Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

The Treatment of Cognitive Dysfunction in Dementia: A Multiple Treatments Meta-analysis

[[abstract]]Objective No cure is currently available for dementia; however, various treatments and interventions have been reported to be effective. The factors influencing the efficacy of dementia treatment have not been comprehensively evaluated. This study evaluated the factors influencing treatment effects on cognitive dysfunction in dementia by comparing the results obtained from a meta-analysis based on meta-regression. Methods We searched for articles, clinical trials, and meta-analyses on the efficacy of pharmacotherapy or psychosocial treatment for dementia published between 2000 and 2016 in the MEDLINE/PubMed, Cochrane Library, SCOPUS, and Airiti Library databases. Results The 235 selected studies involved 44,854 patients with dementia (mainly vascular dementia, Alzheimer disease, and mild cognitive impairment). A preliminary random effects meta-analysis yielded a positive overall effect. The pooled standardized mean difference of the treatment effects on cognitive dysfunction was 0.439 (95% confidence interval 0.374, 0.504). The results of meta-regression showed that in young patients (β = − 0.036, p value < 0.001) with vascular dementia (β = 0.603, p value < 0.001), the efficacies of treatment 2 (symptomatic treatment for vascular dementia with piracetam, nimodipine, aniracetam, flunarizine, vinpocetine, hyperbaric oxygen, oxiracetam, or EGB761) and treatment 5 (treatment with other alternative therapies including acupuncture, premarin, statin, butylphthalide soft capsules, donepezil, huperzine A, and lithium treatment) were higher than those of other existing treatments for cognitive dysfunction (β = 0.308 and 0.321, p values = 0.010 and < 0.001, respectively). Conclusion The most effective intervention for dementia available is symptomatic treatment for vascular dementia. Antipsychotic treatment for dementia alleviates cognitive dysfunction less effectively than does symptomatic treatment. Alternative therapies are also effective at present. Further research on causes and very early diagnosis of Alzheimer disease is warranted.[[notice]]補正完

Effect of Implementing Electronic Toll Collection in Reducing Highway Particulate Matter Pollution

Highway vehicle emissions can result in adverse health problems to nearby residents and workers, especially during traffic congestion. In response, the policy to implement electronic toll collection (ETC) has helped alleviate traffic congestion, as compared to manual toll collection (MTC) and has led to reduced air pollution and improved public health. However, the effect of ETC in reducing particulate matter polluting the air is not well understood, especially in the ultrafine particle (UFP) range (particle diameter <100 nm). To the best of our knowledge, this is the first study to investigate how ETC affects the traffic pattern and air quality, especially UFP and PM2.5. We selected a site in Tainan, Taiwan, and measured UFP and PM2.5 concentrations before and after the construction of the ETC system. The computed traffic volumes during peak travel periods (7:00 AM to 9:00 AM and 4:00 PM to 6:00 PM) respectively, accounted for approximately 23−25% and 14−18% before and after the implementation of ETC, indicating that peak traffic volumes were more homogeneous after ETC. Moreover, the results indicate that the full implementation of ETC can help reduce UFP number concentrations and PM2.5 mass concentrations in the highway downwind area by 4 × 103 #/cm3 and 20.5 μg/m3, respectively. After the full implementation of the ETC, significant reductions in both the UFP number concentration and PM2.5 mass concentration were seen. Furthermore, excessive lifetime cancer risks (ELCR) from exposure to PM2.5 and UFP together were reduced 49.3% after the implementation of the ETC. Accordingly, ETC not only helps alleviate traffic congestion but also reduces traffic emissions and lifetime cancer risk for people living or working near highways