This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 480
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #307530
Title: A Simulation-Based Improvement
Author(s): Thomas H. Taylor, Jr.*+ and Melissa Lewis and Elizabeth R. Zell
Companies: CDC and CDC and CDC
Address: 1926 North Decatur Road, Atlanta, GA, 30307, United States
Keywords: pregnancy ; simulation ; incidence ; prevalence ; Monte Carlo ; denominator
Abstract:

Many public-health-policy decisions have impact on pregnant women. Various quantitative estimates inform these decisions, e.g. the prevalence of novel H1N1 influenza among pregnant women. Common estimates use surveillance-based case counts divided by NCHS estimates of the total number of pregnant women for the catchment area. This approach systematically understates annual incidence. The fundamental flaw arises because pregnancy is a state, not a characteristic, and because the duration of that state is shorter than the explicit time reference in annual rates. We present here an alternative approach which accounts for the fact that women are never pregnant for an entire year. Every woman who is pregnant at any point during a year is also at-risk-and-not-pregnant during part of the year. Our approach employs a Monte Carlo simulation using relevant input from CDC, US Census, NCHS, and WHO.


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