Confidence intervals for ranks of age‐adjusted rates across states or counties. (13th January 2014)

• Record Type: Journal Article
• Authors: Zhang, Shunpu; Luo, Jun; Zhu, Li; Stinchcomb, David G.; Campbell, Dave; Carter, Ginger; Gilkeson, Scott; Feuer, Eric J.
• Is Part Of: Statistics in medicine. Volume 33:Number 11(2014)
• Page Start: 1853
• Page End: 1866
• Publication Date: 2014-01-13
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6071
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6071-abs-0001"> <title>Abstract</title> <p id="sim6071-para-0001">Health indices provide information to the general public on the health condition of the community. They can also be used to inform the government's policy making, to evaluate the effect of a current policy or healthcare program, or for program planning and priority setting. It is a common practice that the health indices across different geographic units are ranked and the ranks are reported as fixed values. We argue that the ranks should be viewed as random and hence should be accompanied by an indication of precision (i.e., the confidence intervals). A technical difficulty in doing so is how to account for the dependence among the ranks in the construction of confidence intervals. In this paper, we propose a novel Monte Carlo method for constructing the individual and simultaneous confidence intervals of ranks for age‐adjusted rates. The proposed method uses as input age‐specific counts (of cases of disease or deaths) and their associated populations. We have further extended it to the case in which only the age‐adjusted rates and confidence intervals are available. Finally, we demonstrate the proposed method to analyze US age‐adjusted cancer incidence rates and mortality rates for cancer and other diseases by states and counties within a state using a website that will be publicly available. The results show that for rare or relatively rare disease (especially at the county level), ranks are essentially meaningless because of their large variability, while for more common disease in larger geographic units, ranks can be effectively utilized. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>