Dataset Information

A geographic information system to study trauma epidemiology in India.

ABSTRACT:

Background

Geographic Information Systems (GIS) describe the topography and chronology of events in a defined vector space. GIS may also be used for an integrated analysis of environmental and road-related risk factors for traffic accidents.

Methods

In a retrospective study, various features of 165 road crashes were linked to a GIS-generated digital map of an area close to a national highway in India. By overlay tools, clusters of accidents were identified, and color-coded according to accident mechanisms and injury patterns.

Results

Spatial analysis revealed a cluster with a high incidence of motorbike injuries resulting in fractures. Examination of the spot demonstrated the risky combination of a speed breaker and a broken traffic light. After fixing the light, no further accidents occurred at the site.

Conclusion

GIS is a promising technology for geo-referencing accident data, and may be a valuable tool to identify areas of priority for injury prevention in India.

SUBMITTER: Bagaria V

PROVIDER: S-EPMC2241765 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

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Publications

A geographic information system to study trauma epidemiology in India.

Bagaria Vaibhav V Bagaria Saurabh S

Journal of trauma management & outcomes 20071126 1

<h4>Background</h4>Geographic Information Systems (GIS) describe the topography and chronology of events in a defined vector space. GIS may also be used for an integrated analysis of environmental and road-related risk factors for traffic accidents.<h4>Methods</h4>In a retrospective study, various features of 165 road crashes were linked to a GIS-generated digital map of an area close to a national highway in India. By overlay tools, clusters of accidents were identified, and color-coded accordi ...[more]

PMID: 18271993

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Project description:Background & objectivesStudies assessing the spatial and temporal association of ambient air pollution with emergency room visits of patients having acute respiratory symptoms in Delhi are lacking. Therefore, the present study explored the relationship between spatio-temporal variation of particulate matter (PM)2.5 concentrations and air quality index (AQI) with emergency room (ER) visits of patients having acute respiratory symptoms in Delhi using the geographic information system (GIS) approach.MethodsThe daily number of ER visits of patients having acute respiratory symptoms (less than or equal to two weeks) was recorded from the ER of four hospitals of Delhi from March 2018 to February 2019. Daily outdoor PM2.5 concentrations and air quality index (AQI) were obtained from the Delhi Pollution Control Committee. Spatial distribution of patients with acute respiratory symptoms visiting ER, PM2.5 concentrations and AQI were mapped for three seasons of Delhi using ArcGIS software.ResultsOf the 70,594 patients screened from ER, 18,063 eligible patients were enrolled in the study. Winter days had poor AQI compared to moderate and satisfactory AQI during summer and monsoon days, respectively. None of the days reported good AQI (<50). During winters, an increase in acute respiratory ER visits of patients was associated with higher PM2.5 concentrations in the highly polluted northwest region of Delhi. In contrast, a lower number of acute respiratory ER visits of patients were seen from the 'moderately polluted' south-west region of Delhi with relatively lower PM2.5 concentrations.Interpretation & conclusionsAcute respiratory ER visits of patients were related to regional PM2.5 concentrations and AQI that differed during the three seasons of Delhi. The present study provides support for identifying the hotspots and implementation of focused, intensive decentralized strategies to control ambient air pollution in worst-affected areas, in addition to the general city-wise strategies.

Project description:Environmental exposure assessment based on Geographic Information Systems (GIS) and study participants' residential proximity to environmental exposure sources relies on the positional accuracy of subjects' residences to avoid misclassification bias. Our study compared the positional accuracy of two automatic geocoding methods to a manual reference method.We geocoded 4,247 address records representing the residential history (1990-2008) of 1,685 women from the French national E3N cohort living in the Rhône-Alpes region. We compared two automatic geocoding methods, a free-online geocoding service (method A) and an in-house geocoder (method B), to a reference layer created by manually relocating addresses from method A (method R). For each automatic geocoding method, positional accuracy levels were compared according to the urban/rural status of addresses and time-periods (1990-2000, 2001-2008), using Chi Square tests. Kappa statistics were performed to assess agreement of positional accuracy of both methods A and B with the reference method, overall, by time-periods and by urban/rural status of addresses.Respectively 81.4% and 84.4% of addresses were geocoded to the exact address (65.1% and 61.4%) or to the street segment (16.3% and 23.0%) with methods A and B. In the reference layer, geocoding accuracy was higher in urban areas compared to rural areas (74.4% vs. 10.5% addresses geocoded to the address or interpolated address level, p < 0.0001); no difference was observed according to the period of residence. Compared to the reference method, median positional errors were 0.0 m (IQR = 0.0-37.2 m) and 26.5 m (8.0-134.8 m), with positional errors <100 m for 82.5% and 71.3% of addresses, for method A and method B respectively. Positional agreement of method A and method B with method R was 'substantial' for both methods, with kappa coefficients of 0.60 and 0.61 for methods A and B, respectively.Our study demonstrates the feasibility of geocoding residential addresses in epidemiological studies not initially recorded for environmental exposure assessment, for both recent addresses and residence locations more than 20 years ago. Accuracy of the two automatic geocoding methods was comparable. The in-house method (B) allowed a better control of the geocoding process and was less time consuming.

Dataset Information

A geographic information system to study trauma epidemiology in India.

Background

Methods

Results

Conclusion

Publications

A geographic information system to study trauma epidemiology in India.

Similar Datasets

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