Displaying publications 41 - 57 of 57 in total

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  1. Mapping and modelling the geographical distribution of soil-transmitted helminthiases in Peninsular Malaysia: implications for control approaches
    Ngui R, Shafie A, Chua KH, Mistam MS, Al-Mekhlafi HM, Sulaiman WW, et al.
    Geospat Health, 2014 May;8(2):365-76.
    PMID: 24893014
    Soil-transmitted helminth (STH) infections in Malaysia are still highly prevalent, especially in rural and remote communities. Complete estimations of the total disease burden in the country has not been performed, since available data are not easily accessible in the public domain. The current study utilised geographical information system (GIS) to collate and map the distribution of STH infections from available empirical survey data in Peninsular Malaysia, highlighting areas where information is lacking. The assembled database, comprising surveys conducted between 1970 and 2012 in 99 different locations, represents one of the most comprehensive compilations of STH infections in the country. It was found that the geographical distribution of STH varies considerably with no clear pattern across the surveyed locations. Our attempt to generate predictive risk maps of STH infections on the basis of ecological limits such as climate and other environmental factors shows that the prevalence of Ascaris lumbricoides is low along the western coast and the southern part of the country, whilst the prevalence is high in the central plains and in the North. In the present study, we demonstrate that GIS can play an important role in providing data for the implementation of sustainable and effective STH control programmes to policy-makers and authorities in charge.
    Matched MeSH terms: Spatial Analysis
  2. Measurement of dengue epidemic spreading pattern using density analysis method: retrospective spatial statistical study of dengue in Subang Jaya, Malaysia, 2006-2010
    Dom NC, Ahmad AH, Latif ZA, Ismail R
    Trans R Soc Trop Med Hyg, 2013 Nov;107(11):715-22.
    PMID: 24062522 DOI: 10.1093/trstmh/trt073
    Dengue has emerged as one of the major public health problems in Malaysia. The Ministry of Health, Malaysia, is committed in monitoring and controlling this disease for many years. The objective of this study is to analyze the dengue outbreak pattern on a monthly basis in Subang Jaya in terms of their spatial dissemination and hotspot identification.
    Matched MeSH terms: Spatial Analysis
  3. Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques
    Ismail A, Toriman ME, Juahir H, Zain SM, Habir NL, Retnam A, et al.
    Mar Pollut Bull, 2016 May 15;106(1-2):292-300.
    PMID: 27001716 DOI: 10.1016/j.marpolbul.2015.10.019
    This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time.

    CAPSULE: The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis.

    Matched MeSH terms: Spatial Analysis
  4. Spatial analysis and source profiling of beta-agonists and sulfonamides in Langat River basin, Malaysia
    Sakai N, Mohd Yusof R, Sapar M, Yoneda M, Ali Mohd M
    Sci Total Environ, 2016 Apr 01;548-549:43-50.
    PMID: 26799806 DOI: 10.1016/j.scitotenv.2016.01.040
    Beta-agonists and sulfonamides are widely used for treating both humans and livestock for bronchial and cardiac problems, infectious disease and even as growth promoters. There are concerns about their potential environmental impacts, such as producing drug resistance in bacteria. This study focused on their spatial distribution in surface water and the identification of pollution sources in the Langat River basin, which is one of the most urbanized watersheds in Malaysia. Fourteen beta-agonists and 12 sulfonamides were quantitatively analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to visualize catchment areas of the sampling points, and source profiling was conducted to identify the pollution sources based on a correlation between a daily pollutant load of the detected contaminant and an estimated density of human or livestock population in the catchment areas. As a result, 6 compounds (salbutamol, sulfadiazine, sulfapyridine, sulfamethazine, sulfadimethoxine and sulfamethoxazole) were widely detected in mid catchment areas towards estuary. The source profiling indicated that the pollution sources of salbutamol and sulfamethoxazole were from sewage, while sulfadiazine was from effluents of cattle, goat and sheep farms. Thus, this combination method of quantitative and spatial analysis clarified the spatial distribution of these drugs and assisted for identifying the pollution sources.
    Matched MeSH terms: Spatial Analysis
  5. Spatial scaling effects on soil bacterial communities in Malaysian tropical forests
    Tripathi BM, Lee-Cruz L, Kim M, Singh D, Go R, Shukor NA, et al.
    Microb Ecol, 2014 Aug;68(2):247-58.
    PMID: 24658414
    Spatial scaling to some extent determines biodiversity patterns in larger organisms, but its role in microbial diversity patterns is much less understood. Some studies have shown that bacterial community similarity decreases with distance, whereas others do not support this. Here, we studied soil bacterial communities of tropical rainforest in Malaysia at two spatial scales: a local scale with samples spaced every 5 mover a 150-m transect, and a regional scale with samples 1 to 1,800 km apart. PCR-amplified soil DNA for the bacterial 16S rRNA gene targeting the V1–V3 region was pyrosequenced using Roche/454 GS FLX Titanium platform. A ranked partial Mantel test showed a weak correlation between spatial distance and whole bacterial community dissimilarity, but only at the local scale. In contrast, environmental distance was highly correlated with community dissimilarity at both spatial scales,stressing the greater role of environmental variables rather than spatial distance in determining bacterial community variation at different spatial scales. Soil pH was the only environmental parameter that significantly explained the variance in bacterial community at the local scale, whereas total nitrogen and elevation were additional important factors at the regional scale.We obtained similar results at both scales when only the most abundant OTUs were analyzed. A variance partitioning analysis showed that environmental variables contributed more to bacterial community variation than spatial distance at both scales. In total, our results support a strong influence of the environment in determining bacterial community composition in the rainforests of Malaysia. However, it is possible that the remaining spatial distance effect is due to some of the myriad of other environmental factors which were not considered here, rather than dispersal limitation.
    Matched MeSH terms: Spatial Analysis
  6. Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia
    Othman M, Latif MT, Jamhari AA, Abd Hamid HH, Uning R, Khan MF, et al.
    Chemosphere, 2021 Jan;262:127767.
    PMID: 32763576 DOI: 10.1016/j.chemosphere.2020.127767
    This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.
    Matched MeSH terms: Spatial Analysis
  7. Fulltext Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia
    Low GKK, Papapreponis P, Isa RM, Gan SC, Chee HY, Te KK, et al.
    Geospat Health, 2018 05 07;13(1):642.
    PMID: 29772885 DOI: 10.4081/gh.2018.642
    Increasing numbers of dengue infection worldwide have led to a rise in deaths due to complications caused by this disease. We present here a cross-sectional study of dengue patients who attended the Emergency and Trauma Department of Ampang Hospital, one of Malaysia's leading specialist hospitals. The objective was to search for potential clustering of severe dengue, in space and/or time, among the annual admissions with the secondary objective to describe the spatio-temporal pattern of all dengue cases admitted to this hospital. The dengue status of the patients was confirmed serologically with the geographic location of the patients determined by residency, but not more specific than the street level. A total of 1165 dengue patients were included in the analysis using SaTScan software. The mean age of these patients was 27.8 years, with a standard deviation of 14.2 years and an age range from 1 to 77 years, among whom 54 (4.6%) were cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 but the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence ratio was 1.51. General presence of dengue cases was, however, detected to be concentrated at the end of the year, which should be useful for hospital planning and management if this pattern holds.
    Matched MeSH terms: Spatial Analysis
  8. Fulltext Socio-epidemiological and land cover risk factors for melioidosis in Kedah, Northern Malaysia
    Abu Hassan MR, Aziz N, Ismail N, Shafie Z, Mayala B, Donohue RE, et al.
    PLoS Negl Trop Dis, 2019 03;13(3):e0007243.
    PMID: 30883550 DOI: 10.1371/journal.pntd.0007243
    BACKGROUND: Melioidosis, a fatal infectious disease caused by Burkholderia pseudomallei, is increasingly diagnosed in tropical regions. However, data on risk factors and the geographic epidemiology of the disease are still limited. Previous studies have also largely been based on the analysis of case series data. Here, we undertook a more definitive hospital-based matched case-control study coupled with spatial analysis to identify demographic, socioeconomic and landscape risk factors for bacteremic melioidosis in the Kedah region of northern Malaysia.

    METHODOLOGY/PRINCIPAL FINDINGS: We obtained patient demographic and residential information and clinical presentation and medical history data from 254 confirmed melioidosis cases and 384 matched controls attending Hospital Sultanah Bahiyah (HSB), the main tertiary hospital of Alor Setar, the capital city of Kedah, during the period between 2005 and 2011. Crude and adjusted odds ratios employing conditional logistic regression analysis were used to assess if melioidosis in this region is related to risk factors connected with socio-demographics, various behavioural characteristics, and co-occurring diseases. Spatial clusters of cases were determined using a continuous Poisson model as deployed in SaTScan. A land cover map in conjunction with mapped case data was used to determine disease-land type associations using the Fisher's exact test deploying simulated p-values. Crude and adjusted odds ratios indicate that melioidosis in this region is related to gender (males), race, occupation (farming) and co-occurring chronic diseases, particularly diabetes. Spatial analyses of disease incidence, however, showed that disease risk and geographic clustering of cases are related strongly to land cover types, with risk of disease increasing non-linearly with the degree of human modification of the natural ecosystem.

    CONCLUSIONS/SIGNIFICANCE: These findings indicate that melioidosis represents a complex socio-ecological public health problem in Kedah, and that its control requires an understanding and modification of the coupled human and natural variables that govern disease transmission in endemic communities.

    Matched MeSH terms: Spatial Analysis
  9. Fulltext Future malaria spatial pattern based on the potential global warming impact in South and Southeast Asia
    Khormi HM, Kumar L
    Geospat Health, 2016 11 21;11(3):416.
    PMID: 27903054 DOI: 10.4081/gh.2016.416
    We used the Model for Interdisciplinary Research on Climate-H climate model with the A2 Special Report on Emissions Scenarios for the years 2050 and 2100 and CLIMEX software for projections to illustrate the potential impact of climate change on the spatial distributions of malaria in China, India, Indochina, Indonesia, and The Philippines based on climate variables such as temperature, moisture, heat, cold and dryness. The model was calibrated using data from several knowledge domains, including geographical distribution records. The areas in which malaria has currently been detected are consistent with those showing high values of the ecoclimatic index in the CLIMEX model. The match between prediction and reality was found to be high. More than 90% of the observed malaria distribution points were associated with the currently known suitable climate conditions. Climate suitability for malaria is projected to decrease in India, southern Myanmar, southern Thailand, eastern Borneo, and the region bordering Cambodia, Malaysia and the Indonesian islands, while it is expected to increase in southern and south-eastern China and Taiwan. The climatic models for Anopheles mosquitoes presented here should be useful for malaria control, monitoring, and management, particularly considering these future climate scenarios.
    Matched MeSH terms: Spatial Analysis
  10. Fulltext Predictive analysis across spatial scales links zoonotic malaria to deforestation
    Brock PM, Fornace KM, Grigg MJ, Anstey NM, William T, Cox J, et al.
    Proc Biol Sci, 2019 Jan 16;286(1894):20182351.
    PMID: 30963872 DOI: 10.1098/rspb.2018.2351
    The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
    Matched MeSH terms: Spatial Analysis
  11. Fulltext Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes
    Muhamad MAH, Che Hasan R, Md Said N, Ooi JL
    PLoS One, 2021;16(9):e0257761.
    PMID: 34555110 DOI: 10.1371/journal.pone.0257761
    Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.
    Matched MeSH terms: Spatial Analysis
  12. Fulltext Environmental and spatial risk factors for the larval habitats of Plasmodium knowlesi vectors in Sabah, Malaysian Borneo
    Byrne I, Aure W, Manin BO, Vythilingam I, Ferguson HM, Drakeley CJ, et al.
    Sci Rep, 2021 Jun 03;11(1):11810.
    PMID: 34083582 DOI: 10.1038/s41598-021-90893-1
    Land-use changes, such as deforestation and agriculture, can influence mosquito vector populations and malaria transmission. These land-use changes have been linked to increased incidence in human cases of the zoonotic malaria Plasmodium knowlesi in Sabah, Malaysian Borneo. This study investigates whether these associations are partially driven by fine-scale land-use changes creating more favourable aquatic breeding habitats for P. knowlesi anopheline vectors. Using aerial remote sensing data, we developed a sampling frame representative of all land use types within a major focus of P. knowlesi transmission. From 2015 to 2016 monthly longitudinal surveys of larval habitats were collected in randomly selected areas stratified by land use type. Additional remote sensing data on environmental variables, land cover and landscape configuration were assembled for the study site. Risk factor analyses were performed over multiple spatial scales to determine associations between environmental and spatial variables and anopheline larval presence. Habitat fragmentation (300 m), aspect (350 m), distance to rubber plantations (100 m) and Culex larval presence were identified as risk factors for Anopheles breeding. Additionally, models were fit to determine the presence of potential larval habitats within the areas surveyed and used to generate a time-series of monthly predictive maps. These results indicate that land-use change and topography influence the suitability of larval habitats, and may partially explain the link between P. knowlesi incidence and deforestation. The predictive maps, and identification of the spatial scales at which risk factors are most influential may aid spatio-temporally targeted vector control interventions.
    Matched MeSH terms: Spatial Analysis
  13. Combining high-resolution gross domestic product data with home and personal care product market research data to generate a subnational emission inventory for Asia
    Hodges JE, Vamshi R, Holmes C, Rowson M, Miah T, Price OR
    Integr Environ Assess Manag, 2014 Apr;10(2):237-46.
    PMID: 23913410 DOI: 10.1002/ieam.1476
    Environmental risk assessment of chemicals is reliant on good estimates of product usage information and robust exposure models. Over the past 20 to 30 years, much progress has been made with the development of exposure models that simulate the transport and distribution of chemicals in the environment. However, little progress has been made in our ability to estimate chemical emissions of home and personal care (HPC) products. In this project, we have developed an approach to estimate subnational emission inventory of chemical ingredients used in HPC products for 12 Asian countries including Bangladesh, Cambodia, China, India, Indonesia, Laos, Malaysia, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam (Asia-12). To develop this inventory, we have coupled a 1 km grid of per capita gross domestic product (GDP) estimates with market research data of HPC product sales. We explore the necessity of accounting for a population's ability to purchase HPC products in determining their subnational distribution in regions where wealth is not uniform. The implications of using high resolution data on inter- and intracountry subnational emission estimates for a range of hypothetical and actual HPC product types were explored. It was demonstrated that for low value products (<500 US$ per capita/annum required to purchase product) the maximum deviation from baseline (emission distributed via population) is less than a factor of 3 and it would not result in significant differences in chemical risk assessments. However, for other product types (>500 US$ per capita/annum required to purchase product) the implications on emissions being assigned to subnational regions can vary by several orders of magnitude. The implications of this on conducting national or regional level risk assessments may be significant. Further work is needed to explore the implications of this variability in HPC emissions to enable the HPC industry and/or governments to advance risk-based chemical management policies in emerging markets.
    Matched MeSH terms: Spatial Analysis
  14. A climate distribution model of malaria transmission in Sudan
    Musa MI, Shohaimi S, Hashim NR, Krishnarajah I
    Geospat Health, 2012 Nov;7(1):27-36.
    PMID: 23242678
    Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the Anopheles mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.
    Matched MeSH terms: Spatial Analysis
  15. Occurrence, fate and environmental risk of linear alkylbenzene sulfonate in the Langat and Selangor River basins, Malaysia
    Sakai N, Shirasaka J, Matsui Y, Ramli MR, Yoshida K, Ali Mohd M, et al.
    Chemosphere, 2017 Apr;172:234-241.
    PMID: 28081507 DOI: 10.1016/j.chemosphere.2016.12.139
    Five homologs (C10-C14) of linear alkylbenzene sulfonate (LAS) were quantitated in surface water collected in the Langat and Selangor River basins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to spatially analyze the occurrence of LAS in both river basins, and the LAS contamination associated with the population was elucidated by spatial analysis at a sub-basin level. The LAS concentrations in the dissolved phase (<0.45 μm) and 4 fractions separated by particle size (<0.1 μm, 0.1-1 μm, 1-11 μm and >11 μm) were analyzed to elucidate the environmental fate of LAS in the study area. The environmental risks of the observed LAS concentration were assessed based on predicted no effect concentration (PNEC) normalized by a quantitative structure-activity relationship model. The LAS contamination mainly occurred from a few populated sub-basins, and it was correlated with the population density and ammonia nitrogen. The dissolved phase was less than 20% in high contamination sites (>1000 μg/L), whereas it was more than 60% in less contaminated sites (<100 μg/L). The environmental fate of LAS in the study area was primarily subject to the adsorption to suspended solids rather than biodegradation because the LAS homologs, particularly in longer alkyl chain lengths, were considerably absorbed to the large size fraction (>11 μm) that settled in a few hours. The observed LAS concentrations exceeded the normalized PNEC at 3 sites, and environmental risk areas and susceptible areas to the LAS contamination were spatially identified based on their catchment areas.
    Matched MeSH terms: Spatial Analysis
  16. Fulltext Risk factors and spatial distribution of extended spectrum β-lactamase-producing- Escherichia coli at retail poultry meat markets in Malaysia: a cross-sectional study
    Aliyu AB, Saleha AA, Jalila A, Zunita Z
    BMC Public Health, 2016 08 02;16:699.
    PMID: 27484086 DOI: 10.1186/s12889-016-3377-2
    BACKGROUND: The significant role of retail poultry meat as an important exposure pathway for the acquisition and transmission of extended spectrum β-lactamase-producing Escherichia coli (ESBL-EC) into the human population warrants understanding concerning those operational practices associated with dissemination of ESBL-EC in poultry meat retailing. Hence, the objective of this study was to determine the prevalence, spatial distribution and potential risk factors associated with the dissemination of ESBL-EC in poultry meat retail at wet-markets in Selangor, Malaysia.

    METHODS: Poultry meat (breast, wing, thigh, and keel) as well as the contact surfaces of weighing scales and cutting boards were sampled to detect ESBL-EC by using culture and disk combination methods and polymerase chain reaction assays. Besides, questionnaire was used to obtain data and information pertaining to those operational practices that may possibly explain the occurrence of ESBL-EC. The data were analysed using logistic regression analysis at 95 % CI.

    RESULTS: The overall prevalence of ESBL-EC was 48.8 % (95 % CI, 42 - 55 %). Among the risk factors that were explored, type of countertop, sanitation of the stall environment, source of cleaning water, and type of cutting board were found to be significantly associated with the presence of ESBL-EC.

    CONCLUSIONS: Thus, in order to prevent or reduce the presence of ESBL-EC and other contaminants at the retail-outlet, there is a need to design a process control system based on the current prevailing practices in order to reduce cross contamination, as well as to improve food safety and consumer health.

    Matched MeSH terms: Spatial Analysis
  17. The genetic characteristics and evolution of Tembusu virus
    Lei W, Guo X, Fu S, Feng Y, Tao X, Gao X, et al.
    Vet Microbiol, 2017 Mar;201:32-41.
    PMID: 28284620 DOI: 10.1016/j.vetmic.2017.01.003
    BACKGROUND: Since the turn of the 21st century, there have been several epidemic outbreaks of poultry diseases caused by Tembusu virus (TMUV). Although multiple mosquito and poultry-derived strains of TMUV have been isolated, no data exist about their comparative study, origin, evolution, and dissemination.

    METHODOLOGY: Parallel virology was used to investigate the phenotypes of duck and mosquito-derived isolates of TMUV. Molecular biology and bioinformatics methods were employed to investigate the genetic characteristics and evolution of TMUV.

    PRINCIPAL FINDINGS: The plaque diameter of duck-derived isolates of TMUV was larger than that of mosquito-derived isolates. The cytopathic effect (CPE) in mammalian cells occurred more rapidly induced by duck-derived isolates than by mosquito-derived isolates. Furthermore, duck-derived isolates required less time to reach maximum titer, and exhibited higher viral titer. These findings suggested that poultry-derived TMUV isolates were more invasive and had greater expansion capability than the mosquito-derived isolates in mammalian cells. Variations in amino acid loci in TMUV E gene sequence revealed two mutated amino acid loci in strains isolated from Malaysia, Thailand, and Chinese mainland compared with the prototypical strain of the virus (MM1775). Furthermore, TMUV isolates from the Chinese mainland had six common variations in the E gene loci that differed from the Southeast Asian strains. Phylogenetic analysis indicated that TMUV did not exhibit a species barrier in avian species and consisted of two lineages: the Southeast Asian and the Chinese mainland lineages. Molecular traceability studies revealed that the recent common evolutionary ancestor of TMUV might have appeared before 1934 and that Malaysia, Thailand and Shandong Province of China represent the three main sources related to TMUV spread.

    CONCLUSIONS: The current broad distribution of TMUV strains in Southeast Asia and Chinese mainland exhibited longer-range diffusion and larger-scale propagation. Therefore, in addition to China, other Asian and European countries linked to Asia have used improved measures to detect and monitor TMUV related diseases to prevent epidemics in poultry.

    Matched MeSH terms: Spatial Analysis
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