Monday, Oct 12 at noon, 6050 ISR
Joe Grengs: Policy & planning for transportation equity
Do, D., L. Wang, and Michael R. Elliott. 2013. "Investigating the relationship between neighborhood poverty and mortality risk: A marginal structural modeling approach." Social Science & Medicine, 91: 58-66.
Extant observational studies generally support the existence of a link between neighborhood context and health. However, estimating the causal impact of neighborhood effects from observational data has proven to be a challenge. Omission of relevant factors may lead to overestimating the effects of neighborhoods on health while inclusion of time-varying confounders that may also be mediators (e.g., income, labor force status) may lead to underestimation. Using longitudinal data from the 1990 to 2007 years of the Panel Study of Income Dynamics, this study investigates the link between neighborhood poverty and overall mortality risk. A marginal structural modeling strategy is employed to appropriately adjust for simultaneous mediating and confounding factors. To address the issue of possible upward bias from the omission of key variables, sensitivity analysis to assess the robustness of results against unobserved confounding is conducted. We examine two continuous measures of neighborhood poverty single-point and a running average. Both were specified as piece-wise linear splines with a knot at 20 percent. We found no evidence from the traditional naive strategy that neighborhood context influences mortality risk. In contrast, for both the single-point and running average neighborhood poverty specifications, the marginal structural model estimates indicated a statistically significant increase in mortality risk with increasing neighborhood poverty above the 20 percent threshold. For example, below 20 percent neighborhood poverty, no association was found. However, after the 20 percent poverty threshold is reached, each 10 percentage point increase in running average neighborhood poverty was found to increase the odds for mortality by 89 percent [95% CI = 1.22, 2.91]. Sensitivity analysis indicated that estimates were moderately robust to omitted variable bias. Published by Elsevier Ltd.