Development of a Comprehensive Approach for Serious Traffic Crash Injury Measurement and Reporting Systems (2021) / Chapter Skim
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Pages 22-28
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From page 22... ...
22 5 Near-Term Solutions to Measuring Serious Injury 5.1 Overview of Near-Term Solutions The time frame for linkage in most states is too slow for the timing of implementation of MAP-21. Indeed, at the time of this writing, the FHWA has released a Notice of Proposed Rulemaking (NPRM)
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23 The next three sections explore the possibility of using available data in the near term to estimate serious injury as defined by MAIS 3+. We focus this section on solutions whereby states can estimate serious injuries using a medical-outcome-based definition, rather than try to "fix" a non-diagnosis-based definition.
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24 There are multiple approaches to sampling in statistics, each of which has advantages and disadvantages. This section contains a brief background on sampling followed by a recommended approach to sampling medical records associated with crash data.
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25 collection differs dramatically, a stratified sample design can improve efficiencies substantially over a simple random sample. The best near-term approach to correcting both bias and over-counting is sampling of medical records from crash-involved occupants in a state.
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26 perfect match to MAIS, head-on collisions would still cause more injuries and the head-on variable would still be significant in the ordered logit model. The inclusion of factors like head-on in the Tarko et al.
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27 of angle crashes and underestimation of single-vehicle crashes, but in this comparison has overcorrected for head-on risk. Table 10.
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28 Figure 5. Comparison of relative proportions of number of vehicles involved for three definitions of serious injury (2011 validation dataset)
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