S (ACE) AEP Vol. 20, No. 9 September 2010: 691–724 716 P93 DEVELOPMENT OF A DYNAMIC SIMULATION MODEL TO ESTIMATE POPULATION MORTALITY EFFECTS RESULTING FROM THE AVAILABILITY OF SMOKELESS TOBACCO PRODUCTS A Bachand, G Curtin, J Swauger, S Sulsky, Colorado State University, Fort Collins, CO; R J Reynolds Tobacco Co., Winston-Salem, NC; ENVIRON International Corp., Amherst, MA PURPOSE: Potential harm reduction resulting from smokeless tobacco (ST) use has received considerable attention. We are developing a model to estimate changes in future population mortality due to availability of ST that is expected to improve upon previous models. METHODS:We used theWinBUGS computer program to create a simulation that estimates mortality for a hypothetical population of persons who have never used tobacco and who, as they age, may transition into and out of tobacco exposure states, including current and former smoking or ST use. Markov Chain Monte Carlo techniques were used to estimate the variability of the results. All model inputs, including age-specific transition probabilities, are specified by the user. RESULTS: The model allows for 15 possible transitions into and out of tobacco exposure states, tracks individual exposure histories, and estimates ageand exposure-history-specific allcause or cause-specificmortality. At each age and at the end of follow-up, the model estimates the number of survivors under two different assumptions-that ST is either available or unavailable-and calculates the difference between the two results. Estimated deaths under the baseline exposure scenario (no ST) closely approximate recent US mortality based on CDC lifetable data. Some alternative exposure scenarios showed benefits from the introduction of ST. DISCUSSION: The simulation model was developed to use and document any inputs and any defined baseline exposure scenario.We expect models such as this one to be informative for development of harm reduction policies. P71 WITHIN-FAMILY ESTIMATES FOR THE ASSOCIATION BETWEEN SIBLING BIRTH ORDER AND RISK FOR AUTISM K Cheslack-Postava, KY Liu, P Bearman, Robert Wood Johnson Foundation Health & Society Scholars, Columbia University, New York, NY PURPOSE:Birth cohort studies have shown an inverse association between maternal parity and risk for autism, but whether this is due to biological mechanisms, confounding by family-level factors, or “stoppage” is not known. Investigating within-family patterns of association will enable us to differentiate between these alternative explanations. METHODS: Sibships consisting of full sibling singleton births occurring between 1992 and 2002 were identified using California birth records, and autism diagnoses were determined using Department of Developmental Services client files. Conditional logistic regression and multi-level models were used to estimate within-family association between birth order and risk for autism. RESULTS: Second and later born children were at increased risk of autism relative to their first-born siblings. The association between birth order and autism diagnosis was time dependent, such that the increased risk occurred when interval between pregnancies was short (OR and 95% CI for second versus first born siblings Z 2.15 (1.87, 2.47) for a 6 month versus 0.99 (0.87, 1.13) for a 24 month pregnancy interval). CONCLUSION: Within-family effect estimates suggest that risk of autism is increased in non-firstborn children relative to firstborn siblings. The increased risk occurs in closely spaced pregnancies, and may indicate a role for nutritional factors. This is different from the pattern in birth cohort analyses, illustrating the utility of applying different modeling approaches. P72 COMPARING METHODS FOR IMPUTING MISSING HEIGHT AND IMPACT ON BODY MASS INDEX MG DeForest, EC Wong, LP Palaniappan, Palo Alto Medical Foundation Research Institute, Palo Alto, CA PURPOSE: To compare common statistical methods for imputing missing values, as applied to height for the calculation of body mass index (BMI) in clinical settings. METHODS: A sample of 9,824 patients with complete height information (height recorded for every weight recorded) was derived from a clinic in Northern California. Patterns of missing heights were simulated for each patient in this sample based on data from other clinic patients who were matched on sex, age, race/ethnicity, and number of clinic visits. Five common imputation methods for missing patient height were compared: patient-specific mean height, median height, mode height, last observed height carried forward (LOCF) and last observed height carried forward then backward (LOCFB). The proportion of imputable height values, and the patient-specific mean squared error (MSE; mean, standard error) for BMI was compared across the five methods. RESULTS: The proportion of imputable values ranged from 16% for mode height, to 100% for mean height, median height, and LOCFB. The MSE of BMI for each method was mean height (meanZ0.48, seZ0.05), median height (0.47, 0.05), mode height (0.29, 0.08), LOCF (0.55, 0.06), LOCFB (0.50, 0.05).
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