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• Re: Social Epidemiology, Intra-Neighbourhood Correlation and Generalized Estimating Equations
  Juan Merlo
  Published on: 15 August 2003
• Social Epidemiology, Intra-Neighbourhood Correlation and Generalized Estimating Equations
  Kenneth R. Petronis
  Published on: 11 August 2003

• Published on: 15 August 2003

  Re: Social Epidemiology, Intra-Neighbourhood Correlation and Generalized Estimating Equations

  • Juan Merlo, MD PHD Ass Prof

  Dear Editor

  I have read with high interest the comments made by Petronis and Anthony on my editorial.[1] I have also read their forthcoming article,[2] and I believe they apply an analytical approach that seems to be, in my opinion, a step in the right direction for research on contextual influences and health that focus on investigation of clustering. I will be very pleased of writing a larger comment and send i...

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  Dear Editor

  I have read with high interest the comments made by Petronis and Anthony on my editorial.[1] I have also read their forthcoming article,[2] and I believe they apply an analytical approach that seems to be, in my opinion, a step in the right direction for research on contextual influences and health that focus on investigation of clustering. I will be very pleased of writing a larger comment and send it for consideration and possible publication in the Journal.

  Measuring clustering with the aim of obtaining substantive scientific information is so far an uncommon approach in social epidemiology and most multilevel analyses have in fact been plain “contextual analysis”[3] focused on measures of association. This seems be true not only for studies using GGE techniques but also for studies using multilevel hierarchical regression. However, the original standpoint of multilevel hierarchical regression analyses is the investigation of complex patterns of variation [4] rather than dealing with residual correlation.

  Regarding the use of “GEE2” and measurement of clustering, the comment of Petronis and Anthony is certainly right.

  From an epidemiological point of view the most interesting question is the conceptual rather than the mathematical approach used. I agree with Petronis and Anthony in their conceptual approach and I believe that they put context back in epidemiology using “GEE2” techniques.[5] The Parwise Odds Ratio and other techniques for measuring neighbourhood heterogeneity and clustering like the Median Odds Ratio and the Interval Odds Ratio [6,7] deserve more development and spreading.

  References

  (1) Merlo J. Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association. J Epidemiol Community Health 2003;57:550-2.

  (2) Petronis KR,.Anthony JC. A different kind of contextual effect: geographic clustering of cocaine incidence in the US. J Epidemiol Community Health 2003;in press.

  (3) Diez Roux AV. A glossary for multilevel analysis. J Epidemiol Community Health 2002;56:588-94.

  (4) Goldstein H. Multilevel Statistical Models. London: Hodder Arnold, 2003.

  (5) Carey V, Zeger SL, Diggle P. Modelling multivariate binary data with alternating logistic regressions. Biometrika 1993;80:517-26.

  (6) Larsen K, Petersen JH, Budtz-Jorgensen E, Endahl L. Interpreting parameters in the logistic regression model with random effects. Biometrics 2000;56:909-14.

  (7)Larsen K,.Merlo J. Appropriate assessment of neighborhood effects on individual health -integrating random and fixed effects in multilevel logistic regression. Working paper 2003.

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  Conflict of Interest:
  None declared.

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• Published on: 11 August 2003

  Social Epidemiology, Intra-Neighbourhood Correlation and Generalized Estimating Equations

  • Kenneth R. Petronis, Service Fellow
  • Other Contributors:
    • James C. Anthony

  Dear Editor,

  The recent editorial entitled "Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association" [1] offers an interesting critique of the generalized estimating equations (GEE) analysis of a paper published in the same issue of JECH (August 2003). In the editorial, the author notes that the paper's GEE analysis treats "the intr...

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  Dear Editor,

  The recent editorial entitled "Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association" [1] offers an interesting critique of the generalized estimating equations (GEE) analysis of a paper published in the same issue of JECH (August 2003). In the editorial, the author notes that the paper's GEE analysis treats "the intra-neighbourhood correlation as a 'nuisance' that needs to be adjusted in the analysis but not explicity investigated" (p. 550).

  The editorial then becomes a call for an alternative, more innovative approach in social epidemiology: "Estimation of the extent to which individuals within a given neighbourhood are correlated with one another in relation to health (that is, the concept of intra-neighbourhood correlation) has value in the context of ideas about the efficacy of focusing intervention on places instead of people" (p.551). The finale is a logical conclusion that studies of intra-neighbourhood correlation may "...present themselves as a new epidemiological approach that may prove very useful in social epidemiology" (p. 551).

  The author is appartently speaking of first-order GEEs but the JECH readership may not appreciate that second order GEEs (GEE2) treat the intra-neighbourhood and inter-neighbourhood correlations into deliberate objects of study and estimation [2]. Although we ourselves deserve absolutely no credit for biostatistical innovations, the "alternating logistic regressions" (ALR) approach [3] we employ in our forthcoming article in JECH [4] is a computationally efficient alternative to GEE2 in the case of a binary outcome. As such, it estimates the pairwise odds ratio (PWOR), which quantifies the degree to which health conditions, behaviors, or perceptions might cluster within neighbourhoods (or other nested structures of community life) to a degree other than one might expect if these health conditions, behaviors, or perceptions were distributed at random across neighbourhoods.

  Because we believe our work is responsive to the author's call for a new approach in social epidemiology that measures intra-neighbourhood correlation, we would welcome an editorial comment on the potential value (and possible shortcomings) of the GEE/ALR approach we used in our forthcoming article in JECH on clustering of cocaine incidence in the United States. We hope you will concur that our application of the ALR approach is a step in the right direction for research on contextual influences and health.

  References

  (1) Merlo J. Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association. JECH 2003;57:550-552.

  (2) Liang K-Y, Zeger SL. Regression analysis for correlated data. Annu. Rev. Pub. Health;14:43-68.

  (3) Carey V, Zeger SL, Diggle P. Modelling multivariate binary data with alternating logistic regressions. Biometrika;80:517-26.

  (4) Petronis KR, Anthony JC. A different kind of contextual effect: geographic clustering of cocaine incidence in the U.S. JECH, in press.

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  Conflict of Interest:
  None declared.

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