Application of geographical information system (GIS) and logistic regression analysis to investigate spatial, temporal and clinical risk factors for road traffic injury (RTI) within Kota Bharu district

• Main Author: Rahman, Nik Hisamuddin Nik Ab.
• Format: Thesis
• Language: English
• Published: 2017
• Subjects: RA0421 Public health. Hygiene. Preventive Medicine; RA440-440.87 Study and teaching. Research
• Online Access: http://eprints.usm.my/43154/

This was a Prospective Cohort Study commencing from July 2011 until June 2013 involving all injuries related to motor vehicle crashes (MVC) attended Emergency Department (ED), Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab 2 (HRPZ 2) Kota Bharu Kelantan. Selected attributes were geospatially analysed by using ARCGIS (by ESRI) software version 10.1 licensed to the USM and Google Map free software and multiple logistic regression was performed by using SPSS version 22.0. A total of 439 cases were recruited. The mean age (SD) of the MVC victims was 26.04 years (s.d 15.26). Male comprised of 302 (71.7%) of the cases. Motorcyclists were the commonest type of victims involved 351(80.0%). Hotspot MVC locations occurred at certain intersections and on roads within Mukim Kenali and Binjai. The number of severely injured and polytrauma are most on the road network within speed limit of 60 km/hour. A person with an increase in ISS of one score had a 37 % higher odd to have disability at hospital discharge (95% CI: 1.253, 1.499, p-value < 0.001). Pediatric age group (less than 19 years of age) had 52.1% lesser odds to have disability at discharge from hospital (95% CI: 0.258, 0.889, p-value < 0.001) and patients who underwent operation for definitive management had 4.14 times odds to have disability at discharge from hospital (95% CI: 1.681, 10.218, p-value = 0.002). An increase in ISS of one score had a 50 % higher the odds to be admitted to hospital (95% CI: 1.359, 1.650, p-value <0.001). Men and those who received multi-intervention had 3.1 (95% CI: 1.345, 7,138, p-value = 0.008) and 6.1 times odds (95% CI: 3.095, 12.121, p-value < 0.001) respectively to be admitted to hospitals following MVC. This study combined geospatial and traditional statistical analyses to evaluate the relationship between injury-related MVCs and clinical parameters and its outcomes. Overall this study has proven that GIS with a combination of traditional statistical analysis is a powerful tool in RTI related research.