This approach can be adapted to different constructs and different spatial scales, depending on the research problem and underlying theory. Our analyses illustrate one approach to quantifying the measurement properties of area-based measures. This seriously hampers their ability to examine contextual effects. Instead, we wanted to further develop and evaluate our ability to measure area-level constructs.Įpidemiologists are very sophisticated at measuring individual-level characteristics but not as sophisticated at measuring features of ecologic settings. Our objective was not even to estimate associations between neighborhood characteristics and health outcomes.
Our objective in the current paper was (merely) to contribute to emerging work on the measurement of area-level constructs, not to fully develop a theory on neighborhood causal effects or to resolve the issue of relevant spatial scale. Hence, anything we can do to improve the rigor of observational work is crucial.
Given the many limitations and logistical challenges of randomized trials (particularly for the study of neighborhood effects), reliance on observational and quasi-experimental data is likely to continue. In this, neighborhood effects research is no different than the rest of epidemiology. However, claims of residual confounding also need to be subjected to empirical inquiry: What specific confounders have been omitted, and how strong are their effects expected to be? Careful observational work can empirically examine the sensitivity of results to different degrees of residual confounding and degrees of extrapolation. Firm believers in nonexchangeability will accept no defense of observational studies because it is impossible to categorically rule out residual confounding, except in the case of the ideal counterfactual experiment. Messer implies that because of this, observational work in neighborhood health-effects research is meaningless. Nonexchangeability (or its simpler and less fashionable synonym, "residual confounding") is always a concern. Messer also alludes to the well-established challenges in estimating causal effects from observational data. For this, improving the validity of area-level measures and sensitivity analyses like the ones we present is crucial.ĭr. Ultimately, we must rely on empirical research to uncover such relations rather than make a priori assertions under the guise of theory. However, even if we were able to offer some crude hypotheses regarding spatial scales relevant to different processes, there are features of areas that could plausibly operate at multiple levels. Additional qualitative research on the ways in which individuals interact with spaces may help us develop better theoretical models that may then be empirically tested. Theorizing on the spatial scale at which different area processes operate is obviously important, but unfortunately there is very little information on which to base this theory. In her discussion of this model, Messer confuses inconsistent empirical support for aspects of the model with the absence of theory itself. However, we do base the measures we explore on a theoretical model of the processes through which residential context may affect cardiovascular disease risk (1, 9). Our paper is merely a methodological illustration, with no grandiose theoretical aims. Messer argues that our paper "promises more, from a theoretical perspective, than it delivers" (1, p.
A major challenge is developing theoretical models of the processes through which neighborhoods (or areas) may affect health. Messer reviews the many challenges involved in observational studies of neighborhood health effects, which we and other investigators have noted (3–8). Lynne Messer (1) recognizes the important contributions of our paper (2) to the discussion of methodological issues related to measurement of neighborhood or area-level properties.
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