPEs should be taken into account in estimating point and interval estimates of ASD prevalence.
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Authors who are presenting talks have a * after their name.
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