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A. Confounding and Effect Modification Congenital anomalies are the leading cause of infant mortality among US whites and the second leading cause among US blacks. Only a few population-based studies had previously examined mortality from congenital anomalies. Researchers from the California Birth Defects Monitoring Program utilized population-based registry data to examine the effects of major anomalies on neonatal mortality. In addition, since low birth weight (LBW) is a risk factor for neonatal mortality and highly associated with congenital anomalies, they were particularly interested in the interrelations between LBW, anomalies, and neonatal mortality. The table below shows the distribution of the 174,533 live births included in the study by birth weight category (LBW vs. normal birth weight (NBW)), anomaly status (any vs. none), and neonatal mortality (deceased vs. lived). [Adapted from an assignment developed by Prof. Linda Cowan, Univ. of Oklahoma] No anomaly, Neonatal mortality Any anomaly, LBW (< 2500 gm) Any anomaly, NBW ( 2500 gm) No anomaly, NBW LBW Deceased 471 188 381 151 Lived 688 8448 9834 154372 a. Construct the appropriate 2x2 table to examine the relationship between anomaly status and neonatal mortality ignoring birth weight. What is the magnitude of the association (odds ratio)? Interpret the association (odds ratio) between congenital anomaly and neonatal mortality. (Please show your work). Odds Ratio (crude) Deceased (Outcome positive) Lived (Outcome negative) Any anomaly (Exposure positive) No anomaly (Exposure negative)

b. We want to determine whether birth weight is a confounder of the relationship between anomaly status and neonatal mortality or it is an effect modifier – modifying the effect of anomaly status on the odds of neonatal mortality – in this study population. Construct two appropriate 2x2 tables to investigate if birth weight is an effect modifier or confounder, or neither in this study (use rule of thumb: use > 10% difference for confounding and > 33% difference for effect modification). [ Note: M-H Adjusted OR = ] Neonatal mortality Any anomaly, LBW (< 2500 gm) Any anomaly, NBW ( 2500 gm) No anomaly, LBW No anomaly, NBW Deceased 471 188 381 151 Lived 688 8448 9834 154372 (Please show your work)

C. Based on your analysis, which odds ratio(s) will you present when reporting the results of this study and why did you choose that/those odds ratio(s)? What are your conclusions regarding the relationship between anomaly status and neonatal mortality in your study population after considering birth weight?