Micro-scale urbanization-based risk factors for dengue epidemics.

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks.

Disease mapping for spatially semi-continuous data by estimating equations with application to dengue control.

Disease mapping is a research field to estimate spatial pattern of disease risks so that areas with elevated risk levels can be identified. The motivation of this article is from a study of dengue fever infection, which causes seasonal epidemics in almost every summer in Taiwan. For analysis of zero-inflated data with spatial correlation and covariates, current methods would either cause a computational burden or miss associations between zero and non-zero responses. In this article, we develop estimating equations for a mixture regression model that accommodates spatial dependence and zero inflation for study of disease propagation. Asymptotic properties for the proposed estimates are established. A simulation study is conducted to evaluate performance of the mixture estimating equations; and a dengue dataset from southern Taiwan is used to illustrate the proposed method.

Identification of geographic clusters for temporal heterogeneity with application to dengue surveillance.

Identifying transmission of hot spots with temporal trends is important for reducing infectious disease propagation. Cluster analysis is a particularly useful tool to explore underlying stochastic processes between observations by grouping items into categories by their similarity. In a study of epidemic propagation, clustering geographic regions that have similar time series could help researchers track diffusion routes from a common source of an infectious disease. In this article, we propose a two-stage scan statistic to classify regions into various geographic clusters by their temporal heterogeneity. The proposed scan statistic is more flexible than traditional methods in that contiguous and nonproximate regions with similar temporal patterns can be identified simultaneously. A simulation study and data analysis for a dengue fever infection are also presented for illustration.

Real-time dengue forecast for outbreak alerts in Southern Taiwan.

Dengue fever is a viral disease transmitted by mosquitoes. In recent decades, dengue fever has spread throughout the world. In 2014 and 2015, southern Taiwan experienced its most serious dengue outbreak in recent years. Some statistical models have been established in the past, however, these models may not be suitable for predicting huge outbreaks in 2014 and 2015. The control of dengue fever has become the primary task of local health agencies. This study attempts to predict the occurrence of dengue fever in order to achieve the purpose of timely warning. We applied a newly developed autoregressive model (AR model) to assess the association between daily weather variability and daily dengue case number in 2014 and 2015 in Kaohsiung, the largest city in southern Taiwan. This model also contained additional lagged weather predictors, and developed 5-day-ahead and 15-day-ahead predictive models. Our results indicate that numbers of dengue cases in Kaohsiung are associated with humidity and the biting rate (BR). Our model is simple, intuitive and easy to use. The developed model can be embedded in a "real-time" schedule, and the data (at present) can be updated daily or weekly based on the needs of public health workers. In this study, a simple model using only meteorological factors performed well. The proposed real-time forecast model can help health agencies take public health actions to mitigate the influences of the epidemic.

The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study.

In recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong's subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.

A heterogeneity measure for cluster identification with application to disease mapping.

Mapping of disease incidence has long been of importance to epidemiology and public health. In this paper, we consider identification of clusters of spatial units with elevated disease rates and develop a new approach that estimates the relative disease risk in association with potential risk factors and simultaneously identifies clusters corresponding to elevated risks. A heterogeneity measure is proposed to enable the comparison of a candidate cluster and its complement under a pair of complementary models. A quasi-likelihood procedure is developed for estimating the model parameters and identifying the clusters. An advantage of our approach over traditional spatial clustering methods is the identification of clusters that can have arbitrary shapes due to abrupt or noncontiguous changes while accounting for risk factors and spatial correlation. Asymptotic properties of the proposed methodology are established and a simulation study shows empirically sound finite-sample properties. The mapping and clustering of enterovirus 71 infections in Taiwan are carried out for illustration.

Functional Correlates of Resting-State Connectivity in the Default Mode Network of Heroin Users on Methadone Treatment and Medication-Free Therapeutic Community Program.

The treatment of heroin addiction is a complex process involving changes in addictive behavior and brain functioning. The goal of this study was to explore the brain default mode network (DMN) functional connectivity using resting-state functional magnetic resonance imaging (rs-fMRI) and decision-making performance based on the Cambridge gambling task in heroin-dependent individuals undergoing methadone treatment (MT, n = 11) and medication-free faith-based therapeutic community program (TC, n = 11). The DMN involved the medial prefrontal cortex (mPFC), left inferior parietal lobe (IPLL), right inferior parietal lobe (IPLR), and posterior cingulate cortex (PCC) subregions for all participants in both the MT and TC groups. Compared with MT, TC had an increased functional connectivity in IPLL-IPLR and IPLR-PCC and decreased functional connectivity in mPFC-IPLL and IPLL-PCC. Both groups exhibited no significant difference in the regional rs-fMRI metric [i.e., amplitude of low-frequency fluctuation (ALFF)]. In the analysis of the neural correlates for decision-making performance, risk adjustment was positively associated with ALFF in IPLL for all participants considering the group effects. The involvement of IPL in decision-making performance and treatment response among heroin-dependent patients warrants further investigation.

Prediction of annual dengue incidence by hydro-climatic extremes for southern Taiwan.

Dengue is one of the most rapidly spreading mosquito-borne viral diseases in the world. An increase in the incidence of dengue is commonly thought to be a consequence of variability of weather conditions. Taiwan, which straddles the Tropic of Cancer, is an excellent place to study the relationship between weather conditions and dengue fever cases since the island forms an isolated geographic environment. Therefore, clarifying the association between extreme weather conditions and annual dengue incidence is one of important issues for epidemic early warning. In this paper, we develop a Poisson regression model with extreme weather parameters for prediction of annual dengue incidence. A leave-one-out method is used to evaluate the performance of predicting dengue incidence. Our results indicate that dengue transmission has a positive relationship with the minimum temperature predictors during the early summer while a negative relationship with all the maximum 24-h rainfall predictors during the early epidemic phase of dengue outbreaks. Our findings provide a better understanding of the relationships between extreme weather and annual trends in dengue cases in Taiwan and it could have important implications for dengue forecasts in surrounding areas with similar meteorological conditions.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan.

Comparison of escitalopram and paroxetine in the treatment of major depressive disorder.

This is a single-blind, parallel, flexible-dose study to compare the efficacy and tolerability of escitalopram and paroxetine in the treatment of patients with major depressive disorder. We recruited 399 patients from the outpatient clinics of five hospitals in northern Taiwan. Patients were administered either escitalopram (10-30 mg) or paroxetine (20-40 mg) according to the judgment of clinicians. These patients were assessed using the Hamilton Rating Scale for Depression (HAM-D) and the Hamilton Rating Scale for Anxiety at weeks 0, 1, 2, 4, 6, and 8. A total of 302 patients fulfilled the evaluation criteria and were included in a statistical analysis. We found that escitalopram induced more significant symptom reduction and response rate in terms of the mean HAM-D scores at week 6 (P<0.05) and week 8 (P<0.05) than paroxetine, but that there were no significant differences between the two groups in the remission rate. Escitalopram induced significantly less frequency of adverse effects of weakness (P<0.01), nausea and vomiting (P<0.001), drowsiness (P<0.01) as well as somnolence (P<0.01) than paroxetine, although all these side effects were mild and tolerable. However for a more definitive result, future prospective trials with the inclusion of a placebo group and a double-blind design are needed. In patients who did not have severe depression (HAM-D score at baseline<21), but not in severely depressed patients, escitalopram was statistically superior to paroxetine, as shown by the mean change in the HAM-D score.

Distribution and drug susceptibilities of Candida species causing candidemia from a medical center in central Taiwan.

Invasive fungal infections have increased significantly in the past few decades because of the increase in high-risk populations. To investigate the distribution and drug susceptibilities of such infections, we analyzed all 152 Candida isolates causing candidemia from 2004 to 2006 at the China Medical University Hospital, a medical center in central Taiwan. Candida albicans was the most common species, accounting for 52.6% of the isolates, followed by C. tropicalis (19.7%), C. parapsilosis (14.5%), C. glabrata (8.6%), C. guilliermondii (3.9%), and C. pelliculosa (0.7%). All isolates were susceptible to amphotericin B, anidulafungin, micafungin, and voriconazole according to minimum inhibitory concentrations (MICs) after a 24-h incubation; 0.7%, 6.6%, and 7.9% of isolates were resistant to amphotericin B, fluconazole, and voriconazole, respectively, after 48-h incubation. Both C. albicans and C. parapsilosis had high degrees of agreement for azoles between 24- and 48-h incubation periods, whereas C. glabrata (38.5-46.2%) and C. tropicalis (56.7-63.3%) did not. The majority of the isolates with high azole MICs displayed a trailing growth phenotype. Hence, the MICs of different drugs after 24-h incubation may be considered for prognosis of candidemia.

CYP2B6 polymorphisms influence the plasma concentration and clearance of the methadone S-enantiomer.

Methadone is a racemic compound composed of the R-form and S-form enantiomers. The drug is usually used in maintenance therapy for the heroin-addicted patients. In our previous study, we found that the cytochrome P-450 (CYP) isozyme 2B6 preferentially metabolizes the S-methadone enantiomer. We thus tested whether CYP2B6 gene polymorphisms had any influence on the concentration or clearance of methadone. Ten single nucleotide polymorphisms within this gene region were evaluated in 366 patients undergoing methadone maintenance for at least 3 months. The plasma steady-state levels of racemic methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine were then measured in these individuals. The rs10403955 (T allele in intron 1), rs3745274 (G allele in exon 4), rs2279345 (T allele in intron 5), and rs707265 (A allele in exon 9) CYP2B6 allele types were found to be significantly associated with a higher clearance, a lower plasma concentration, and a lower concentration-to-dosage (C/D) ratio of (S)-methadone (P < 0.0017). Two haplotype blocks of a trinucleotide haplotype (rs8100458-rs10500282-rs10403955 in intron 1) and a hexanucleotide haplotype (rs2279342-rs3745274-rs2279343-rs2279345-rs1038376-rs707265 from intron 2 to exon 9) were constructed within CYP2B6. The major combinations of T-T-T and A-G-A-T-A-A of these particular haplotypes showed significant associations with the plasma concentrations of S-methadone and its C/D ratio (P < 0.0001, respectively). We conclude that genetic polymorphisms in the CYP2B6 gene may therefore be indicators of the clearance, plasma concentration and C/D ratio of S-methadone.