1. Kevin Kovach, DrPH(c), MSc, CHES Johnson County Department of Health and Environment – Olathe, Kansas Does the County Poverty Rate Influence Birth Weight and Infant Mortality in Kansas? Social economic status (SES) is a known risk factor for almost all health conditions and is considered a fundamental cause of health. The fundamental cause theory suggests that SES embodies a range of resources, such as money, knowledge, and power, and that individuals living with low SES lack the resources to improve their health. According to the ecological framework, SES can act both at the individual level (e.g., income or education) and at the community level (e.g. the built environment, social systems, or culture) [1]. The infant mortality rate is a sensitive measure that not only measures the health status of infants, but is a leading indicator of broader public health issues [2]. Birth weight is a primary risk factor for infant mortality and has also been proposed as a public health indicator [3]. The purpose of this study was to examine if community level SES, operationalized as the county poverty rate, has an independent effect on infant mortality and birth weight, after taking into account individual maternal SES and behaviors. This was accomplished through statistical analysis of linked birth and death certificate data from 2006 to 2010. The county poverty rate was observed to have a small but consistent negative effect on birth weight and infant mortality in Kansas. After adjusting for maternal characteristics the effect of the county poverty rate was attenuated toward the null. Maternal characteristics that remained significantly associated with birth outcomes after adjustment included: 1) race, 2) education, 3) marital status, and 4) smoking status. Using the social epidemiological principal of “risk regulators,” it could be proposed that the county poverty rate acts as a proxy to some social phenomenon that drives more proximal risk factors (e.g., less education, out of wedlock pregnancy, smoking, alcohol use, etc.). In this case, addressing the social context in these neighborhoods may be more beneficial than addressing individual level risk factors. Future studies could be improved by using smaller geographical units of measurement, such as census tracts. Qualitative methods could be used to better define the social context. Community based participatory research may a good method for addressing this issue in an action oriented manner. Conclusion The county poverty rate was observed to have an indirect, but not a direct effect and infant mortality and birth weight. Risk factors with the most direct influence include: 1) race, 2) marital status, 3) cigarette smoking, and 4) alcohol use. Both education and smoking status appeared to interact with the county poverty rate. Data This was a cross sectional, multilevel analysis of birth and death certificate data (2006 – 2010) linked with data from the American Community Survey 2006 – 2010 five year estimates. Outcomes included birth weight and infant mortality. Risk factors included the county poverty rate and maternal characteristics (Table 1). Linear and logistic regression was used for birth weight and infant mortality, respectively. Three models were used for the analysis: 1) an “empty” random effects model was used to assess for variation due to clustering within counties, 2) a fixed effects model assessing for the crude effect of the county poverty rate on infant mortality and birth weight, and 3) a fixed effects model adjusting for maternal characteristics. Methods Introduction 1. Glass, T., & McAtee, M. (2005). Behavioral Science at the Crossroads in Public Health: Extending Horizons, Envisioning the Future. Social Science and Medicine, 1650-1671. 2. Lynch, J., & Kaplan, G. (2000). Socioeconomic Position. In L. Berkman, & I. Kawachi, Social Epidemiology (pp. 13-35). New York, NY: Oxford. 3. Peoples-Sheps, M. D., Guild, P. A., Farel, A. M., Cassady, C. E., Kennelly, J., Potrzebowski, P. W., et al. (1998). Model Indicators for Maternal and Child Health: An Overview of Process, Product, and Applications. Maternal and Child Health Journal, 241-256. 4. Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and Longitudinal Modeling using Stata. College Station, TX: Stata Press. 5. Reidpath, D., & Allotey, P. (2003). Infant mortality rate as an indicator of population health. Journal of Epidemiology and Community Health, 344-346. 6. U.S. Census Bureau. (2011). 2006-2010 American Community Survey Kansas. Sources Kansas Department of Health and Environment – Bureau of Epidemiology and Public Health Informatics Kansas “Region 15” Public Health Preparedness Acknowledgements Table 1: Effect of County Poverty Rate on Birth Weight and Infant Mortality Birth WeightInfant Mortality Independent VariablesCrude β (95% CI) Adjusted β (95% CI) Crude OR (95% CI) Adjusted OR (95% CI) County Poverty Rate-6 (-7; -6)-2 (-2; -1)1.02 (1.01; 1.03)1.00 (0.99; 1.01) Age10 (10; 11)5 (4; 6)0.96 (0.95; 0.97)0.99 (0.98; 1.01) Race White Black - -234 (-243; -224) - -181 (-192; -172) - 2.02 (1.68; 2.43) - 1.50 (1.24; 1.83) Education B.S. or greater A.S. or some college H.S. or GED Less than H.S. - -80 (-86; -74) -153 (-160; -147) -177 (-184; -169) - -16 (-23; -9) -47 (-55; -39) -63 (-73; -54) - 1.44 (1.19; 1.73) 2.19 (1.82; 2.63) 2.56 (2.13; 3.08) - 1.18 (0.85; 1.20) 0.99 (0.77; 1.28) 1.20 (0.99; 1.44) Health Insurance Private Medicaid Self - -145 (-151; -139) -80 (-88; -73) - -12 (-19; -5) 25 (14; 35) - 1.76 (1.53; 2.02) 1.57 (1.26; 1.95) - 1.01 (0.85; 1.20) 0.99 (0.77; 1.28) Married (no)-147 (-152; -142)-41 (-47; -34)1.88 (1.67; 2.13)1.26 (1.08; 1.46) Cigarette Smoking (yes)-177 (-183; -171)-132 (-139; -126)1.74 (1.53; 1.99)1.36 (1.18; 1.58) Alcohol Use (yes)-233 (-276; -171)-112 (-163; -60)2.57 (1.15; 5.77)1.55 (0.68; 3.50) Figure 1: Birth Weight Predicted by the County Poverty RateFigure 2: Probability of Infant Death Predicted by the County Poverty Rate