Displaying publications 121 - 140 of 433 in total

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  1. Assessment of the impact of petroleum and petrochemical industries to the surrounding areas in Malaysia using mosses as bioindicator supported by multivariate analysis
    Abdullah MZ, Saat AB, Hamzah ZB
    Environ Monit Assess, 2012 Jun;184(6):3959-69.
    PMID: 21822578 DOI: 10.1007/s10661-011-2236-y
    Biomonitoring of multi-element atmospheric deposition using terrestrial moss is a well-established technique in Europe. Although the technique is widely known, there were very limited records of using this technique to study atmospheric air pollution in Malaysia. In this present study, the deposition of 11 trace metals surrounding the main petroleum refinery plant in Kerteh Terengganu (eastern part of peninsular Malaysia) has been evaluated using two local moss species, namely Hypnum plumaeforme and Taxithelium instratum as bioindicators. The study was also done by means of observing whether these metals are attributed to work related to oil exploration in this area. The moss samples have been collected at 30 sampling stations in the vicinity of the petrochemical industrial area covering up to 15 km to the south, north, and west in radius. The contents of heavy metal in moss samples were analyzed by energy dispersive x-ray fluorescence technique. Distribution of heavy metal content in all mosses is portrayed using Surfer software. Areas of the highest level of contaminations are highlighted. The results obtained using the principal components analysis revealed that the elements can be grouped into three different components that indirectly reflected three different sources namely anthropogenic factor, vegetation factor, and natural sources (soil dust or substrate) factor. Heavy metals deposited mostly in the distance after 9 km onward to the western part (the average direction of wind blow). V, Cr, Cu, and Hg are believed to have originated from local petrochemical-based industries operated around petroleum industrial area.
    Matched MeSH terms: Multivariate Analysis
  2. Similarities and differences of metal distributions in the tissues of molluscs by using multivariate analyses
    Yap CK, Edward FB, Tan SG
    Environ Monit Assess, 2010 Jun;165(1-4):39-53.
    PMID: 19452255 DOI: 10.1007/s10661-009-0925-6
    Multivariate analysis including correlation, multiple stepwise linear regression, and cluster analyses were applied to investigate the heavy metal concentrations (Cd, Cu, Fe, Ni, Pb, and Zn) in the different parts of bivalves and gastropods. It was also aimed to distinguish statistically the differences between the marine bivalves and the gastropods with regards to the accumulation of heavy metals in the different tissues. The different parts of four species of bivalves and four species of gastropods were obtained and analyzed for heavy metals. The multivariate analyses were then applied on the data. From the multivariate analyses conducted, there were correlations found between the soft tissues of bivalves and gastropods, but none was found between the shells and the soft tissues of most of the molluscs (except for Cerithidea obtusa and Puglina cochlidium). The significant correlations (P < 0.05) found between the soft tissues were further complemented by the multiple stepwise linear regressions where heavy metals in the total soft tissues were influenced by the accumulation in the different types of soft tissues. The present study found that the distributions of heavy metals in the different parts of molluscs were related to their feeding habits and living habitats. The statistical approaches proposed in this study are recommended for use in biomonitoring studies, since multivariate analyses can reduce the cost and time involved in identifying an effective tissue to monitor the heavy metal(s) bioavailability and contamination in tropical coastal waters.
    Matched MeSH terms: Multivariate Analysis
  3. Multivariate analysis of heavy metals concentrations in river estuary
    Alkarkhi AF, Ahmad A, Ismail N, Easa AM
    Environ Monit Assess, 2008 Aug;143(1-3):179-86.
    PMID: 17899414
    Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.
    Matched MeSH terms: Multivariate Analysis
  4. Determination of volatile organic compounds pollution sources in malaysian drinking water using multivariate analysis
    Soh SC, Abdullah MP
    Environ Monit Assess, 2007 Jan;124(1-3):39-50.
    PMID: 16967208
    A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 microm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 microg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 microg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.
    Matched MeSH terms: Multivariate Analysis
  5. Multivariate analysis for source identification of pollution in sediment of Linggi River, Malaysia
    Elias MS, Ibrahim S, Samuding K, Rahman SA, Wo YM, Daung JAD
    Environ Monit Assess, 2018 Mar 29;190(4):257.
    PMID: 29600468 DOI: 10.1007/s10661-018-6632-4
    Rapid socioeconomic development in the Linggi River Basin has contributed to the significant increase of pollution discharge into the Linggi River and its adjacent coastal areas. The toxic element contents and distributions in the sediment samples collected along the Linggi River were determined using neutron activation analysis (NAA) and inductively coupled plasma-mass spectrometry (ICP-MS) techniques. The measured mean concentration of As, Cd, Pb, Sb, U, Th and Zn is relatively higher compared to the continental crust value of the respective element. Most of the elements (As, Cr, Fe, Pb, Sb and Zn) exceeded the freshwater sediment quality guideline-threshold effect concentration (FSQG-TEC) value. Downstream stations of the Linggi River showed that As concentrations in sediment exceeded the freshwater sediment quality guideline-probable effect concentration (FSQG-PEC) value. This indicates that the concentration of As will give an adverse effect to the growth of sediment-dwelling organisms. Generally, the Linggi River sediment can be categorised as unpolluted to strongly polluted and unpolluted to strongly to extremely polluted. The correlation matrix of metal-metal relationship, principle component analysis (PCA) and cluster analysis (CA) indicates that the pollution sources of Cu, Ni, Zn, Cd and Pb in sediments of the Linggi River originated from the industry of electronics and electroplating. Elements of As, Cr, Sb and Fe mainly originated from motor-vehicle workshops and metal work, whilst U and Th originated from natural processes such as terrestrial runoff and land erosion.
    Matched MeSH terms: Multivariate Analysis
  6. Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models
    Rizeei HM, Azeez OS, Pradhan B, Khamees HH
    Environ Monit Assess, 2018 Oct 04;190(11):633.
    PMID: 30288624 DOI: 10.1007/s10661-018-7013-8
    Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.
    Matched MeSH terms: Multivariate Analysis
  7. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS
    Golkarian A, Naghibi SA, Kalantar B, Pradhan B
    Environ Monit Assess, 2018 Feb 17;190(3):149.
    PMID: 29455381 DOI: 10.1007/s10661-018-6507-8
    Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
    Matched MeSH terms: Multivariate Analysis
  8. Regression and multivariate models for predicting particulate matter concentration level
    Nazif A, Mohammed NI, Malakahmad A, Abualqumboz MS
    Environ Sci Pollut Res Int, 2018 Jan;25(1):283-289.
    PMID: 29032528 DOI: 10.1007/s11356-017-0407-2
    The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.
    Matched MeSH terms: Multivariate Analysis
  9. An overview of groundwater chemistry studies in Malaysia
    Kura NU, Ramli MF, Sulaiman WNA, Ibrahim S, Aris AZ
    Environ Sci Pollut Res Int, 2018 Mar;25(8):7231-7249.
    PMID: 26686857 DOI: 10.1007/s11356-015-5957-6
    In this paper, numerous studies on groundwater in Malaysia were reviewed with the aim of evaluating past trends and the current status for discerning the sustainability of the water resources in the country. It was found that most of the previous groundwater studies (44 %) focused on the islands and mostly concentrated on qualitative assessment with more emphasis being placed on seawater intrusion studies. This was then followed by inland-based studies, with Selangor state leading the studies which reflected the current water challenges facing the state. From a methodological perspective, geophysics, graphical methods, and statistical analysis are the dominant techniques (38, 25, and 25 %) respectively. The geophysical methods especially the 2D resistivity method cut across many subjects such as seawater intrusion studies, quantitative assessment, and hydraulic parameters estimation. The statistical techniques used include multivariate statistical analysis techniques and ANOVA among others, most of which are quality related studies using major ions, in situ parameters, and heavy metals. Conversely, numerical techniques like MODFLOW were somewhat less admired which is likely due to their complexity in nature and high data demand. This work will facilitate researchers in identifying the specific areas which need improvement and focus, while, at the same time, provide policymakers and managers with an executive summary and knowledge of the current situation in groundwater studies and where more work needs to be done for sustainable development.
    Matched MeSH terms: Multivariate Analysis
  10. Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach
    Gul S, Zou X, Hassan CH, Azam M, Zaman K
    Environ Sci Pollut Res Int, 2015 Dec;22(24):19773-85.
    PMID: 26282441 DOI: 10.1007/s11356-015-5185-0
    This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.
    Matched MeSH terms: Multivariate Analysis
  11. Renal impairment and all-cause mortality in cardiovascular disease: effect modification by type 2 diabetes mellitus
    Selvarajah S, Uiterwaal CS, Haniff J, van der Graaf Y, Visseren FL, Bots ML, et al.
    Eur J Clin Invest, 2013 Feb;43(2):198-207.
    PMID: 23301500 DOI: 10.1111/eci.12035
    BACKGROUND:
    Renal impairment and type 2 diabetes mellitus (DM) are well-known independent risk factors for mortality. The evidence of their combined effects on mortality is unclear, but of importance because it may determine aggressiveness of treatment. This study sought to assess and quantify the effect modification of diabetes on renal impairment in its association with mortality.

    MATERIALS AND METHODS:
    Patients with cardiovascular disease or at high risk, recruited in the Second Manifestations of ARTerial disease cohort study, were selected. A total of 7135 patients were enrolled with 33 198 person-years of follow-up. Renal impairment was defined by albuminuria status and estimated glomerular filtration rate (eGFR). Outcome was all-cause mortality.

    RESULTS:
    Mortality increased progressively with each stage of renal impairment, for both albuminuria status and eGFR, for diabetics and non-diabetics. There was no effect modification by diabetes on mortality risk due to renal impairment. The relative excess risk due to interaction (RERI) for DM and microalbuminuria was 0·21 (-0·11, 0·52), for overt proteinuria -1·12 (-2·83, 0·59) and for end-stage renal failure (ESRF) 0·32 (-3·65, 4·29). The RERI for DM with eGFR of 60-89 mL/min/1·73 m(2) was -0·31(-0·92, 0·32), for eGFR of 30-59 mL/min/1·73 m(2) -0·07 (-0·76, 0·62) and for eGFR of < 30 mL/min/1·73 m(2) 0·38 (-0·85, 1·61).

    CONCLUSIONS:
    Type 2 diabetes mellitus does not modify nor increase the risk relation between all-cause mortality and renal impairment. These findings suggest that the hallmark for survival is the prevention and delay in progression of renal impairment in patients with cardiovascular disease.
    Matched MeSH terms: Multivariate Analysis
  12. Raised levels of Il-6, Il-17a, and Il-22 in fatal leptospirosis
    Wan Yusoff WSY, Abdullah M, Sekawi Z, Amran F, Yuhana MY, Mohd Taib N, et al.
    Eur J Clin Microbiol Infect Dis, 2019 Dec;38(12):2349-2353.
    PMID: 31529307 DOI: 10.1007/s10096-019-03699-5
    Clinical manifestations of leptospirosis range from mild, common cold-like illness, to a life-threatening condition. The host immune response has been hypothesized to play a major role in leptospirosis outcome. Increased levels of inflammatory mediators, such as cytokines, may promote tissue damage that lead to increased disease severity. The question is whether cytokines levels may predict the outcome of leptospirosis and guide patient management. This study aimed to assess the association between Th1-, Th2-, and Th17-related cytokines with the clinical outcome of patients with leptospirosis. Different cytokine levels were measured in fifty-two plasma samples of hospitalized patients diagnosed with leptospirosis in Malaysia (January 2016-December 2017). Patients were divided into two separate categories: survived (n = 40) and fatal outcome (n = 12). Nineteen plasma samples from healthy individuals were obtained as controls. Cytokine quantification was performed using Simple Plex™ assays from ProteinSimple (San Jose, CA, USA). Measurements were done in triplicate and statistical analysis was performed using GraphPad software and SPSS v20. IL-6 (p = 0.033), IL-17A (p = 0.022), and IL-22 (p = 0.046) were significantly elevated in fatal cases. IL-17A concentration (OR 1.115; 95% CI 1.010-1.231) appeared to be an independent predictor of fatality of leptospirosis. Significantly higher levels of TNF-α (p ≤ 0.0001), IL-6 (p ≤ 0.0001), IL-10 (p ≤ 0.0001), IL-12 (p ≤ 0.0001), IL17A (p ≤ 0.0001), and IL-18 (p ≤ 0.0001) were observed among leptospirosis patients in comparison with healthy controls. Our study shows that certain cytokine levels may serve as possible prognostic biomarkers in leptospirosis patients.
    Matched MeSH terms: Multivariate Analysis
  13. The relationship between dental anxiety and dental decay experience in antenatal mothers
    Esa R, Savithri V, Humphris G, Freeman R
    Eur J Oral Sci, 2010 Feb;118(1):59-65.
    PMID: 20156266 DOI: 10.1111/j.1600-0722.2009.00701.x
    The aim of this study was to investigate the relationship between dental anxiety and dental decay experience among antenatal mothers attending Maternal and Child Health clinics in Malaysia. A cross-sectional study was conducted on a consecutive sample of 407 antenatal mothers in Seremban, Malaysia. The questionnaire consisted of participants' demographic profile and the Dental Fear Survey. The D(3cv)MFS was employed as the outcome measure and was assessed by a single examiner (intraclass correlation = 0.98). A structural equation model was designed to inspect the relationship between dental anxiety and dental decay experience. The mean Dental Fear Survey score for all participants was 35.1 [95% confidence interval (34.0, 36.3)]. The mean D(3cv)MFS score was 10.8 [95% confidence interval (9.5, 12.1)]. Participants from low socio-economic status groups had significantly higher D(3cv)MFS counts than those from high socio-economic status groups. The path model with dental anxiety and socio-economic status as predictors of D(3cv)MFS showed satisfactory fit. The correlation between dental anxiety and dental decay experience was 0.30 (standardized estimate), indicating a positive association. Socio-economic status was also statistically significantly associated with the D(3cv)MFS count (beta = 0.19). This study presented robust evidence for the significant relationship between dental anxiety and dental decay experience in antenatal mothers.
    Matched MeSH terms: Multivariate Analysis
  14. The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum
    Goh CF, Craig DQ, Hadgraft J, Lane ME
    Eur J Pharm Biopharm, 2017 Feb;111:16-25.
    PMID: 27845181 DOI: 10.1016/j.ejpb.2016.10.025
    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm(-1)) containing the carboxylate (COO(-)) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO(-) asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin.
    Matched MeSH terms: Multivariate Analysis
  15. Clustering of cardiovascular risk factors in a middle-income country: a call for urgency
    Selvarajah S, Haniff J, Kaur G, Hiong TG, Cheong KC, Lim CM, et al.
    Eur J Prev Cardiol, 2013 Apr;20(2):368-75.
    PMID: 22345688 DOI: 10.1177/2047487312437327
    BACKGROUND: This study aimed to estimate the prevalence of cardiovascular risk factors and its clustering. The findings are to help shape the Malaysian future healthcare planning for cardiovascular disease prevention and management.
    METHODS: Data from a nationally representative cross-sectional survey was used. The survey was conducted via a face-to-face interview using a standardised questionnaire. A total of 37,906 eligible participants aged 18 years and older was identified, of whom 34,505 (91%) participated. Focus was on hypertension, hyperglycaemia (diabetes and impaired fasting glucose), hypercholesterolaemia and central obesity.
    RESULTS: Overall, 63% (95% confidence limits 62, 65%) of the participants had at least one cardiovascular risk factor, 33% (32, 35%) had two or more and 14% (12, 15%) had three risk factors or more. The prevalence of hypertension, hyperglycaemia, hypercholesterolaemia and central obesity were 38%, 15%, 24% and 37%, respectively. Women were more likely to have a higher number of cardiovascular risk factors for most age groups; adjusted odds ratios ranging from 1.1 (0.91, 1.32) to 1.26 (1.12, 1.43) for the presence of one risk factor and 1.07 (0.91, 1.32) to 2.00 (1.78, 2.25) for two or more risk factors.
    CONCLUSIONS: Cardiovascular risk-factor clustering provides a clear impression of the true burden of cardiovascular disease risk in the population. Women displayed higher prevalence and a younger age shift in clustering was seen. These findings signal the presence of a cardiovascular epidemic in an upcoming middle-income country and provide evidence that drastic measures have to be taken to safeguard the health of the nation.
    Study name: National Health and Morbidity Survey (NHMS-2006)
    Matched MeSH terms: Multivariate Analysis
  16. Lipid-lowering treatment in hypercholesterolaemic patients: the CEPHEUS Pan-Asian survey
    Park JE, Chiang CE, Munawar M, Pham GK, Sukonthasarn A, Aquino AR, et al.
    Eur J Prev Cardiol, 2012 Aug;19(4):781-94.
    PMID: 21450606 DOI: 10.1177/1741826710397100
    BACKGROUND: Treatment of hypercholesterolaemia in Asia is rarely evaluated on a large scale, and data on treatment outcome are scarce. The Pan-Asian CEPHEUS study aimed to assess low-density lipoprotein cholesterol (LDL-C) goal attainment among patients on lipid-lowering therapy.
    METHODS: This survey was conducted in eight Asian countries. Hypercholesterolaemic patients aged ≥18 years who had been on lipid-lowering treatment for ≥3 months (stable medication for ≥6 weeks) were recruited, and lipid concentrations were measured. Demographic and other clinically relevant information were collected, and the cardiovascular risk of each patient was determined. Definitions and criteria set by the updated 2004 National Cholesterol Education Program guidelines were applied.
    RESULTS: In this survey, 501 physicians enrolled 8064 patients, of whom 7281 were included in the final analysis. The mean age was 61.0 years, 44.4% were female, and 85.1% were on statin monotherapy. LDL-C goal attainment was reported in 49.1% of patients overall, including 51.2% of primary and 48.7% of secondary prevention patients, and 36.6% of patients with familial hypercholesterolaemia. The LDL-C goal was attained in 75.4% of moderate risk, 55.4% of high risk, and only 34.9% of very high-risk patients. Goal attainment was directly related to age and inversely related to cardiovascular risk and baseline LDL-C.
    CONCLUSION: A large proportion of Asian hypercholesterolaemic patients on lipid-lowering drugs are not at recommended LDL-C levels and remain at risk for cardiovascular disease. Given the proven efficacy of lipid-lowering drugs in the reduction of LDL-C, there is room for further optimization of treatments to maximize benefits and improve outcomes.
    Matched MeSH terms: Multivariate Analysis
  17. Fulltext Low nocturnal diastolic ocular perfusion pressure as a risk factor for NTG progression: a 5-year prospective study
    Raman P, Suliman NB, Zahari M, Kook M, Ramli N
    Eye (Lond), 2018 07;32(7):1183-1189.
    PMID: 29491486 DOI: 10.1038/s41433-018-0057-8
    OBJECTIVE: To assess the relationship between baseline intraocular pressure (IOP), blood pressure (BP) and ocular perfusion pressure (OPP), and the 5-year visual field progression in normal-tension glaucoma (NTG) patients.

    DESIGN: Prospective, longitudinal study.

    METHODS: Sixty-five NTG patients who were followed up for 5 years are included in this study. All the enrolled patients underwent baseline 24-h IOP and BP monitoring via 2-hourly measurements in their habitual position and were followed up for over 5 years with reliable VF tests. Modified Anderson criteria were used to assess VF progression. Univariable and multivariable analyses using Cox's proportional hazards model were used to identify the systemic and clinical risk factors that predict progression. Kaplan-Meier survival analyses were used to compare the time elapsed to confirmed VF progression in the presence or absence of each potential risk factor.

    RESULTS: At 5-year follow-up, 35.4% of the enrolled patients demonstrated visual field progression. There were statistically significant differences in the mean diastolic blood pressure (p  43.7 mmHg (log rank = 0.018).

    CONCLUSION: Diastolic parameters of BP and OPP were significantly lower in the NTG patients who progressed after 5 years. Low nocturnal DOPP is an independent predictor of glaucomatous visual field progression in NTG patients.

    Matched MeSH terms: Multivariate Analysis
  18. Perceived involvement and preferences in shared decision-making among patients with hypertension
    Mah HC, Muthupalaniappen L, Chong WW
    Fam Pract, 2016 06;33(3):296-301.
    PMID: 26993483 DOI: 10.1093/fampra/cmw012
    BACKGROUND: Shared decision-making (SDM) is an important component of patient-centred care. However, there is limited information on its implementation in Malaysia, particularly in chronic diseases such as hypertension.

    OBJECTIVE: The objective of this study was to examine perceived involvement and role preferences of patients with hypertension in treatment decision-making.

    METHODS: A cross-sectional survey was conducted among 210 patients with hypertension in a teaching hospital in Malaysia.

    RESULTS: The majority of respondents agreed that their doctor recognized that a decision needs to be made (89.5%) and informed them that different options are available (77.1%). However, respondents' perceived level of involvement in other aspects of treatment decision-making process was low, including in the selection of treatment and in reaching an agreement with their doctor on how to proceed with treatment. In terms of preferred decision-making roles, 51.4% of respondents preferred a collaborative role with their physicians, 44.8% preferred a passive role while only 1.9% preferred an active role. Age and educational level were found to be significantly related to patient preferences for involvement in SDM. Younger patients (<60 years) and those with higher educational level preferred SDM over passive decision-making (ρ < 0.01). Encouragement from health care providers was perceived as a major motivating factor for SDM among patients with hypertension, with 91% of respondents agreeing that this would motivate their participation in SDM.

    CONCLUSION: Preferences for involvement in decision-making among patients with hypertension are varied, and influenced by age and educational level. Physicians have a key role in encouraging patients to participate in SDM.

    Matched MeSH terms: Multivariate Analysis
  19. Chemometric approach to the optimization of HS-SPME/GC-MS for the determination of multiclass pesticide residues in fruits and vegetables
    Abdulra'uf LB, Tan GH
    Food Chem, 2015 Jun 15;177:267-73.
    PMID: 25660885 DOI: 10.1016/j.foodchem.2015.01.031
    An HS-SPME method was developed using multivariate experimental designs, which was conducted in two stages. The significance of each factor was estimated using the Plackett-Burman (P-B) design, for the identification of significant factors, followed by the optimization of the significant factors using central composite design (CCD). The multivariate experiment involved the use of Minitab® statistical software for the generation of a 2(7-4) P-B design and CCD matrices. The method performance evaluated with internal standard calibration method produced good analytical figures of merit with linearity ranging from 1 to 500 μg/kg with correlation coefficient greater than 0.99, LOD and LOQ were found between 0.35 and 8.33 μg/kg and 1.15 and 27.76 μg/kg respectively. The average recovery was between 73% and 118% with relative standard deviation (RSD=1.5-14%) for all the investigated pesticides. The multivariate method helps to reduce optimization time and improve analytical throughput.
    Matched MeSH terms: Multivariate Analysis
  20. Multivariate statistical analysis treatment of DSC thermal properties for animal fat adulteration
    Dahimi O, Rahim AA, Abdulkarim SM, Hassan MS, Hashari SB, Mashitoh AS, et al.
    Food Chem, 2014 Sep 1;158:132-8.
    PMID: 24731324 DOI: 10.1016/j.foodchem.2014.02.087
    The adulteration of edible fats is a kind of fraud that impairs the physical and chemical features of the original lipid materials. It has been detected in various food, pharmaceutical and cosmeceutical products. Differential scanning calorimetry (DSC) is the robust thermo-analytical machine that permits to fingerprint the primary crystallisation of triacylglycerols (TAGs) molecules and their transition behaviours. The aims of this study was to assess the cross-contamination caused by lard concentration of 0.5-5% in the mixture systems containing beef tallow (BT) and chicken fat (CF) separately. TAGs species of pure and adulterated lipids in relation to their crystallisation and melting parameters were studied using principal components analysis (PCA). The results showed that by using the heating profiles the discrimination of LD from BT and CF was very clear even at low dose of less than 1%. Same observation was depicted from the crystallisation profiles of BT adulterated by LD doses ranging from 0.1% to 1% and from 2% to 5%, respectively. Furthermore, CF adulterated with LD did not exhibit clear changes on its crystallisation profiles. Consequently, DSC coupled with PCA is one of the techniques that might use to monitor and differentiate the minimum adulteration levels caused by LD in different animal fats.
    Matched MeSH terms: Multivariate Analysis
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