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Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

Hosseinpour M, Sahebi S, Zamzuri ZH, Yahaya AS, Ismail N

Accid Anal Prev, 2018 Sep;118:277-288.
PMID: 29861069 DOI: 10.1016/j.aap.2018.05.003

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables.

Matched MeSH terms: Spatial Analysis
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
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
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
Statistical and spatial analysis for soil heavy metals over the Murray-Darling river basin in Australia

Tao H, Al-Hilali AA, Ahmed AM, Mussa ZH, Falah MW, Abed SA, et al.

Chemosphere, 2023 Mar;317:137914.
PMID: 36682637 DOI: 10.1016/j.chemosphere.2023.137914

Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.

Matched MeSH terms: Spatial Analysis
Fulltext Association between Landscape Factors and Spatial Patterns of Plasmodium knowlesi Infections in Sabah, Malaysia

Fornace KM, Abidin TR, Alexander N, Brock P, Grigg MJ, Murphy A, et al.

Emerg Infect Dis, 2016 Feb;22(2):201-8.
PMID: 26812373 DOI: 10.3201/eid2202.150656

The zoonotic malaria species Plasmodium knowlesi has become the main cause of human malaria in Malaysian Borneo. Deforestation and associated environmental and population changes have been hypothesized as main drivers of this apparent emergence. We gathered village-level data for P. knowlesi incidence for the districts of Kudat and Kota Marudu in Sabah state, Malaysia, for 2008-2012. We adjusted malaria records from routine reporting systems to reflect the diagnostic uncertainty of microscopy for P. knowlesi. We also developed negative binomial spatial autoregressive models to assess potential associations between P. knowlesi incidence and environmental variables derived from satellite-based remote-sensing data. Marked spatial heterogeneity in P. knowlesi incidence was observed, and village-level numbers of P. knowlesi cases were positively associated with forest cover and historical forest loss in surrounding areas. These results suggest the likelihood that deforestation and associated environmental changes are key drivers in P. knowlesi transmission in these areas.

Matched MeSH terms: Spatial Analysis*
Investigating the spatial distribution of phototrophic picoplankton in a tropical estuary

Lee CW, Lim JH, Heng PL

Environ Monit Assess, 2013 Dec;185(12):9697-704.
PMID: 23748919 DOI: 10.1007/s10661-013-3283-3

We sampled extensively (29 stations) at the Klang estuarine system over a 3-day scientific expedition. We measured physical and chemical variables (temperature, salinity, dissolved oxygen, total suspended solids, dissolved inorganic nutrients) and related them to the spatial distribution of phototrophic picoplankton (Ppico). Multivariate analysis of variance of the physicochemical variables showed the heterogeneity of the Klang estuarine system where the stations at each transect were significantly different (Rao's F₁₈, ₃₆ = 8.401, p < 0.001). Correlation analyses also showed that variables related to Ppico abundance and growth were mutually exclusive. Distribution of Ppico was best explained by the physical mixing between freshwater and seawater whereas Ppico growth was correlated with temperature.

Matched MeSH terms: Spatial Analysis
Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Alyousifi Y, Ibrahim K, Kang W, Zin WZW

Environ Monit Assess, 2020 Oct 21;192(11):719.
PMID: 33083907 DOI: 10.1007/s10661-020-08666-8

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

Matched MeSH terms: Spatial Analysis
Spatial and statistical trend characteristics of rainfall erosivity (R) in upper catchment of Baram River, Borneo

Vijith H, Dodge-Wan D

Environ Monit Assess, 2019 Jul 13;191(8):494.
PMID: 31302794 DOI: 10.1007/s10661-019-7604-z

The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha-1 h-1 year-1, respectively. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha-1 h-1 year-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha-1h-1year-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha-1 h-1 year-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha-1 h-1 year-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha-1 h-1 year-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha-1 h-1 year-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha-1 h-1 year-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.

Matched MeSH terms: Spatial Analysis
Spatial and temporal risk quotient based river assessment for water resources management

Wan Mohtar WHM, Abdul Maulud KN, Muhammad NS, Sharil S, Yaseen ZM

Environ Pollut, 2019 May;248:133-144.
PMID: 30784832 DOI: 10.1016/j.envpol.2019.02.011

Malaysia depends heavily on rivers as a source for water supply, irrigation, and sustaining the livelihood of local communities. The evolution of land use in urban areas due to rapid development and the continuous problem of illegal discharge have had a serious adverse impact on the health of the country's waterways. Klang River requires extensive rehabilitation and remediation before its water could be utilised for a variety of purposes. A reliable and rigorous remediation work plan is needed to identify the sources and locations of streams that are constantly polluted. This study attempts to investigate the feasibility of utilising a temporal and spatial risk quotient (RQ) based analysis to make an accurate assessment of the current condition of the tributaries in the Klang River catchment area. The study relies on existing data sets on Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), and Ammonia (NH3) to evaluate the water quality at thirty strategic locations. Analysis of ammonia pollution is not only based on the limit established for river health but was expanded to include the feasibility of using the water for water intake, recreational activities, and sustaining fish population. The temporal health of Klang River was evaluated using the Risk Matrix Approach (RMA) based on the frequency of RQ > 1 and associated colour-coded hazard impacts. By using the developed RMA, the hazard level for each parameter at each location was assessed and individually mapped using Geographic Information System (GIS). The developed risk hazard mapping has high potential as one of the essential tools in making decisions for a cost-effective river restoration and rehabilitation.

Matched MeSH terms: Spatial Analysis
Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique

S C, M V P, S V, M N, K P, Panda B, et al.

Environ Res, 2022 03;204(Pt A):111729.
PMID: 34478727 DOI: 10.1016/j.envres.2021.111729

This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.

Matched MeSH terms: Spatial Analysis
Fulltext The spillover of tourism development on CO2 emissions: a spatial econometric analysis

Jiaqi Y, Yang S, Ziqi Y, Tingting L, Teo BSX

Environ Sci Pollut Res Int, 2022 Apr;29(18):26759-26774.
PMID: 34859343 DOI: 10.1007/s11356-021-17026-z

Climate change and tourism's interaction and vulnerability have been among the most hotly debated topics recently. In this context, the study focuses on how CO2 emissions, the primary cause of global warming and climate change, respond to changes in tourism development. In order to do so, the impact of tourism development on CO2 emissions in the most visited countries is investigated. A panel data from 2000 to 2017 for top 70 tourist countries are analysed using a spatial econometric method to investigate the spatial effect of tourism on environmental pollution. The direct, indirect, and overall impact of tourism on CO2 emissions are estimated using the most appropriate generalized nested spatial econometric (GNS) method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1% level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and CO2 emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness, and economic growth significantly influence environmental pollution. In addition, education expenditure and infrastructure play a significant moderating role among tourism and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and CO2 emissions and indicate that while a country's emissions initially rise with the tourism industry's growth, it begins declining after a limit.

Matched MeSH terms: Spatial Analysis
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
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
Fulltext Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia

Abd Majid N, Rainis R, Sahani M, Mohamed AF, Abdul Ghani Aziz SA, Muhamad Nazi N

Geospat Health, 2021 03 11;16(1).
PMID: 33706498 DOI: 10.4081/gh.2021.915

In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.

Matched MeSH terms: Spatial Analysis
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
Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia

Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS

Geospat Health, 2018 05 07;13(1):613.
PMID: 29772882 DOI: 10.4081/gh.2018.613

This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial accessibility to health care services among children with cerebral palsy in Johor, Peninsular Malaysia

Abu Bakar MA, Samat N, Yaacob NS

Geospat Health, 2021 10 19;16(2).
PMID: 34672180 DOI: 10.4081/gh.2021.987

Cerebral palsy (CP) is one of the most common causes of disability in childhood, leading to functional limitations and poor nutritional status. Families with CP children face challenges in providing proper care. Thus, accessibility of CP patients to health facilities is important to ensure that they can maintain regular visits to health facilities for proper treatment and care. The current study aimed to map the spatial distribution of CP in Johor, Malaysia and measure the accessibility of CP patients to nearby hospitals, health clinics and community-based rehabilitation centres. The study is based on CP cases in 2017 obtained from the Department of Social Welfare, Malaysia and analysed using the average nearest neighbour, buffer analysis and Kernel Density Estimation. Results indicate that there is generally good access to health care services for many of the CP children in Johor, but for 25% of those living more than 10 km away from the health clinics or community-based rehabilitation centres, regular visits can be a problem. This information should be used for targeted intervention and planning for health care strategies. Furthermore, information on hospital accessibility of CP children would allow for planning of proper and regular treatment for these patients. The study has shown that it is possible to improve the understanding of the distribution of CP cases by integrating spatial analysis using geographical information systems without relying on official information about the density of populations.

Matched MeSH terms: Spatial Analysis
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
Fulltext Spatial clustering of colorectal cancer in Malaysia

Syed Soffian SS, Mohammed Nawi A, Hod R, Abdul Maulud KN, Mohd Azmi AT, Hasim Hashim MH, et al.

Geospat Health, 2023 May 25;18(1).
PMID: 37246545 DOI: 10.4081/gh.2023.1158

INTRODUCTION: The rise in colorectal cancer (CRC) incidence becomes a global concern. As geographical variations in the CRC incidence suggests the role of area-level determinants, the current study was designed to identify the spatial distribution pattern of CRC at the neighbourhood level in Malaysia.
METHOD: Newly diagnosed CRC cases between 2010 and 2016 in Malaysia were identified from the National Cancer Registry. Residential addresses were geocoded. Clustering analysis was subsequently performed to examine the spatial dependence between CRC cases. Differences in socio-demographic characteristics of individuals between the clusters were also compared. Identified clusters were categorized into urban and semi-rural areas based on the population background.
RESULT: Most of the 18 405 individuals included in the study were male (56%), aged between 60 and 69 years (30.3%) and only presented for care at stages 3 or 4 of the disease (71.3%). The states shown to have CRC clusters were Kedah, Penang, Perak, Selangor, Kuala Lumpur, Melaka, Johor, Kelantan, and Sarawak. The spatial autocorrelation detected a significant clustering pattern (Moran's Index 0.244, p< 0.01, Z score >2.58). CRC clusters in Penang, Selangor, Kuala Lumpur, Melaka, Johor, and Sarawak were in urbanized areas, while those in Kedah, Perak and Kelantan were in semi-rural areas.
CONCLUSION: The presence of several clusters in urbanized and semi-rural areas implied the role of ecological determinants at the neighbourhood level in Malaysia. Such findings could be used to guide the policymakers in resource allocation and cancer control.

Matched MeSH terms: Spatial Analysis

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