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

Summary 1.Faecal pellet group (FPG) count data are widely used to estimate animal abundance, with two alternative methods normally employed. Faecal accumulation rate (FAR) techniques measure the daily accumulation rate of pellet groups, while faecal standing crop (FSC) techniques measure overall density. To estimate abundance, both methods require estimates of the animal defaecation rate. FSC techniques also require an estimate of pellet group decomposition rate. In general, FAR techniques are considered less prone to bias while FSC methods are considered more precise and cost-effective. On balance, the majority of authors and practitioners prefer FSC methods, although little empirical evidence supports this decision. 2. FPG count data were obtained from 26 study areas to compare the precision of FAR and FSC count techniques when applied to wild deer populations in the UK uplands. The time needed to collect count data was quantified in 10 study areas. 3. The coefficients of variation (CV) of FSC pellet group count data ranged from 9% to 23% and were approximately 0·7-0·9 times those of equivalent FAR data. The precision of both methods was related to the density of pellet groups. On average, FSC count data took 80 min per plot to obtain, with FAR taking 1·6-1·9 times longer. 4. For the precision of FSC and FAR abundance estimates to be comparable in the range of conditions studied, decomposition rate trials would require a CV of 5-20%. While a number of studies report this to be possible, estimates of the time needed to obtain this level of precision generally exceed the net available time that results from the deployment of FSC rather than FSC pellet group counts. 5. Synthesis and applications . Using the levels of finance available to most deer managers in the UK uplands, deer abundance estimates obtained using FSC techniques on individual study sites up to 20 000 ha appear generally less cost-effective than FAR when compared in terms of their overall precision. As FAR methods are also thought to have less potential for bias when applied in the appropriate environmental conditions, they should be preferred over FSC when estimating deer abundance in concealing habitats.

Background: The gene expression profiles of most human tissues have been studied by determining the transcriptome of whole tissue homogenates. Due to the solid composition of tissues it is difficult to study the transcriptomes of individual cell types that compose a tissue. To overcome the problem of heterogeneity we have developed a method to isolate individual cell types from whole tissue that are a source of RNA suitable for transcriptome profiling.

Background: The gene expression profiles of most human tissues have been studied by determining the transcriptome of whole tissue homogenates. Due to the solid composition of tissues it is difficult to study the transcriptomes of individual cell types that compose a tissue. To overcome the problem of heterogeneity we have developed a method to isolate individual cell types from whole tissue that are a source of RNA suitable for transcriptome profiling.