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RE: Reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States

Posted by plosmedicine on 31 Mar 2009 at 00:25 GMT

Author: Adamson Muula
Position: Dr
Institution: University of Malawi and University of North Carolina at Chapel Hill
Submitted Date: May 07, 2008
Published Date: May 8, 2008
This comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.

Ezzati et al (1) must be commended for their contribution to the literature on mortality disparities in the United States studied using data across several decades. These types of papers have potential to stimulate remedial action for factors that are amenable to change. Even for immutable variables such as sex and race, an attempt should be made to understand why a particular sex or race experiences significant adverse health outcomes compared to the other i.e. the mechanisms how disadvantage by sex or race is mediated. The ultimate goal of course should be to encourage even better health outcomes in the “better offs” while also promoting a reduction in the gap with the “worse offs”. While Ezzati et al (1) did not intend to describe the mechanisms behind the disparities, it is equally important that the reasons behind the mortality and morbidity outcomes are explored. For instance, why reduced life expectancy in the particular geographical areas and racial groups? Why is this race or sex smoking more than the other? How does it happen that the risk of death increases if one leaves in a county with an x proportion of this race-ethnicity group? More importantly perhaps, how do we ensure that premature mortality and disability is prevented in counties with an x proportion of this race-ethnicity group? Was there any “risk clustering” observed i.e. high proportion of race x being associated with low education associated with low income?
Ezzati et al’s paper also begs other questions. In the methods section, the author reported that their used country-level social demographic characteristics of proportion of the population by education and per-capita income, among other county-level attributes. However, the education level categories used were not specified in the paper. Research who categorize education level as = high school versus > high school may potentially reach different conclusions to those who may have = high school, some college, college graduate and professional or graduate degree. Or was education level used a continuous variable of years of education completed among adults in the county? The authors also referred to per-capita income and not household income. This certainly raises the question as to how the authors dealt with households with dependents not earning an income of their own.



Ezzati M, Friedman AB, Kulkarni SC, Murray CJL. Reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PLos Med 2008; 5(4): e66

Competing interests declared: I declare that I have no competing interests