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Improving the quality of road injury statistics by using regression models to redistribute ill-defined events
  1. Saeid Shahraz1,
  2. Kavi Bhalla2,
  3. Rafael Lozano3,
  4. David Bartels4,
  5. Christopher J L Murray3
  1. 1Schneider Institute for Health Policy, Heller School of Social Policy and Management, Brandeis University, Cambridge, Massachusetts, USA
  2. 2Department of Global Health and Social Medicine, Harvard School of Public Health, Harvard University, Boston, Massachusetts, USA
  3. 3Institute of Health Metrics and Evaluation, University of Washington, Seattle, Washington, USA
  4. 4Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Saeid Shahraz, Heller School of Social Policy and Management, Brandeis University, 165 Pleasant St, Unit 112, Cambridge, MA 02139, USA; shahraz{at}brandeis.edu

Abstract

Objective To test the predictive ability of multinomial regression method in obtaining category of death distribution for cases with unknown/ill-defined mortality codes.

Methods The authors evaluated the performance of the multinomial regression model by fitting the model to trial datasets from 2004 Mexican vital registration data. To predict category of death, the regression method makes use of explanatory variables, such as gender, age, place of crash, place of residence, education and insurance type. The authors compared the results of a full model regression with those of a reduced model that only contained gender and age as explanatory variables. For this comparison, the authors constructed two forms of data: dummy variable adjustment method and case-wise deleted method. The comparison was made through estimated area under the curve (AUC) for each outcome variable.

Results The full model significantly outperformed the gender-age (reduced) model using both datasets. In the case-wise deleted method, the AUC was increased from 0.55 to 0.7 for the reduced model and from 0.64 to 0.84 for the full model. Improvement in AUC using the dummy variable adjustment method was less significant.

Conclusions To predict ill-defined categories of death, adding relevant explanatory variables to gender and age is recommended. Multiple imputations may perform even better than this model especially when significant portion of the data are missing.

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Footnotes

  • Funding The current project was entirely funded by the World Bank Global Road Safety Facility.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement The authors would like to share the Stata do files and the SMCL files so that the readers and the reviewers can follow the analysis steps and the detailed results of the regression models applied.

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