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Background: It was reported that 35.5% of tuberculosis (TB) cases reported in 2003 in Taipei City had no recorded pre- treatment body weight and that among those who had, inconsistent dosing of anti-TB drugs was frequent. Taiwan Centers for Disease Control (CDC) have taken actions to strengthen dosing of anti-TB drugs among general practitioners. Prescribing practices of anti-TB drugs in Taipei City in 2007-2010 were investigated to assess whether interventions on dosing were effective. Methodology/Principal Findings: Lists of all notified culture positive TB cases in 2007-2010 were obtained from National TB Registry at Taiwan CDC. A medical audit of TB case management files was performed to collect pretreatment body weight and regimens prescribed at commencement of treatment. Dosages prescribed were compared with dosages recommended. The proportion of patients with recorded pre-treatment body weight was 64.5% in 2003, which increased to 96.5% in 2007-2010 (p<0.001). The proportion of patients treated with consistent dosing of a 3-drug fixed-dose combination (FDC) increased from 73.9% in 2003 to 87.7% in 2007-2010 (p<0.001), and that for 2- drug FDC from 76.0% to 86.1% (p = 0.024), for rifampicin (RMP) from 62.8% to 85.5% (p<0.001), and for isoniazid from 87.8% to 95.3% (p<0.001). In 2007-2010, among 2917 patients treated with either FDCs or RMP in single-drug preparation, the dosage of RMP was adequate (8-12 mg/kg) in 2571(88.1%) patients, too high in 282(9.7%), too low in 64(2.2%). In multinomial logistic regression models, factors significantly associated with adequate dosage of RMP were body weight and preparations of RMP. Patients weighting ,40kg (relative risk ratio (rrr) 6010.5, 95% CI 781.1-46249.7) and patients weighting 40-49 kg (rrr 1495.3, 95% CI 200.6-11144.6) were more likely to receive higher-than-recommended dose of RMP. [ABSTRACT FROM AUTHOR]

Background: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. Methods and Findings: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. Conclusions: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs. [ABSTRACT FROM AUTHOR]