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Help Center > Non-Fatal Crash Completeness Tool

Description for Non-Fatal Crash Completeness Tool

All States are responsible for reporting fatal and non-fatal crash records to FMCSA that meet the standard reporting criteria. FMCSA uses these records to support its mission to reduce crashes, injuries, and fatalities involving large trucks and buses on our nation's roadways. Because of the complexity of State crash reporting systems, it is often difficult for States to determine if all reportable non-fatal crash records have been submitted to FMCSA. State-specific evaluations of crash reporting have determined significant underreporting and prompted the need to develop non-fatal crash record benchmarks for each State. The NFCC tool is intended to serve as a guideline to assess whether a State's non-fatal crash reporting falls within an expected range.

 View the Interactive Guide to learn about the NFCC evaluation process

The NFCC tool determines a State rating by comparing the number of State-reported non-fatal crash records in MCMIS to an expected range of non-fatal crash records generated by a statistical model. The input data used by the model, how the data are processed through the model, and the evaluation and final determination of a State's NFCC result are described in a 4-step approach.

Step 1: Input State Fatal Data

The tool's algorithm utilizes a relationship between the numbers of fatal crash involvements to the number of non-fatal crash record involvements. State's fatal crash records reported to MCMIS are used as an input value to this tool. This record set represents interstate and intrastate motor carriers and includes large truck and bus vehicle types. The MCMIS fatal crash records cover a 12-month time period that ends six months prior to the MCMIS snapshot date.

Step 2: Process Data Through a Statistical Model

It is hypothesized that a ratio of fatal crash involvements to non-fatal reportable involvements exists that is independent of any State’s data system and applies across all the States. Since the number of fatal crash involvements is generally well known, it is then possible to predict the number of non-fatal crash involvements that occur in a State. The NFCC tool utilizes a simple log-linear regression model that translates the number of fatal crash involvements in MCMIS to an expected range of non-fatal crash record involvements. The State fatal data is input to the statistical model to generate estimates of non-fatal crash involvements.

Two sets of prediction intervals are generated by the model to provide ranges of non-fatal crash involvements. The prediction intervals are expressed in terms of the probability that the number of reportable non-fatal crash involvements fall within the prediction interval. The model was programmed to output 99% and 90% prediction intervals. The prediction intervals are used to define expected data ranges, and each range has an upper and lower boundary.

The model incorporates an urbanization factor to account for the proportion of rural to urban commercial vehicle travel in a State. The purpose of the adjustment is to account for any variation in the ratio of fatal to non-fatal reportable crash involvements that may be due to variations in the proportion of rural or urban commercial motor vehicle travel in a State.

Step 3: Compare State Non-Fatal Data to Model Results

The State-reported number of non-fatal crash records in MCMIS are compared against the expected data ranges output by the model. It is determined where the MCMIS non-fatal crash records fall within the expected data ranges. This determination is used to assign each State a base result of 'good', 'fair', 'poor', or 'insufficient data'. The base result assignments are one part of the final result assignment process.

The diagram below illustrates the result ranges based on the expected ranges generated from the statistical model. States with few fatal crash records may yield less predictable output and may be assigned an 'insufficient data' rating.

Result Ranges
Under Reporting   Over Reporting
Poor * Fair Good Fair
<
Lower Boundary
of 99% PI
>=
Lower Boundary
of 99% PI
AND
<
Lower Boundary
of 90% PI
>=
Lower Boundary
of 90% PI
AND
<=
Upper Boundary
of 99% PI
>
Upper Boundary
of 99% PI

* Assign a base result of 'Insufficient Data' if Reported MCMIS Fatal Records is less than 15
   AND Non-Fatal Records is within the Poor Result Range

The base result assignments are defined as follows.

Base Result Assignments: 

Good result range: Reported Non-Fatal crash records (from MCMIS) is greater than or equal to lower boundary of 90% prediction interval AND less than or equal to upper boundary of 99% prediction interval

Fair result range Reported Non-Fatal crash records (from MCMIS) is greater than or equal to lower boundary of 99% prediction interval AND less than lower boundary of 90% prediction interval (under reporting) OR
Reported Non-Fatal crash records is greater than upper boundary of 99% prediction interval (over reporting)

Poor result range: Reported Non-Fatal crash records (from MCMIS) is less than lower boundary of 99% prediction interval AND MCMIS fatal crash records is greater than or equal to 15 (under reporting)

Insufficient Data result range: Reported Non-Fatal crash records (from MCMIS) is less than lower boundary of 99% prediction interval AND MCMIS Fatal crash records is < 15

Step 4: Determine Results

The NFCC result is determined by reviewing the base results and the most recent fatal crash completeness (FCC) measure rating for each State. Both of these results are important for the assignment of the final NFCC result.

The FCC measure rating is considered since the model's output is using State-reported fatal crash records to generate estimated non-fatal crash record values. If a State's FCC rating is 'good' or 'fair', it is assumed their MCMIS fatal crash reporting is sufficient to estimate the expected number of non-fatal crash records. In those cases, the NFCC base result is regarded as the final NFCC result.

If a State's FCC rating is either 'poor' or 'insufficient data', it is assumed their MCMIS fatal crash reporting is insufficient due to inadequate reporting or too few fatal crash involvements occurring in the State. An “override” is administered, meaning, the base result cannot be used. The override will result in an NFCC result of either 'poor' or 'insufficient data' due to incomplete MCMIS fatal reporting.

The final Non-Fatal Crash Completeness result is determined as follows:

Result Criteria
Good  Good result Reported MCMIS non-fatal records fall within Good range AND FCC rating is Good or Fair
Fair  Fair result Reported MCMIS non-fatal records fall within Fair range AND FCC rating is Good or Fair
Poor  Poor result Reported MCMIS non-fatal records fall within Poor range AND FCC rating is Good, Fair or Poor
Insufficient Data Insufficient Data
Base result is assigned Insufficient Data OR
Reported MCMIS non-fatal records fall within Good or Fair range AND FCC rating is Poor OR
FCC rating is Insufficient Data
Definitions
  •  A statistical tool where existing data are used to define a relationship between two sets of numbers, so that in the future, one set can be reliably predicted from the other. In reference to the NFCC measure, verified fatal and non-fatal crash record data were used to define a relationship for predicting an expected range of non-fatal crash records from a known quantity of fatal crash records.
  •  The calculation of a State’s NFCC measure rating is determined by reviewing its Base Rating to its fatal crash completeness measure rating. The Base Rating establishes a good, fair, poor or insufficient data rating range for each State. Each designation is determined by where a State’s non-fatal crash reporting falls.
  •  A crash where one or more persons has non-fatal injuries requiring transportation by a vehicle for the purpose of obtaining immediate medical attention; or one or more of the vehicles were towed away from the scene due to "disabling damage". The towed vehicle need not be the commercial motor vehicle involved in the crash.
  •  An estimated range of values, as defined by a lower and upper value, is determined by a mathematical model and the probability that another number will fall between them. In reference to the NFCC measure, the log-linear regression model generates a unique interval of non-fatal crash records.
  •  A ratio of rural to urban commercial motor vehicle (CMV) travel in the State as calculated from the Federal Highway Administration’s data in the Annual Highway Statistics publication. For each State, the RU factor is calculated by averaging the most recent three years of annual rural CMV travel miles by the average of the most recent three years of annual urban CMV travel miles.
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