Design options for sample-based mortality surveillance

Background Reliable information on causes of death is a fundamental component of health development strategies, yet globally only about one-third of countries have access to such information. For countries currently without adequate mortality reporting systems there are useful models other than resource-intensive population-wide medical certification. Sample-based mortality surveillance is one such approach. This paper provides methods for addressing appropriate sample size considerations in relation to mortality surveillance, with particular reference to situations in which prior information on mortality is lacking. Methods The feasibility of model-based approaches for predicting the expected mortality structure and cause composition is demonstrated for populations in which only limited empirical data is available. An algorithm approach is then provided to derive the minimum person-years of observation needed to generate robust estimates for the rarest cause of interest in three hypothetical populations, each representing different levels of health development. Results Modelled life expectancies at birth and cause of death structures were within expected ranges based on published estimates for countries at comparable levels of health development. Total person-years of observation required in each population could be more than halved by limiting the set of age, sex, and cause groups regarded as 'of interest'. Discussion The methods proposed are consistent with the philosophy of establishing priorities across broad clusters of causes for which the public health response implications are similar. The examples provided illustrate the options available when considering the design of mortality surveillance for population health monitoring purposes.

The return to schooling: Estimates from a sample of young Australian twins

This study uses a sample of young Australian twins to examine whether the findings reported in [Ashenfelter, Orley and Krueger, Alan, (1994). 'Estimates of the Economic Return to Schooling from a New Sample of Twins', American Economic Review, Vol. 84, No. 5, pp.1157-73] and [Miller, P.W., Mulvey, C and Martin, N., (1994). 'What Do Twins Studies Tell Us About the Economic Returns to Education?: A Comparison of Australian and US Findings', Western Australian Labour Market Research Centre Discussion Paper 94/4] are robust to choice of sample and dependent variable. The economic return to schooling in Australia is between 5 and 7 percent when account is taken of genetic and family effects using either fixed-effects models or the selection effects model of Ashenfelter and Krueger. Given the similarity of the findings in this and in related studies, it would appear that the models applied by [Ashenfelter, Orley and Krueger, Alan, (1994). 'Estimates of the Economic Return to Schooling from a New Sample of Twins', American Economic Review, Vol. 84, No. 5, pp. 1157-73] are robust. Moreover, viewing the OLS and IV estimators as lower and upper bounds in the manner of [Black, Dan A., Berger, Mark C., and Scott, Frank C., (2000). 'Bounding Parameter Estimates with Nonclassical Measurement Error', Journal of the American Statistical Association, Vol. 95, No.451, pp.739-748], it is shown that the bounds on the return to schooling in Australia are much tighter than in [Ashenfelter, Orley and Krueger, Alan, (1994). 'Estimates of the Economic Return to Schooling from a New Sample of Twins', American Economic Review, Vol. 84, No. 5, pp. 1157-73], and the return is bounded at a much lower level than in the US. (c) 2005 Elsevier B.V. All rights reserved.

How many glomerular profiles must be measured to obtain reliable estimates of mean glomerular areas in human renal biopsies?

The objective of this study was to investigate the number of glomerular profiles that are required for accurate estimates of mean profile area in a renal biopsy series. Slides from 384 renal biopsies from one center were reviewed. They contained a median of seven glomerular profiles or of four profiles without sclerosis. Profile areas were measured using stereologic point counting. The true individual mean for each biopsy was calculated and the true population mean for groups of biopsies derived. Individual and population random sample means then were calculated from a random sampling of profiles in each biopsy and were compared with true means for the same biopsies. The effect on the true population means of the entire group of biopsies was also assessed, as the minimum number of glomerular profiles that were required for inclusion was changed. In a single biopsy, random sampling of >= 10 profiles without exclusions and of eight profiles or more without sclerosis reliably estimated the true mean areas. In a group of 30 biopsies, random sampling of five or more glomeruli per biopsy reliably estimated the true population mean. In the aggregate series, inclusion of all 384 biopsies produced the most robust true population mean; the reliability of the estimates decreased as the numbers of eligible biopsies diminished with increasing requisite minimum numbers of profiles per biopsy. We conclude that, while >= 10 profiles might be needed for reliable area estimates in a single biopsy, far fewer profiles per biopsy can suffice when groups of biopsies are studied. In analyses of groups of biopsies, all available biopsies should be used without consideration of the number of glomerular profiles in each. Stipulation of a specific minimum number of glomeruli in each biopsy for inclusion reduces the power of analyses because fewer biopsies are available for evaluation.