Enhancing road traffic safety: application of geographical information system (gis) analysis to identify spatial & terporal risk factors for severe road traffic injury within Kota Bharu district - Rahman, Nik Hisamuddin Nik Ab (Universiti Sains Malaysia, 2015)
Road traffic injuries (RTI) is a very common cause of admission to the hospital worldwide, in particular in the developing countries (Hyder, 2014). It has been a long and agonizing disease that contributes to major cause of loss to life, long term suffering, disablement and psychological sequelae to both the victims & carer. However in particular, in Malaysia it is well known that there is lack of research into RTI epidemiology. The primary aim of this study is to document the demographic parameters, the predominant injury mechanisms and severity, geographical positioning data (i.e coordinates of the incidents locations), spatial data, mortality, length of hospital stay and finally the clinical outcome. The overall output is integrated spatial-temporal, pre-hospital and clinical data. We prospectively & retrospectively identified all injured subjects who had been referred to our department after sustaining RTIs within the district of Kota Bharu. We will also obtain the data from Hospital Raja Perempuan Zainab 2 Kota Bharu (MOH) & the police POL27 (Accident computerized form) form. All patients must be diagnosed with road related injuries. For this study, we extracted age, gender, accident mechanisms and causes, and vehicle types from the police & hospital management and outcome. A set of digital maps will be obtained from the Town Planning Unit of Kota Bharu Municipal Office (local district map). Vector spaces were spanned over these maps using GIS software (ARCGIS 10.1 licensed to USM), and data from the identified trauma cases were added. Spatial analysis and overlay tools were used to identify local clusters of events. The data collected using manual data form will be transferred into the SPSS version 22.0 software produced by IBM and licensed to the USM. The data form comprises of several sections such as general demography, injury data, prehospital care, ED management, outcome and geographical data. The variables will be in both categorical and numerical data. The data will be analyzed by variety of methods ranging from descriptive analysis, univariate analysis and multivariate analysis. A total of 439 cases were recruited over the ten month data collection period commencing August 2012 till May 2013. The data showed that motorcycle contributed most (82%) to the RTI victims. Most of the RTI cases occurred along hotspot areas within certain Mukim/Borough within the Kota Bharu District namely Kenali, Demit and Binjai areas. The factors associated with the duration of stay and disability outcome include injury severity score (ISS) and being operated in the hospital. The most common areas of occurrence of RTI include staright road, at non peak hours, in the evening and in the suburban areas. The RTI cases within the Kota Bharu district follow the genaral pattern of RTI cases in other parts of Malaysia. The identification of general demographic and geographical pattern of the RTI will assist the policy maker in implementing the preventive program for road safety in future. The research involved 2 parts: the GIS analysis for RTI hotspot for the vulnerable road users and th, analysis for associated factors for disability and the prolonged hospital stay after the RTI. Overall o' and half year period of data collection showed that majority of vulnerable road users involved in R1 among the motorcyclists. The pillion and main riders are equally involved in the RTI. Most of the ir involved the average age of 44 years and among the productive working group. The helmet wearing among the common geographical features where the RTI occurred are straight road, within suburbai area with speed limit of 60km/hr. The buffer analysis within 100 meters of the acccident showed th< common build up surrounding involved include the shop lots, restaurants/cafe and villages. The geo parameters and the clinical parameters were combined and the multiple logistic regression analysis performed searching for the association factors for the disabhty and prolonged hospital stay outcom analysis showed that the injury severity score is the main predictive factors for both outcome. None geographical factors are strong enough to predict the two outcomes.