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1.  Geographical heterogeneity and influenza infection within households 
BMC Infectious Diseases  2014;14:369.
Although it has been suggested that schoolchildren vaccination reduces influenza morbidity and mortality in the community, it is unknown whether geographical heterogeneity would affect vaccine effectiveness.
A 3-year prospective, non-randomized sero-epidemiological study was conducted during 2008–2011 by recruiting schoolchildren from both urban and rural areas. Respective totals of 124, 206, and 176 households were recruited and their household contacts were followed. Serum samples were collected pre-vaccination, one-month post-vaccination and post-season from children and household contacts for hemagglutination inhibition (HI) assay. A multivariate logistic model implemented with generalized estimation equations (GEE) was fitted with morbidity or a four-fold increase in HI titer of the household contacts for two consecutive sera as the dependent variable; with geographical location, vaccination status of each household and previous vaccination history as predictor variables.
Although our results show no significant reduction in the proportion of infection or clinical morbidity among household contacts, a higher risk of infection, indicated by odds ratio > 1, was consistently observed among household children contacts from the un-vaccinated households after adjusting for confounding variables. Interestingly, a statistically significant lower risk of infection was observed among household adult contacts from rural area when compared to those from urban area (OR = 0.89; 95% CI: 0.82-0.97 for Year 2 and OR = 0.85; 95% CI: 0.75-0.96 for Year 3).
A significant difference in the risk of influenza infection among household adults due to geographical heterogeneity, independent of schoolchildren vaccination status, was revealed in this study. Its impact on vaccine effectiveness requires further study.
PMCID: PMC4094897  PMID: 24993483
Influenza; Trivalent Inactivated Vaccine (TIV); Children; Household contacts; Geographical heterogeneity
2.  Asymptomatic ratio for seasonal H1N1 influenza infection among schoolchildren in Taiwan 
Studies indicate that asymptomatic infections do indeed occur frequently for both seasonal and pandemic influenza, accounting for about one-third of influenza infections. Studies carried out during the 2009 pH1N1 pandemic have found significant antibody response against seasonal H1N1 and H3N2 vaccine strains in schoolchildren receiving only pandemic H1N1 monovalent vaccine, yet reported either no symptoms or only mild symptoms.
Serum samples of 255 schoolchildren, who had not received vaccination and had pre-season HI Ab serotiters <40, were collected from urban, rural areas and an isolated island in Taiwan during the 2005–2006 influenza season. Their hemagglutination inhibition antibody (HI Ab) serotiters against the 2005 A/New Caledonia/20/99 (H1N1) vaccine strain at pre-season and post-season were measured to determine the symptoms with the highest correlation with infection, as defined by 4-fold rise in HI titer. We estimate the asymptomatic ratio, or the proportion of asymptomatic infections, for schoolchildren during the 2005–6 influenza season when this vaccine strain was found to be antigenically related to the circulating H1N1 strain.
Fever has the highest correlation with the 2005–06 seasonal influenza A(H1N1) infection, followed by headache, cough, vomiting, and sore throat. Asymptomatic ratio for the schoolchildren is found to range between 55.6% (95% CI: 44.7-66.4)-77.9% (68.8-87.0) using different sets of predictive symptoms. Moreover, the asymptomatic ratio was 66.9% (56.6-77.2) when using US-CDC criterion of fever + (cough/sore throat), and 73.0 (63.3-82.8) when under Taiwan CDC definition of Fever + (cough or sore throat or nose) + ( headache or pain or fatigue).
Asymptomatic ratio for children is found to be substantially higher than that of the general population in literature. In providing reasonable quantification of the asymptomatic infected children spreading pathogens to others in a seasonal epidemic or a pandemic, our estimates of symptomatic ratio of infected children has important clinical and public health implications.
PMCID: PMC3938038  PMID: 24520993
Seasonal influenza; H1N1; Asymptomatic ratio; Asymptomatic infection; Taiwan; Symptoms
3.  Detecting Rare Variants in Case-Parents Association Studies 
PLoS ONE  2013;8(9):e74310.
Despite the success of genome-wide association studies (GWASs) in detecting common variants (minor allele frequency ≥0.05) many suggested that rare variants also contribute to the genetic architecture of diseases. Recently, researchers demonstrated that rare variants can show a strong stratification which may not be corrected by using existing methods. In this paper, we focus on a case-parents study and consider methods for testing group-wise association between multiple rare (and common) variants in a gene region and a disease. All tests depend on the numbers of transmitted mutant alleles from parents to their diseased children across variants and hence they are robust to the effect of population stratification. We use extensive simulation studies to compare the performance of four competing tests: the largest single-variant transmission disequilibrium test (TDT), multivariable test, combined TDT, and a likelihood ratio test based on a random-effects model. We find that the likelihood ratio test is most powerful in a wide range of settings and there is no negative impact to its power performance when common variants are also included in the analysis. If deleterious and protective variants are simultaneously analyzed, the likelihood ratio test was generally insensitive to the effect directionality, unless the effects are extremely inconsistent in one direction.
PMCID: PMC3784439  PMID: 24086332
4.  Gout and subsequent increased risk of cardiovascular mortality in non-diabetics aged 50 and above: a population-based cohort study in Taiwan 
Limited data are available on the risk ratios for fatal cardiovascular disease (CVD) outcome from gout and chronic kidney disease (CKD) in non-diabetic individuals.
Nationwide population-based retrospective prospective study with a 5-year follow-up to investigate the association between physician-diagnosed gout and CKD in non-diabetics aged 50 and above who had no pre-existing serious CVD and the subsequent risk of death from CVD. Hazard ratios (HR) of CVD mortality were adjusted for gender, age, smoking- and alcoholism-related diagnoses, hypertension, hyperlipidemia, atrial fibrillation and Charlson’s comorbidity index score.
A case cohort (n=164,463) having gout and a control cohort (n= 3,694,377) having no gout were formed. The prevalence of gout in this study was 4.26% whereas that of gout plus CKD was 8.17%. Male to female ratio among the individuals with gout was 3.2:1. The relative risk (RR) of subsequent cardiovascular mortality between the case and control cohort was 1.71 (95% confidence interval (CI), 1.66-1.75). The presence of CKD in nondiabetic subjects with no gout (control group) has a RR of CVD mortality at 3.05 (95% CI, 2.94-3.15). The presence of gout has protective effect on subjects with CKD with a RR of 1.84 (95% CI, 1.71-1.98). As compared with individuals with no gout, the adjusted HR (aHR) for CVD mortality among the individuals with gout was 1.10 (95% CI 1.07-1.13). In a Cox model, when compared with subjects having neither gout nor CKD, the aHR in subjects with no gout but with CKD is 1.76 (95% CI, 1.70-1.82); in subjects with gout but without CKD, 1.10 (1.07-1.13); interestingly, the aHR is attenuated in subjects with concomitant gout plus CKD which is 1.38 (1.29-1.48).
Among non-diabetic individuals aged 50 years or above who had no preceding serious CVD, those with gout were 1.1 times more likely to die from CVD as were individuals without gout. The presence of gout appears to attenuate the risk of subsequent CV mortality in subjects with CKD. Further studies should focus on finding an explanation for the protective effect of gout on CV mortality in patients with CKD.
PMCID: PMC3556493  PMID: 23170782
5.  Assessment of performance of survival prediction models for cancer prognosis 
Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments.
We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models.
A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1) For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2) The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3) Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results.
1) Different performance metrics for evaluation of a survival prediction model may give different conclusions in its discriminatory ability. 2) Evaluation using a high-risk versus low-risk group comparison depends on the selected risk-score threshold; a plot of p-values from all possible thresholds can show the sensitivity of the threshold selection. 3) A randomization test of the significance of Somers’ rank correlation can be used for further evaluation of performance of a prediction model. 4) The cross-validated power of survival prediction models decreases as the training and test sets become less balanced.
PMCID: PMC3410808  PMID: 22824262
6.  Transmissibility and temporal changes of 2009 pH1N1 pandemic during summer and fall/winter waves 
BMC Infectious Diseases  2011;11:332.
In order to compare the transmissibility of the 2009 pH1N1 pandemic during successive waves of infections in summer and fall/winter in the Northern Hemisphere, and to assess the temporal changes during the course of the outbreak in relation to the intervention measures implemented, we analyze the epidemiological patterns of the epidemic in Taiwan during July 2009-March 2010.
We utilize the multi-phase Richards model to fit the weekly cumulative pH1N1 epidemiological data (numbers of confirmed cases and hospitalizations) as well as the daily number of classes suspended under a unique "325" partial school closing policy in Taiwan, in order to pinpoint the turning points of the summer and fall/winter waves, and to estimate the reproduction numbers R for each wave.
Our analysis indicates that the summer wave had slowed down by early September when schools reopened for fall. However, a second fall/winter wave began in late September, approximately 4 weeks after the school reopened, peaking at about 2-3 weeks after the start of the mass immunization campaign in November. R is estimated to be in the range of 1.04-1.27 for the first wave, and between 1.01-1.05 for the second wave.
Transmissibility of the summer wave in Taiwan during July-early September, as measured by R, was lower than that of the earlier spring outbreak in North America and Europe, as well as that of the winter outbreak in Southern Hemisphere. Furthermore, transmissibility during fall/winter in Taiwan was noticeably lower than that of the summer, which is attributable to population-level immunity acquired from the earlier summer wave and also to the intervention measures that were implemented prior to and during the fall/winter wave.
PMCID: PMC3247203  PMID: 22136530
7.  Correction: Serological Evidence of Subclinical Transmission of the 2009 Pandemic H1N1 Influenza Virus Outside of Mexico 
PLoS ONE  2011;6(5):10.1371/annotation/edeb5a9c-04b2-4d09-ae2b-6d527bf27c81.
PMCID: PMC3103590
8.  Serological Evidence of Subclinical Transmission of the 2009 Pandemic H1N1 Influenza Virus Outside of Mexico 
PLoS ONE  2011;6(1):e14555.
Relying on surveillance of clinical cases limits the ability to understand the full impact and severity of an epidemic, especially when subclinical cases are more likely to be present in the early stages. Little is known of the infection and transmissibility of the 2009 H1N1 pandemic influenza (pH1N1) virus outside of Mexico prior to clinical cases being reported, and of the knowledge pertaining to immunity and incidence of infection during April–June, which is essential for understanding the nature of viral transmissibility as well as for planning surveillance and intervention of future pandemics.
Methodology/Principal Findings
Starting in the fall of 2008, 306 persons from households with schoolchildren in central Taiwan were followed sequentially and serum samples were taken in three sampling periods for haemagglutination inhibition (HI) assay. Age-specific incidence rates were calculated based on seroconversion of antibodies to the pH1N1 virus with an HI titre of 1∶40 or more during two periods: April–June and September–October in 2009. The earliest time period with HI titer greater than 40, as well as a four-fold increase of the neutralization titer, was during April 26–May 3. The incidence rates during the pre-epidemic phase (April–June) and the first wave (July–October) of the pandemic were 14.1% and 29.7%, respectively. The transmissibility of the pH1N1 virus during the early phase of the epidemic, as measured by the effective reproductive number R0, was 1.16 (95% confidence interval (CI): 0.98–1.34).
Approximately one in every ten persons was infected with the 2009 pH1N1 virus during the pre-epidemic phase in April–June. The lack of age-pattern in seropositivity is unexpected, perhaps highlighting the importance of children as asymptomatic transmitters of influenza in households. Although without virological confirmation, our data raise the question of whether there was substantial pH1N1 transmission in Taiwan before June, when clinical cases were first detected by the surveillance network.
PMCID: PMC3022590  PMID: 21267441
9.  A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data 
PLoS ONE  2010;5(8):e12493.
Mass spectrometry is a high throughput, fast, and accurate method of protein analysis. Using the peaks detected in spectra, we can compare a normal group with a disease group. However, the spectrum is complicated by scale shifting and is also full of noise. Such shifting makes the spectra non-stationary and need to align before comparison. Consequently, the preprocessing of the mass data plays an important role during the analysis process. Noises in mass spectrometry data come in lots of different aspects and frequencies. A powerful data preprocessing method is needed for removing large amount of noises in mass spectrometry data.
Hilbert-Huang Transformation is a non-stationary transformation used in signal processing. We provide a novel algorithm for preprocessing that can deal with MALDI and SELDI spectra. We use the Hilbert-Huang Transformation to decompose the spectrum and filter-out the very high frequencies and very low frequencies signal. We think the noise in mass spectrometry comes from many sources and some of the noises can be removed by analysis of signal frequence domain. Since the protein in the spectrum is expected to be a unique peak, its frequence domain should be in the middle part of frequence domain and will not be removed. The results show that HHT, when used for preprocessing, is generally better than other preprocessing methods. The approach not only is able to detect peaks successfully, but HHT has the advantage of denoising spectra efficiently, especially when the data is complex. The drawback of HHT is that this approach takes much longer for the processing than the wavlet and traditional methods. However, the processing time is still manageable and is worth the wait to obtain high quality data.
PMCID: PMC2930864  PMID: 20824164

Results 1-9 (9)