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Identifying Causal Ecologic Effects on Health: A Methodological Assessment

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Macrosocial Determinants of Population Health

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References

  • Acemoglu, D., & Angrist, J. D. (1999). How large are the social returns to education? Evidence from compulsory schooling laws. NBER Working Paper Series Working Paper 7444.

    Google Scholar 

  • Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444–55.

    Article  Google Scholar 

  • Benson, K., & Hartz, A. J. (2000). A comparison of observational studies and randomized controlled trials. New England Journal of Medicine, 342(25), 1878–1886.

    Article  PubMed  CAS  Google Scholar 

  • Best, N. G., Spiegelhalter, D. J., Thomas, A., & Brayne, C. E. G. (1996). Bayesian analysis of realistically complex models. Journal of Royal Statistical Society A, 159, 232–342.

    Article  Google Scholar 

  • Blakely, T. A., & Subramanian, S. V. (2006). Multilevel studies. Methods for social epidemiology. In J. M. Oakes & J. S. Kaufman (Eds.), Methods in social epidemiology. New York: Jossey-Bass/Wiley.

    Google Scholar 

  • Blakely, T. A., & Woodward, A. J. (2000). Ecological effects in multi-level studies. Journal of Epidemiology and Community Health, 54, 367–374.

    Article  PubMed  CAS  Google Scholar 

  • Cummins, S., Petticrew, M., Higgins, C., Findlay, A., Sparks, L. (2005). Large scale food retailing as an intervention for diet and health: Quasi-experimental evaluation of a natural experiment. Journal of Epidemiology and Community Health, 59(12), 1035–1040.

    Article  PubMed  Google Scholar 

  • Currie, J., Gruber, J. (1996). Saving babies: The efficacy and cost of recent changes in the Medicaid eligibility of pregnant women. Journal of Political Economy CIV, 104(6), 1256–1296.

    Google Scholar 

  • Cutler, D. M., Glaeser, E. L. (1997). Are ghettos good or bad? Quarterly Journal of Economics 112(3), 827–872.

    Article  Google Scholar 

  • Ettner, S. L. (1997). Measuring the human cost of a weak economy: Does unemployment lead to alcohol abuse? Social Science & Medicine, 44(2), 251–60.

    Article  CAS  Google Scholar 

  • Fielding, A. (2004). The role of the Hausman Test and whether higher level effects should be treated as random or fixed. Multilevel Modeling Newsletter, 16(2), 3–9.

    Google Scholar 

  • Glymour, M. (2006). Using causal diagrams to understand common problems in social epidemiology. In J. M. Oakes & J. S. Kaufman (Eds.), Methods in social epidemiology. New York: Jossey-Bass/Wiley.

    Google Scholar 

  • Goldstein, H. (2003). Multilevel statistical models. London: Arnold.

    Google Scholar 

  • Greenland, S. (2000). An introduction to instrumental variables for epidemiologists. International Journal of Epidemiology, 29, 722–9.

    Article  PubMed  CAS  Google Scholar 

  • Greenland, S., & Pearl, J. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37–48.

    Article  PubMed  CAS  Google Scholar 

  • Harding, D. J. (2003). Counterfactual models of neighborhood effects: The effect of neighborhood poverty on dropping out and teenage pregnancy. American Journal of Sociology, 109(3), 676–719.

    Article  Google Scholar 

  • Hernán, M. A., & Hernandez-Diaz, S. (2002). Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. American Journal of Epidemiology, 155(2), 176–184.

    Article  PubMed  Google Scholar 

  • Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007, in press). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis.

    Google Scholar 

  • Holland, P. W. (1986). Statistics and causal inference (with discussion and rejoinder). Journal of the American Statistical Association, 81, 945–970.

    Article  Google Scholar 

  • Hoxby, C. M. (2000). Does competition among public schools benefit students and taxpayers? American Economic Review, 90(5), 1209–1238.

    Article  Google Scholar 

  • Jones, K., & Bullen, N. (1994). Contextual models of urban house prices: A comparison of fixed- and random-coefficient models developed by expansion. Economic Geography, 70, 252–272.

    Article  Google Scholar 

  • Katz, L. F., Kling, J., & Liebman, J. (2001). Moving to opportunity in Boston: Early impacts of a housing mobility program. Quarterly Journal of Economics 116(2), 607–654.

    Article  Google Scholar 

  • Kawachi, I., & Berkman, L. F. (Eds.), (2003). Neighborhoods and health. New York: Oxford University Press.

    Google Scholar 

  • Kawachi, I., & Subramanian, S. V. (2006). Measuring and modeling the social and geographic context of trauma: A multilevel modeling approach. Journal of Traumatic Stress, 19(2), 195–203.

    Article  PubMed  Google Scholar 

  • Kawachi, I., & Subramanian, S. V. (2007). Neighborhood influences on health. Journal of Epidemiology and Community Health,61(1), 3–4.

    Article  PubMed  CAS  Google Scholar 

  • King, G. (1997). A solution to the ecological inference problem: Reconstructing individual behavior from aggregate data. Princeton: Princeton University Press.

    Google Scholar 

  • Kling, J. (2000). Moving to opportunity research. Washington, DC (April 30, 2006); http://www.nber.org/∼ kling/mto/

    Google Scholar 

  • Kling, J. R., Liebman, J. B., & Katz, L. F. (2005). Experimental analysis of neighborhood effects. Cambridge, MA: National Bureau of Economic Research.

    Google Scholar 

  • Krieger, N., Chen, J. T., Waterman, P. D., Rehkopf, D. H., & Subramanian, S. V. (2005). Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: The Public Health Disparities Geocoding Project. American Journal of Public Health, 95(2), 312–323.

    Article  PubMed  Google Scholar 

  • Langford, I. H., Bentham, G., & McDonald, A. L. (1998). Multilevel modeling of geographically aggregated health data: A case study on malignant melanoma mortality and UV exposure in the European Community. Statistics in Medicine, 17(1), 41–57.

    Article  PubMed  CAS  Google Scholar 

  • Leyland, A. H. (2005). Assessing the impact of mobility on health: Implications for life course epidemiology. Journal of Epidemiology and Community Health, 59, 90–91.

    Article  PubMed  Google Scholar 

  • Little, R. J., & Rubin, D. B. (2003). Statistical analysis with missing data. New York: John Wiley & Sons.

    Google Scholar 

  • Macintyre, S. (1997). What are the spatial effects and how can we measure them? In A. Dale (Ed.), Exploiting national surveys and census data: The role of locality and spatial effects. Manchester: Faculty of Economic and Social Studies, University of Manchester.

    Google Scholar 

  • Manski, C. F. (1993). Identification problems in social sciences. Sociological Methodology, 23, 1–56.

    Article  Google Scholar 

  • Moon, G., Subramanian, S. V., Jones, K., Duncan, C., & Twigg, L. (2005). Area-based studies and the evaluation of multilevel influences on health outcomes. In A. Bowling & S. Ebrahim (Eds.), Handbook of health research methods: Investigation, measurement and analysis. Berkshire, England: Open University Press.

    Google Scholar 

  • Mujahid, M., Diez Roux, A., Morenoff, J., & Raghunathan. T. (2007). Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Amercian Journal of Epidemiology, 165(8), 858–867

    Article  Google Scholar 

  • Oakes, M. (2004). The (mis)estimation of neighborhood effects: Causal inference for a practicable social epidemiology. Social Science and Medicine, 58, 1929–1952.

    Article  PubMed  Google Scholar 

  • Pearl, J. (2000). Causality. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Raudenbush, S. W. (2003). The quantitative assessment of neighborhood social environment. In I. Kawachi & L. F. Berkman (Eds.), Neighborhoods and health.New York: Oxford University Press.

    Google Scholar 

  • Raudenbush, S. W., & Willms, J. D. (1995). The estimation of school effects. Journal of Educational and Behavioral Statistics, 20(4), 307–335.

    Article  Google Scholar 

  • Rosenbaum, J. (1995). Changing the geography of opportunity by expanding residential choice: Lessons from the Gautreaux program. Housing Policy Debate, 6, 231–269.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  • Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.

    Google Scholar 

  • Rubin, D. B. (1997a). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757–763.

    CAS  Google Scholar 

  • Rubin, D. B. (1997b). Practical implications of the modes of statistical inference for causal effects and the critical role of the assignment mechanism. Biometrics, 47, 1213–1234.

    Article  Google Scholar 

  • Rubin, D. B. (2004). Teaching statistical inference for causal effects in experiments and observational studies. Journal of Educational and Behavioral Statistics, 29(3), 343–367.

    Article  Google Scholar 

  • Rubin, D. B., Stuart, E. A., & Zanutto, E. L. (2004). A potential outcomes view of value-added assessment in education. Journal of Educational and Behavioral Statistics, 29(1), 103–116.

    Article  Google Scholar 

  • Sampson, R. J., & Morenoff, J. D. (2002). Assessing neighborhood effects: Social processes and new directions in research. Annual Review of Sociology, 28, 443–478.

    Article  Google Scholar 

  • Subramanian, S. V. (2004). Multilevel methods, theory and analysis. In N. Anderson (Ed.), Encyclopedia on health and behavior. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Subramanian, S. V., Chen, J. T., Rehkopf, D. H., Waterman, P. D., & Krieger, N. (2006a). Comparing individual and area-based socioeconomic measures for the surveillance of health disparities: A multilevel analysis of Massachusetts (US) births, 1988–92. American Journal of Epidemiology, 164(9), 823–834.

    Article  CAS  Google Scholar 

  • Subramanian, S. V., Chen, J. T., Rehkopf, D. H., Waterman, P. D., & Krieger, N. (2006b). Subramanian et. al. Respond to Think conceptually, act cautiously. American Journal of Epidemiology, 164(9), 841–844.

    Article  Google Scholar 

  • Subramanian, S. V., Jones, K., & Duncan, C. (2003). Multilevel methods for public health research. In I. Kawachi & L. F. Berkman (Eds.), Neighborhoods and health. New York: Oxford University Press.

    Google Scholar 

  • Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.

    Article  Google Scholar 

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Subramanian, S.V., Glymour, M.M., Kawachi, I. (2007). Identifying Causal Ecologic Effects on Health: A Methodological Assessment. In: Macrosocial Determinants of Population Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-70812-6_15

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  • DOI: https://doi.org/10.1007/978-0-387-70812-6_15

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