Methodological considerations in MVC epidemiological research. 2021

Liraz Fridman, and Linda Rothman, and Andrew William Howard, and Brent E Hagel, and Colin Macarthur
Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada liraz.fridman@gmail.com.

The global burden of MVC injuries and deaths among vulnerable road users, has led to the implementation of prevention programmes and policies at the local and national level. MVC epidemiological research is key to quantifying MVC burden, identifying risk factors and evaluating interventions. There are, however, several methodological considerations in MVC epidemiological research. This manuscript collates and describes methodological considerations in MVC epidemiological research, using examples drawn from published studies, with a focus on the vulnerable road user population of children and adolescents. Methodological considerations in MVC epidemiological research include the availability and quality of data to measure counts and calculate event rates and challenges in evaluation related to study design, measurement and statistical analysis. Recommendations include innovative data collection (eg, naturalistic design, stepped-wedge clinical trials), combining data sources for a more comprehensive representation of collision events, and the use of machine learning/artificial intelligence for large data sets. MVC epidemiological research can be challenging at all levels: data capture and quality, study design, measurement and analysis. Addressing these challenges using innovative data collection and analysis methods is required.

UI MeSH Term Description Entries
D012107 Research Design A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly. Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D002648 Child A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL. Children
D003625 Data Collection Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data. Data Collection Methods,Dual Data Collection,Collection Method, Data,Collection Methods, Data,Collection, Data,Collection, Dual Data,Data Collection Method,Method, Data Collection,Methods, Data Collection
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000063 Accidents, Traffic Accidents on streets, roads, and highways involving drivers, passengers, pedestrians, or vehicles. Traffic accidents refer to AUTOMOBILES (passenger cars, buses, and trucks), BICYCLING, and MOTORCYCLES but not OFF-ROAD MOTOR VEHICLES; RAILROADS nor snowmobiles. Traffic Collisions,Traffic Crashes,Traffic Accidents,Accident, Traffic,Collision, Traffic,Collisions, Traffic,Crashes, Traffic,Traffic Accident,Traffic Collision
D000293 Adolescent A person 13 to 18 years of age. Adolescence,Youth,Adolescents,Adolescents, Female,Adolescents, Male,Teenagers,Teens,Adolescent, Female,Adolescent, Male,Female Adolescent,Female Adolescents,Male Adolescent,Male Adolescents,Teen,Teenager,Youths
D001185 Artificial Intelligence Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language. AI (Artificial Intelligence),Computer Reasoning,Computer Vision Systems,Knowledge Acquisition (Computer),Knowledge Representation (Computer),Machine Intelligence,Computational Intelligence,Acquisition, Knowledge (Computer),Computer Vision System,Intelligence, Artificial,Intelligence, Computational,Intelligence, Machine,Knowledge Representations (Computer),Reasoning, Computer,Representation, Knowledge (Computer),System, Computer Vision,Systems, Computer Vision,Vision System, Computer,Vision Systems, Computer
D012307 Risk Factors An aspect of personal behavior or lifestyle, environmental exposure, inborn or inherited characteristic, which, based on epidemiological evidence, is known to be associated with a health-related condition considered important to prevent. Health Correlates,Risk Factor Scores,Risk Scores,Social Risk Factors,Population at Risk,Populations at Risk,Correlates, Health,Factor, Risk,Factor, Social Risk,Factors, Social Risk,Risk Factor,Risk Factor Score,Risk Factor, Social,Risk Factors, Social,Risk Score,Score, Risk,Score, Risk Factor,Social Risk Factor

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