A comparative epidemiologic analysis of SARS in Hong Kong, Beijing and Taiwan.

BACKGROUND: The 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak infected 8,422 individuals leading to 916 deaths around the world. However, there have been few epidemiological studies of SARS comparing epidemiologic features across regions. The aim of this study is to identify similarities and differences in SARS epidemiology in three populations with similar host and viral genotype. METHODS: We present a comparative epidemiologic analysis of SARS, based on an integrated dataset with 3,336 SARS patients from Hong Kong, Beijing and Taiwan, epidemiological and clinical characteristics such as incubation, onset-to-admission, onset-to-discharge and onset-to-death periods, case fatality ratios (CFRs) and presenting symptoms are described and compared between regions. We further explored the influence of demographic and clinical variables on the apparently large differences in CFRs between the three regions. RESULTS: All three regions showed similar incubation periods and progressive shortening of the onset-to-admission interval through the epidemic. Adjusted for sex, health care worker status and nosocomial setting, older age was associated with a higher fatality, with adjusted odds ratio (AOR): 2.10 (95% confidence interval: 1.45, 3.04) for those aged 51-60; AOR: 4.57 (95% confidence interval: 3.32, 7.30) for those aged above 60 compared to those aged 41-50 years. Presence of pre-existing comorbid conditions was also associated with greater mortality (AOR: 1.74; 95% confidence interval: 1.36, 2.21). CONCLUSION: The large discrepancy in crude fatality ratios across the three regions can only be partly explained by epidemiological and clinical heterogeneities. Our findings underline the importance of a common data collection platform, especially in an emerging epidemic, in order to identify and explain consistencies and differences in the eventual clinical and public health outcomes of infectious disease outbreaks, which is becoming increasingly important in our highly interconnected world.

Tree-structured supervised learning and the genetics of hypertension.

This paper is about an algorithm, FlexTree, for general supervised learning. It extends the binary tree-structured approach (Classification and Regression Trees, CART) although it differs greatly in its selection and combination of predictors. It is particularly applicable to assessing interactions: gene by gene and gene by environment as they bear on complex disease. One model for predisposition to complex disease involves many genes. Of them, most are pure noise; each of the values that is not the prevalent genotype for the minority of genes that contribute to the signal carries a "score." Scores add. Individuals with scores above an unknown threshold are predisposed to the disease. For the additive score problem and simulated data, FlexTree has cross-validated risk better than many cutting-edge technologies to which it was compared when small fractions of candidate genes carry the signal. For the model where only a precise list of aberrant genotypes is predisposing, there is not a systematic pattern of absolute superiority; however, overall, FlexTree seems better than the other technologies. We tried the algorithm on data from 563 Chinese women, 206 hypotensive, 357 hypertensive, with information on ethnicity, menopausal status, insulin-resistant status, and 21 loci. FlexTree and Logic Regression appear better than the others in terms of Bayes risk. However, the differences are not significant in the usual statistical sense.