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Background: The causative pathogen is rarely identified in the emergency department (ED), since the results of cultures are usually unavailable. As a result, antimicrobial treatment may be overused. The aim of our study was to investigate the pathogens, risk factors of acute gastroenteritis, and predictors of acute bacterial gastroenteritis in the ED. Methods: We conducted a matched case-control study of 627 stool samples and 612 matched pairs. Results: Viruses (41.3%) were the leading cause of gastroenteritis, with noroviruses (32.2%) being the most prevalent, followed by bacteria (26.8%) and Giardia lamblia (12.4%). Taking antacids (adjusted odds ratio [aOR] 4.10; 95% confidence interval [CI], 2.57-6.53), household members/classmates with gastroenteritis (aOR 4.69; 95% CI, 2.76-7.96), attending a banquet (aOR 2.29; 95% CI, 1.64-3.20), dining out (aOR 1.70; 95% CI, 1.13-2.54), and eating raw oysters (aOR 3.10; 95% CI, 1.61-5.94) were highly associated with gastroenteritis. Elders (aOR 1.04; 05% CI, 1.02-1.05), those with CRP >10 mg/L (aOR 2.04; 95% CI, 1.15-3.62), or those who were positive for fecal leukocytes (aOR 2.04; 95% CI, 1.15-3.62) or fecal occult blood (aOR 1.97; 95% CI, 1.03-3.77) were more likely to be hospitalized in ED. In addition, presence of fecal leukocytes (time ratio [ TR ] 1.22; 95% CI, 1.06-1.41), abdominal pain (TR 1.20; 95% CI, 1.07-1.41), and frequency of vomiting (TR 0.79; 95% CI, 0.64-0.98) were significantly associated with the duration of acute gastroenteritis. Presence of fecal leukocytes (aOR 2.08; 95% CI, 1.42-3.05), winter season (aOR 0.45; 95% CI, 0.28-0.74), frequency of diarrhea (aOR 1.69; 95% CI, 1.01-2.83), and eating shrimp or crab (aOR 1.53; 95% CI, 1.05-2.23) were highly associated with bacterial gastroenteritis. The area under the receiver operating characteristic curve of the final model was 0.68 (95% CI, 0.55-0.63). Conclusions: Acute bacterial gastroenteritis was highly associated with season, frequency of diarrhea, frequency of vomiting, and eating shrimp or crab.

This careful study is based on interviews of passengers and crew members on three flights that carried patients with the severe acute respiratory syndrome (SARS). Twenty-two persons became ill a mean of four days after one of the flights. The risk was highest among the passengers seated within three rows in front of the symptomatic index patient (relative risk, 3.1). After one flight, 22 persons became ill. The severe acute respiratory syndrome (SARS) is characterized by the acute onset of fever with cough, shortness of breath, difficulty breathing, or some combination of these symptoms; the symptoms begin an average of four days after exposure to an infected person. As of September 26, 2003, 8098 cases of SARS and 774 deaths due to SARS (10 percent mortality) in more than 25 countries had been reported to the World Health Organization (WHO). 1 , 2 Although there have been anecdotal reports of transmission on aircraft, the risk, if any, to passengers has not been well documented. WHO and the Centers for . . .

Objective: This study applied the Health Belief Model to investigate factors in the decision by caregivers to vaccinate their children for influenza. Design: Cross-sectional study. Sample and Measurements: Purposive sampling obtained 2,778 useable responses to surveys of 33 public health centers and 40 medical institutions participating in vaccination programs in southern Taiwan. Data were collected using the Caregiver Demographics and Children's Health History Questionnaire, Children's Influenza Vaccination History Questionnaire, and a Health Belief Model Questionnaire. Multiple logistic regression was used to analyze predictors of influenza vaccinations in children. Results: Predictors of vaccination revealed by logistic regression analysis included age, current employment, and residence of the caregiver as well as chronic disease, hospitalization, and influenza histories of the child. Other predictors revealed by the Health Belief Model were perceived susceptibility of the children to influenza, perceived benefits of vaccinations to children, perceived barriers to vaccinations, and cues to action. Eleven items in the model were also significant predictors of vaccination. Conclusions: The survey results can be used to develop strategies for increasing influenza vaccination rates.

Background The reported cases with varicella have not decreased and outbreaks of varicella among vaccinated children continue to be reported 9 years after the public vaccination program in Taipei. We investigated an outbreak to determine varicella vaccine coverage and effectiveness. Methods An outbreak occurred in an elementary school which located in southern Taipei from April 2007 through May 2007. A retrospect cohort study was performed by using a self-administered questionnaire for parents. Results Ten out of sixteen varicella cases were vaccinated. Overall vaccine coverage was 71.2%. The common reasons for not receiving varicella vaccine were that varicella vaccine was unavailable because the student didn't live in Taipei (29.4%) or the children could not be vaccinated due to certain illnesses (23.5%). The sensitivity and specificity of self-reported vaccination status was 0.900 (95% CI: 0.864, 0.935) and 0.611 (95% CI: 0.514, 0.701). The vaccine effectiveness was 69.3%-100.0% against any disease severity of varicella. Overall vaccine effectiveness against moderate or severe varicella was 85.5%. Attending cram school was associated with the risk of developing the varicella illness (RR: 13.39; 95% CI: 5.38, 33.31). Unvaccinated students tended to show moderate to severe (>50 lesions) afflictions of the disease (RR: 4.17; 95% CI: 1.15, 15.14). Conclusions Because of the low vaccination coverage, varicella outbreaks continue to be reported in Taipei. Increasing vaccine coverage and second dose vaccination for increasing vaccine effectiveness may be considered.

A single-tube multiprobe real-time PCR assay for simultaneous detection of Entamoeba histolytica and Entamoeba dispar was developed. One primer pair with 2 species-specific probes was designed based on new SSU RNA regions of the ribosomal DNA-containing episome. The sensitivity is 1 parasite per milliliter of feces and thus superior to the conventional nested PCR and comparable to other published real-time PCR protocols. The applicability for clinical diagnosis was validated with 218 stool specimens from patients. A total of 51 E. histolytica and 39 E. dispar positive samples was detected by the multiprobe real-time PCR compared to 39 and 22 by routine nested PCR diagnosis. The detection rate of Entamoeba species for the multiprobe real-time PCR assays was significantly higher than the nested PCR (40.8% vs. 28.0%, P < 0.01). The test did not show cross reactivity with DNA from Entamoeba moshkovskii, Giardia lamblia, Cryptosporidium sp., Escherichia coli, or other nonpathogenic enteric parasites. The multiprobe real-time PCR assay is simple and rapid and has high specificity and sensitivity. The assay could streamline the laboratory diagnosis procedure and facilitate epidemiological investigation.

In simple logistic regression, one uses information obtained from the disease status (y) and the true exposure (x) to estimate the logistic regression coefficient $\\beta\\sb1.$ When the true exposure (x) is measured with error, one obtains a biased estimator for the logistic regression coefficient $\\beta\\sb1$ by using the observed exposure X, instead of x, in the logistic regression analysis. In order to correct this bias, one first needs to investigate the relationship between the true x and the observed X. Their relationship is usually defined by a measurement error model. We investigated two measurement error models, the classical measurement error model and Rosner's measurement error model. Under certain conditions, these two measurement error models are equivalent in both the univariate and multivariate cases. When the sample size for the main study sample (N$\\sb1)$ and validation sample (N$\\sb2)$ was small, we examined the performance of Rosner's linear approximation estimator $(\\{\\beta\\sb l})$ for normal measurement errors. $(\\{\\beta\\sb l})$ performs well when N$\\sb1$ is at least 300 and N$\\sb2$ not less than 40 for $\\lambda$ = 0.3, N$\\sb2$ not less than 30 for $\\lambda$ = 0.5, and N$\\sb2$ not less than 20 for $\\lambda$ = 0.7. $\\lambda$ is an indicator for the severity of the measurement error problem. The larger the $\\lambda$ value, the more severe the measurement error problem. When $\\lambda$ is 0.1, $\\{\\beta\\sb l}$ should not be used. When $\\lambda$ is 0.9, there is no need to apply $\\{\\beta\\sb l}$. We also evaluated the behavior of $\\{\\beta\\sb l}$ for non-normal measurement errors. $\\{\\beta\\sb l}$ is not robust for measurement error sampling distributions (uniform, normal, logistic, Laplace, and centered lognormal) used in this study. The best performance by $\\{\\beta\\sb l}$ occurs when the measurement error is distributed normally. The symmetry of the distribution may play a role in the performance of $\\{\\beta\\sb l}.$ The distributional tail does not affect the performance of $\\{\\beta\\sb l}.$ However, the behavior of $\\{\\beta\\sb l}$ depends on N$\\sb2, \\lambda,$ and the measurement error distribution.