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Methodological considerations in MVC epidemiological research.
Fridman, Liraz; Rothman, Linda; Howard, Andrew William; Hagel, Brent E; Macarthur, Colin.
Afiliação
  • Fridman L; Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada liraz.fridman@gmail.com.
  • Rothman L; School of Occupational and Public Health Faculty of Community Services, Ryerson University, Toronto, Ontario, Canada.
  • Howard AW; Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.
  • Hagel BE; Orthopaedic Surgery, Hospital for Sick Children, Toronto, Ontario, Canada.
  • Macarthur C; Department of Paediatrics, University of Calgary, Calgary, Alberta, Canada.
Inj Prev ; 27(2): 155-160, 2021 04.
Article em En | MEDLINE | ID: mdl-33199349
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Acidentes de Trânsito Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Child / Humans Idioma: En Revista: Inj Prev Assunto da revista: PEDIATRIA / TRAUMATOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Acidentes de Trânsito Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Child / Humans Idioma: En Revista: Inj Prev Assunto da revista: PEDIATRIA / TRAUMATOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá