______________________________________________________________________________ 

This document is available to the public from the National Technical Information Service, Springfield, VA 22161 

DOT HS 809 870 November 2005 

Technical Report 

New England Low Fatality Rates versus 

Low Safety Belt Use 

Published by 

NHTSA’s 

National Center for Statistics and Analysis 
This publication is distributed by the U.S. Department of Transportation, National Highway Traffic 

Safety Administration, in the interest of information exchange. The opinions, findings, and 

conclusions expressed in this publication are those of the author(s) and not necessarily those of the 

Department of Transportation or the National Highway Traffic Safety Administration. The United 

States Government assumes no liability for its contents or use thereof. If trade or manufacturers’ 

names are mentioned, it is only because they are considered essential to the object of the publication 

and should not be construed as an endorsement. The United States Government does not endorse 

products or manufacturers. 
Technical Report Documentation Page 

1. Report No.

DOT HS 809 870 

2. Government Accession No. 3. Recipients's Catalog No. 

5. Report Date 

November 2005 

4. Title and Subtitle

New England Low Fatality Rates versus Low Safety 

Belt Use 6. Performing Organization Code 

NPO-121 

7. Author(s) National Center for Statistics and Analysis Staff 8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)n code

Mathematical Analysis Division 

National Center for Statistics and Analysis 

National Highway Traffic Safety Administration 

U.S. Department of Transportation 

NPO-121, 400 Seventh Street SW. 

Washington, DC 20590 

11. Contract of Grant No. 

13. Type of Report and Period Covered

NHTSA Technical Report 

12. Sponsoring Agency Name and Address

Mathematical Analysis Division, 

National Center for Statistics and Analysis 

National Highway Traffic Safety Administration 

U.S. Department of Transportation 

NPO-121, 400 Seventh Street SW. 

Washington, DC 20590

14. Sponsoring Agency Code

15. Supplementary Notes 

16. Abstract 

This study examines the apparent anomaly of low safety belt usage in New England and relatively low fatality rates in New 

England. There are many variables that effect fatalities. Restraint use is one of the independent variables that contribute to 

fatalities and the subsequent fatality rates. The values of other variables in New England offset the expected high fatality rates 

if only safety belt use is considered. 

Restraint use continues to be the most effective individual countermeasure to reduce traffic crash related injuries and fatalities. 

17. Key Words 

restraint, safety belts, seat belts, logistic regression 

linear regression, multivariate, New England, fatality rates, 

States, VMT, registered vehicles, population, drivers

18. Distribution Statement

Document is available to the public through the National 

Technical Information Service, Springfield, VA 22161 

http://www.ntis.gov

19. Security Classif. (of this report)

 Unclassified 

20. Security Classif. (of this page)

 Unclassified 

21. No of Pages

18 

22. Price

Form DOT F1700.7 (8-72) Reproduction of completed page authorized 

Table of Contents 

Executive Summary.................................................................................................................1 

1. Background ........................................................................................................................3 

2. Approach ............................................................................................................................4 

3. Databases ............................................................................................................................4 

4. State-Reported Belt Use as a Predictor of Belt Use in Fatal Crashes ...........................4 

5. Four Variables Associated With Fatalities......................................................................5 

6. Variables Indicative of Fatalities in Crashes...................................................................6 

7. Fatal New England Crashes Compared to Fatal Crashes in the Rest of the 

United States.......................................................................................................................6 

8. Fatality Rate Models..........................................................................................................7 

9. Conclusions.........................................................................................................................9 

10. Appendix A......................................................................................................................10 

11. Appendix B ......................................................................................................................17 

12. Bibliography ...................................................................................................................19 

List of Tables 

Table 1: Reported Restraint Use in Fatal Crashes by Year ......................................................3

Table 2: Variables Associated with Increasing Fatalities.........................................................5

Table 3: Characteristics That Raise the Odds of a Front-Seat Fatality Occurring Within a 

GES Crash..................................................................................................................................6

Table 4: FARS Characteristics Associated With Fatal crashes ...............................................7

Table 5: R2 Values by Level Modeled and Dependent Variables ............................................7 
 

NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

Table 6: Order of First Appearance of Safety Belt Usage in Best Models...............................8

Table A1: Fatalities Divided by 100 Million Vehicle Miles Traveled. Good Model – State 

Characteristics Only.................................................................................................................11

Table A2: Fatalities Divided by 100 Million Vehicle Miles Traveled. Better Model – State 

Characteristics With Alcohol...................................................................................................11

Table A3: Fatalities Divided by 100 Million Vehicle Miles Traveled. Good Model – State 

Characteristics With Alcohol and Safety Belt Use..................................................................12

Table A4: Fatalities Divided by Licensed Drivers. Good Model – State Characteristics 

Only..........................................................................................................................................13

Table A5: Fatalities Divided by Licensed Drivers. Better Model – State Characteristics 

With Alcohol............................................................................................................................13

Table A6: Fatalities Divided by Licensed Drivers. Best Model – State Characteristics With 

Alcohol and Safety Belt Use....................................................................................................13 

Table A7: Fatalities Divided by Registered Vehicles. Good Model – State Characteristics 

Only..........................................................................................................................................14

Table A8: Fatalities Divided by Registered Vehicles. Better Model – State Characteristics 

With Alcohol............................................................................................................................14

Table A9: Fatalities Divided by Registered Vehicles. Best Model – State Characteristics 

With Alcohol and Safety Belt Use...........................................................................................15 

Table A10: Fatalities Divided by Registered Vehicles. Good Model – State Characteristics 

Only..........................................................................................................................................16

Table A11: Fatalities Divided by Registered Vehicles. Better Model – State 

Characteristics With Alcohol...................................................................................................16

Table A12: Fatalities Divided by Registered Vehicles. Best Model – State Characteristics 

With Alcohol and Safety Belt Use...........................................................................................16

Table B1: Variables Associated With Increasing Fatalities by State .....................................18
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

7

Executive Summary 

Background Issue 

How can both the fatality rates and safety belt use 

rates be lower for New England than for the rest of 

the country? This appears to be contradictory. Is 

there a logical explanation to resolve this apparent 

anomaly? 

 

Methodology 

Two distinct but complimentary approaches were 

used to answer this question. First, dichotomous 

logistic models were developed to identify crash 

variables that are associated with fatal crashes. 

For the second approach, continuous models for four 

commonly used fatality rates were constructed. 

Three “good/better/best” levels were modeled for 

each fatality rate. The first level, the good level, 

modeled the characteristics of each State; the second 

level, the better level, added alcohol information. 

The third level, the best level, added safety belt 

usage to the second-level models. 

Conclusions 

Safety belt use is not the only variable affecting the 

fatality rates. There are many other contributing 

crash factors. During the first approach, which uses 

multivariate logistic models, significant crash 

variables were associated with the crash being 

classified as a fatal crash, that is, someone died as a 

result of it. These significant crash variables 

included speed limits of at least 55 mph, only a 

single vehicle involved in the crash, crashes at night, 

rollover crashes, head-on crashes, curved roadways, 

and restraint use. These significant crash variables 

were shown to be different for New England 

compared to the rest of the United States. The odds 

of a fatal crash in New England were shown to be 

not significantly different than the rest of the United 

States when adjustments were made for differences 

in crash characteristics of the New England States. 

The second approach acknowledges that there are a 

host of non-crash-related variables that can affect 

fatality rates. In particular, in addition to restraint 

use, other factors including such variables as the 

average vehicle miles driven, income, employment, 

minority density, land use, and the percent of fatal 

crashes with a maximum imputed blood alcohol 

concentration of .08 grams pre deciliter or higher 

affect the fatality rates of the States. The best-level 

continuous models explained approximately 90 

percent of the variation for the four commonly used 

fatality rates among the States, i.e., R2 ≈ 0.90. One 

sees that the values of these other factors 

reduce/offset the effect of low restraint use and 

result in lower-than-expected fatality rates if only 

restraint use is considered. 

The difference in restraint use in fatal crashes 

between New England (53 %) and the rest of the 

United States (58 %) is not very great and is 

narrowing. 

Restraint use is the only factor under control of each 

vehicle occupant. Restraint use remains the most 

effective counter-measure to reduce injuries and 

deaths available to vehicle occupants. 

Increased restraint use in New England will further 

reduce fatalities and injuries in New England 

although such increased use would not significantly 

alter the apparent anomaly, which appears to be the 

result of other factors.
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

8

New England Low Fatality Rates 

versus Low Safety Belt Use 

1. Background 

Reported New England safety belt use, as measured 

by State surveys, is lower than much of the United 

States. However, New England fatality rates are not 

correspondingly high. (The New England States are: 

Connecticut, Massachusetts, Maine, New 

Hampshire, Rhode Island, and Vermont.) 

Data extracted from the National Center for 

Statistics and Analysis published data1

 from 2000 to 

2002 shows that the restraint use rate of fatally 

injured occupants of passenger cars and light trucks 

for New England is 32 percent and for the rest of the 

nation the corresponding restraint use is 41 percent. 

When the unknowns are distributed, the rates are 

38 percent and 44 percent respectively. 

Table 1 shows the reported restraint use in fatal 

crashes for New England and the rest of the United 

States by year. Restraint use of non-fatally-injured 

people are included in the calculations. Although 

the reported restraint use in New England is 

somewhat lower than in the rest of the United States, 

the restraint use has been increasing both in New 

England and the rest of the United Sates. In 

addition, the difference between New England and 

the rest of the United States is diminishing. 

Four commonly published fatality rates are: 

(1) fatalities per 100 million vehicle miles traveled, 

(2) fatalities per 100,000 drivers, 

(3) fatalities per 100,000 registered vehicles, and (4) 

fatalities per 100,000 population. These four 

published fatality rates are 1.01, 12.30, 10.69, 9.13, 

respectively, for New England and 1.53, 22.55, 

19.57, 15.25, respectively, for the rest of the United 

States. 

The question arises, how can both the fatality rates 

and belt use rates be lower for New England than the 

rest of the country. This appears to be contradictory. 

To answer the question we actually need to answer a 

more general question, that is: What are the 

 

1 Traffic Safety Facts 2000, DOT HS 809 337, pp. 148-149 , 

Table 108 and pp. 156-157, Table 112, April 2002 

 Traffic Safety Facts 2001, DOT HS 809 484, pp. 148-149, 

Table 108 and pp. 156-157, Table 112, December 2002 

 Traffic Safety Facts 2002, DOT HS 809 620, pp. 148-149, 

Table 108 and pp. 156-157, Table 112, January 2004

significant factors, i.e., the independent variables, 

that explain State fatality rates? The simple 

explanation of the apparent anomaly is that restraint 

use is one of many factors associated with fatality 

rates. The values of these other factors reduce/offset 

the effect of low restraint use and result in lowerthan-expected fatality rates if only restraint use is 

considered. 

Table 1: Reported Restraint Use in Fatal 

Crashes by Year 

Year New England Rest of US 

2000 47 % 54 % 

2001 49 % 55 % 

2002 53 % 56 % 

2003 53 % 58 % 

Improvement 6 percentage points 4 percentage points 

Source: NHTSA/NCSA/FARS/SAS 

2. Approach 

Several different questions have been proposed to 

examine the apparent discrepancy in fatality rates 

compared to belt use rates: 

 Does State-reported safety belt use predict belt 

use in fatal crashes? 

 Are significant crash variables different for New 

England compared to the rest of the United 

States? 

 Which additional crash characteristics indicate a 

fatality will occur in a crash? 

 Are the odds of a fatal crash in New England 

significantly different/higher than the rest of the 

United States? When adjustments are made for 

differences in crash characteristics in the New 

England States, is the New England location still 

a significant variable? 

Finally, continuous models for each of the four 

fatality rates were constructed. Three 

“good/better/best” model levels for each rate were 

constructed. The first level, the good level, modeled 

the characteristics of each State; the second level, 

the better level, added alcohol information. The third 
level, the best level, added safety belt usage to the 

second level models. The best-level models 

explained approximately 90 percent of the variation 

among the States, i.e., R2 ≈ 0.90. 

3. Databases 

NHTSA’s belt use data: Data on belt use is 

collected by most of the States and the District of 

Columbia. Belt use rates from the States and 

territories are based on surveys conducted according 

to criteria issued in Section 157 of Title 23 of the 

Federal Register (23 CFR Part 1340). These criteria 

were established as part of an occupant protection 

incentive grant program for the 50 States, the 

District of Columbia, and Puerto Rico. 

NHTSA’s National Automotive Sampling SystemGeneral Estimates System (NASS-GES) data are 

obtained from a nationally representative sample 

selected from all police-reported crashes. To be 

eligible for the GES sample, a police accident report 

must be completed for the crash, and the crash must 

involve at least one motor vehicle traveling on a 

trafficway and result in property damage, injury, or 

death. 

NHTSA’s Fatality Analysis Reporting System, 

(FARS) became operational in 1975. FARS data are 

based on a census of fatal traffic crashes within the 

United States including the District of Columbia and 

Puerto Rico. To be included in FARS, a crash must 

involve a motor vehicle traveling on a trafficway 

customarily open to the public, and must result in 

the death of an occupant of a vehicle or a 

nonmotorist within 30 days of the crash. 

The Federal Highway Administration’s Highway 

Statistics provides data on highway mileage and 

performance. The statistics have been analyzed and 

reported using procedures that provide comparability 

of values among the States. As a result, some values 

may differ from values reported by the States or 

other agencies for similar items. 

4. State-Reported Belt Use as a Predictor of 

Belt Use in Fatal Crashes 

Belt use survey data collected using the criteria as 

stated in the Databases section of this note from 

1998 to 2002, also found in Glassbrenner,2

 were 

combined with reported belt use in fatal crashes in a 

NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

9

 

2

 Glassbrenner, Donna, Safety Belt Use in 2003 – Use 

Rates in the States and Territories DOT HS 809 713, 

linear regression model. Both belt use rates, for 

fatally injured outboard front-seat passengers and for 

all outboard front-seat passengers involved in fatal 

crashes were modeled. The results are as follows: 

091.0*689.0 Yˆ = SRBU −

002.0*818.0 Zˆ = SBRU −

Where: 

Yˆ is the estimated safety belt use rate for fatally 

injured outboard front-seat passengers, 

Zˆ is the estimated safety belt use rate for all 

outboard front-seat passengers involved in fatal 

crashes; and SRBU is the State reported belt use as 

reported in Glassbrenner.2

In both cases, the p-values for the coefficients of 

SRBU are (p < 0.0001). The R-square values are 

73.8 percent and 77.4 percent for fatally injured 

outboard front- seat passengers and all outboard 

front-seat passengers respectively. This 

demonstrates a relationship between State-reported 

belt use and belt use in fatal crashes as reported in 

FARS. If this relationship did not exist, then there 

would not be any reason to be concerned about the 

unexpected relationship between observed belt use 

in New England and the New England fatality rates. 

The results of this analysis are: The belt use rate of 

fatally injured outboard front-seat passengers is 

approximately 58 percent of the State-reported belt 

use rate. Correspondingly, the belt use rate for all 

outboard front-seat passengers is approximately 82 

percent of the State-reported belt use rate. The 

difference between belt use rate for fatalities, 58 

percent and 82 percent of all passengers, is largely 

due to the effectiveness of seat belts. 

5. Four Variables Associated with Fatalities 

Speed limits of 55 mph or higher, rollovers, good 

weather, and crashes that occur in rural areas are 

positively associated with increasing fatalities; see 

table 2. 

 

 

National Highway Traffic Safety Administration, March 

2004. 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

10

Table 2: Variables Associated with 

Increasing Fatalities

Variable New 

England 

Rest of 

US 

Speed limit ≥ 55 mph 24 % 56 % 

Rollovers 6 % 9 % 

Good weather 84 % 88 % 

Rural crashes 42 % 61 % 

Source: NHTSA/NCSA/FARS/SAS 

Each of these variables tends to reduce the fatalities 

in New England compared to the rest of the United 

States. 

Good weather often increases the odds of a fatality. 

A reasonable explanation is that many vehicles are 

driven at slower speeds in bad weather than in good 

weather. As a result, many crashes that occur in bad 

weather may have less kinetic energy to dissipate 

and as a result there are fewer fatalities. 

The figure of Appendix B shows these four variables 

for each State. The States and the District of 

Columbia have been partitioned into three levels, 

Better than Average/white, Average/gray, and 

Below Average/black with 17 jurisdictions in each 

level. When examining the figure one sees that the 

New England States are mostly classified as Better 

than Average/white, for these variables. 

6. Variables Indicative of Fatalities in 

Crashes 

To identify additional variables that are associated 

with fatal crashes, the General Estimates System was 

chosen. 

Since the GES data are from a complex sample 

survey rather than a census, professional software 

for survey data analysis for multi-stage sample 

designs (SUDAAN) developed by Research Triangle 

Institute was used to analyze these this data. In 

addition to the variables already discussed, five 

additional crash characteristics were identified as 

associated with increasing the odds of a fatality of an 

outboard front-seat passenger given a crash; see 

table 3. 

Table 3: Characteristics That Raise the 

Odds of a Front-Seat Fatality Occurring 

Within a GES Crash 

Characteristic Odds Ratio p-value 

Speed limit ≥ 55 mph 2.639 0.0001 

Single-vehicle crash 2.480 <0.0001 

Night crash 1.535 0.0001 

Rollover-involved crash 3.450 <0.0001 

Head-on crash 10.327 <0.0001 

Curved roadway 2.235 <0.0001 

Good weather 1.624 0.0022 

Unbelted 5.549 <0.0001 

Source: NHTSA/NCSA/GES/SUDAN 

7. Fatal New England Crashes Compared to 

Fatal Crashes in the Rest of the United States 

In fatal crashes, by definition, at least one person 

must die. In a single-vehicle crash with a single 

occupant and no nonoccupants the driver/occupant 

always dies in the crash by definition. This can cause 

technical problems when logistic regression is 

employed.3

 To avoid these problems, the fatal crash 

data were limited to crashes involving at least two 

individuals. 

This reduced set of crashes is analyzed with logistic 

regression to estimate the change in the odds of a 

fatality of an occupant in a passenger vehicle due to 

the crash occurring in New England. The result is 

New England increases the odds of a fatality by 14.5 

percent. The coefficient has a p-value of < 0.0001. 

We can therefore conclude that within this limited 

set of crashes, the location of New England raises 

the odds of a fatality compared to the rest of the 

country by 14.5 percent. 

From the previous analysis of the GES data one sees 

that several crash characteristics -speed limit of 55 

mph or higher, single-vehicle crashes, night crashes, 

rollover-involved crashes, head-on crashes, curved 

roadways, good weather, and lack of restraint use -- 

all raise the odds of a crash leading to a fatality. In 

 

3

 Hosmer, D. W. and Lemeshow, S. Applied Logistic 

Regression, John Wiley and Sons 1989. 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

11

addition, the difference of a rural location compared 

to an urban location is known to increase the odds of 

a fatality in a crash. Armed with this information 

we can now ask the question: 

Is the New England location a significant variable, if 

adjustments are made for other independent 

characteristics that are known to contribute to fatal 

crashes? The answer is no; see Table 4. 

One can interpret the results of Table 4 in the 

following way. Although there is a distinct 

difference in the propensity for a fatality within New 

England compared to the rest of the country, that 

difference is offset by differences in crash 

characteristics. 

It is clear that crash characteristics are associated 

with fatalities and therefore fatality rates. In 

addition to the location of the crash (New England 

versus the rest of the country) other factors 

contribute to fatalities. The models constructed so 

far address discrete events. Continuous-fatality-rate 

models are now investigated. 

Table 4: FARS Characteristics Associated 

with Fatal Crashes 

Characteristic p-value 

Speed limit ≥ 55 mph <0.0001 

Single-vehicle crash <0.0001 

Night crash <0.0001 

Rollover-involved crash 0.0236 

Head-on crash <0.0001 

Curved roadway <0.0001 

Good weather <0.0001 

Unbelted <0.0001 

Rural location <0.0001 

New England 0.1358

Source: NHTSA/NCSA/FARS/SAS 

8. Fatality Rate Models 

Three levels of non-linear continuous models are 

used to predict four commonly used fatality rates. 

The modeled fatality rates were: (1) fatalities 

divided by 100 million vehicle miles traveled, 

(2) fatalities divided by licensed drivers, (3) fatalities 

divided by registered vehicles, and (4) fatalities 

divided by population. The “good” models represent 

the inherent nature of each State and have terms that 

reflect differences in congestion and mobility, 

affluence, culture, the environment, and land use 

(rural versus urban). The “better” models add the 

first of two social behavior variables, namely, the 

presence of alcohol in fatal crashes. Finally, the 

“best” models add data on safety belt use. SAS®

brand software was used to determine the 

coefficients of the terms of the multivariate models. 

All terms included in all the models were 

statistically significant with p-values of less than 

0.05. 

The “good” models, which represent the inherent 

nature of the States, explain from 82 percent to 86 

percent of the variation, that is, 0.82 ≤ R2 ≤ 0.86. 

With the addition of the alcohol data to the model, 

even more of the variation is explained, with 0.83 ≤

R2 ≤ 0.91. 

Inclusion of data on safety belt use in the “best” 

models raises the explained variation to at least 86 

percent, that is, 0.86 ≤ R2 ≤ 0.91; see table 5. 

 

Table 5: R2 Values by Level Modeled and 

Dependent Variable. 

Level VMT Drivers Registered 

Vehicles Pop. 

Good 0.82 0.83 0.86 0.85 

Better 0.83 0.84 0.91 0.89 

Best 0.86 0.91 0.91 0.91 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Safety belt usage is critically important in 

understanding fatality rates. When building 

multivariate models one technique allows the 

software to add the variables to the model according 

to their importance in explaining the variance of the 

dependent variable. When this is done for the “best” 

models the first appearance of safety belt usage is 

either the second or fourth variable added to the 

model. The improvement in R2

 ranges from a low of 

0.05 to a high of 0.21. 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

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Table 6: Order of First Appearance of Safety 

Belt Usage in Best Models 

Model 1st

Appearance 

R2

Improvement 

VMT 2nd variable 0.14 

Drivers 2nd variable 0.21 

Reg. Veh. 4th variable 0.05 

Population 2nd variable 0.17 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

The models with their variables and associated 

coefficients are in Appendix A. The variable 

definitions appear below by categories. 

Variables reflecting differences in congestion and 

mobility: 

VMT/Drivers: 

Total vehicle miles traveled in the State divided 

by the number of licensed drivers in the State. 

VMT/Population: 

Total vehicle miles traveled in the State divided 

by the State population. (Included in all three 

models.) 

VMT/Vehicles: 

Total vehicle miles traveled in the State divided 

by the number of registered vehicles in the State. 

Area/Population: 

Total land area in square miles in the State 

divided by the State population. 

Area/Vehicles: 

Total land area in square miles in the State 

divided by the number of registered vehicles in 

the State. 

Variables reflecting differences in affluence: 

Per Cap Income: 

Per capita income for the State. 

% Unemployed: 

Unemployment rate for the State. 

% Employed: 

Percent of residents 16 or older with full- or 

part-time employment. 

Variable reflecting differences in culture: 

% Minority: 

The percent of the population within the State 

that is classified as any racial minority or 

Hispanic. (Included in all models.) 

Variables reflecting differences in land use (rural 

versus urban): 

Area: 

Rural and urban areas within a State in square 

miles. 

Rural Area: 

Rural area within a State in square miles 

% Rural Area: 

The percent of the State’s land that is classified 

as rural area. 

Lane Miles: 

The urban and rural lane miles within the State. 

Rural Lane Miles: 

Rural lane miles within the State 

% Rural Lane Miles: 

The percent of all lane miles within the State 

classified as rural 

Variables reflecting differences in social behavior: 

% Alcohol ≥ 0.08: 

The percent of fatal crashes, within the State, 

with a maximum imputed blood alcohol 

concentration (BAC) level of .08 or higher. 

(Included in all three level-2 and level-3 

models.) 

% Unrestrained: 

The percent of unrestrained outboard front-seat 

passengers, involved in fatal crashes, within the 

state. (Included in all level- 3 models.) 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

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9. Conclusions

The difference in restraint use in fatal crashes 

between New England, 53 percent, and the rest 

of the United States, 58 percent, is not very great 

and is narrowing. 

Restraint use is only one of several variables 

needed to adequately account for the differences 

in fatality rates among the States. 

Combinations of variables that describe 

differences in social economic conditions 

(including safety belt use), congestion, 

affluence, culture, and land use predict virtually 

all of the variance of the four fatality rates for 

the 50 States and the District of Columbia. 

The differences in the other variables, beyond 

the control of vehicle occupants, reduce the 

expected high fatality rates in New England, if 

only restraint use is considered. 

Restraint use, which appears in all of the “best” 

models, is the only variable over which motor 

vehicle occupants have control. 

Restraint use is the most effective 

countermeasure to reduce injuries and fatalities.
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

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Appendix A 

Variables and coefficients of the models 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

15

Fatalities Divided by 100 Million Vehicle Miles Traveled 

Table A1: Fatalities Divided by 100 

Million Vehicle Miles Traveled. 

Good Model – State Characteristics Only 

Independent Variables Coefficients R2 = 0.82 

VMT/Drivers -0.000036 p = .0322 

Area/Vehicles 3.65E10-5 p = .0005 

% Minority2 3.18E10-6 P = .0096 

Lane Miles2 2.78E10-14 p = .0315 

(Per Cap Income) * 

 (% Employed) -1.74E10-7 p < .0001 

(Per Cap Income) * 

 (% Rural Area) 6.32E10-7 p < .0001 

(% Minority) * 

 (% Unemployed) 4.08E10-5 p = .0087 

% Employed -0.00054 p = .0012 

Lane Miles -2.20E10-8 p = .0065 

Area/Population 0.00015 p = .0002 

1/(% Minority) -0.01845 p = .0177 

1/(% Unemployed) -0.08450 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

The “*” indicates the interaction term 

between the two variables. 

Table A2: Fatalities Divided by 100 

Million Vehicle Miles Traveled. 

Better Model – State Characteristics With 

Alcohol 

Independent Variables Coefficients R2 = 0.83 

VMT/Area 0.00131 p = .0136 

Area/Drivers -0.00260 p = .0227 

% Rural Lane Miles 0.01193 p = .0013 

% Employed2 -5.63E10-6 p < .0001 

% Alcohol ≥ 0.082 0.02507 P = .0003 

(Per Cap Income) * 

 (% Unemployed) -4.80E10-8 p = .0002 

Lane Miles2 -0.00123 p = .0003 

Area/Population 0.00036 p = .0200 

1/(% Minority) -0.02897 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

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Fatalities Divided by 100 Million Vehicle Miles Traveled Continued 

Table A3: Fatalities Divided by 100 

Million Vehicle Miles Traveled. 

Best Model – State Characteristics With 

Alcohol and Safety Belt Use 

Independent Variables Coefficients R2 = 0.86 

Rural Lane 

Miles/Area 

-0.00094 p = .0013 

% Rural Area 0.03706 p = .0002 

% Alcohol ≥ 0.082 0.10160 p = .0032 

(Per Cap Income) * 

 (% Unemployed) -5.69E10-8 p = .0011 

(% Employed) * 

 (% Restrained) -5.60E10-6 p = .0006 

(% Employed) * 

 (% Alcohol ≥ 0.08) -0.00997 p = .0144 

(% Employed) * 

 (% Restrained) 7.64E10-5 p < .0001 

Drivers/Area 0.01235 p = .0390 

Area/Population 0.00042 p = .0053 

Area/Drivers -0.00031 p = .0062 

1/(% Minority) -0.02658 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

17

Fatalities Divided by Licensed Drivers

Table A4: Fatalities Divided by Licensed 

Drivers Good Model – State 

Characteristics Only 

Independent 

Variables Coefficients R2

 = 0.83 

VMT/Drivers 0.01421 p < .0001 

Area/Population 0.00202 p = .0055 

Area/Vehicles -0.00165 p = .0323 

% Minority 0.00428 p = .0003 

% Minority2 1.45E10-5 p = .0037 

% Unemployed2 9.24E10-4 p = .0001 

Rural Area2 -9.65E10-13 p = .0235 

Per Cap Income 8.62E10-6 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Table A5: Fatalities Divided by Licensed 

Drivers Better Model – State 

Characteristics With Alcohol 

Independent 

Variables Coefficients R2

 = 0.84 

VMT/Drivers 0.01179 p = .0240 

VMT/Population 0.01356 p = .0491 

VMT/Vehicles -0.00751 p = .0268 

% Rural Lane Miles 0.20642 p < .0001 

% Minority2 -2.79E10-5 p = .0204 

(Per Cap Income) * 

 (% Rural Area) -3.76E10-6 p = .0056 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.01010 p < .0001 

% Employed -0.00726 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Table A6: Fatalities Divided by Licensed 

Drivers 

Best – State Characteristics With 

Alcohol and Safety Belt Use 

Independent 

Variables Coefficients R2

 = 0.91 

VMT/Drivers 0.01322 p < .0001 

Area/Population 0.00237 p = .0002 

Area/Vehicles -0.00206 p = .0016 

% Rural Area 0.33236 p < .0001 

% Minority2 -5.34E10-5 p = .0001 

% Alcohol ≥ 0.08 -0.27909 p = .0464 

Rural Area2 -9.18E10-13 p = .0235 

(Per Cap Income) * 

 (% Rural Area) -5.85E10-6 p = .0022 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.01414 p < .0001 

(% Unemployed) * 

 (% Unrestrained) 2.82E10-4 p = .0014 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

18

Fatalities Divided by Registered Vehicles 

Table A7: Fatalities Divided by 

Registered Vehicles 

Good Model – State Characteristics Only 

Independent 

Variables Coefficients R2

 = 0.86 

VMT/Vehicles 0.01426 p < .0001 

Area/Population 0.00145 p = .0050 

Area/Vehicles -0.00138 p = .0060 

% Rural Lane Miles 0.18152 p < .0001 

Minority 0.00364 p = .0004 

Minority2 -1.99E10-5 p = 0.160 

% Employed -0.00447 p = .0026 

(Per Capita Inc) * 

 (% Rural Area) -4.65E10-6 p = .0003 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Table A8: Fatalities Divided by 

Registered Vehicles 

Better Model – 

 State Characteristics With Alcohol 

Independent 

Variables Coefficients R2

 = 0.91 

VMT/Vehicles 0.01473 p < .0001 

Area/Vehicle 0.00109 p = .0358 

Area/Population 0.00141 p = .0043 

% Rural Lane Miles 0.12657 p = .0007 

% Employed -0.00519 p = .0006 

Per Capita Income -1.24E10-5 p = .0082 

% Alcohol ≥ 0.08 -0.82876 p = .0215 

% Minority2 -3.86E10-5 p = .0007 

% Area2 -8.55E10-13 p = .0089 

(Per Capita Inc) * 

 (% Alcohol ≥ 0.08) 2.65E10-5 p = .0331 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.01192 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

19

Fatalities Divided by Registered Vehicles Continued

Table A9: Fatalities Divided by 

Registered Vehicles 

Best Model – State Characteristics With 

Alcohol and Safety Belt Use 

Independent 

Variables Coefficients R2

 = 0.91 

VMT/Population 0.00810 p = .0088 

VMT/Vehicle 0.01086 p < .0001 

% Rural Area 0.28177 p < .0001 

% Minority2 2.66E10-5 p = .0095 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.01824 p < .0001 

(% Minority) * 

 (% Rural Area) -0.00384 p = .0001 

(% Employed) * 

 (% Alcohol ≥ 0.08) -0.00541 p = .0001 

% Unrestrained -0.01336 p = .0035 

% Unrestrained2 1.02E10-4 p = .0041 

% Unemployed2 -0.00273 p = .0128 

(% Unemployed) * 

 (% Unrestrained) 7.68E10-4 p = .0018 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

20

Fatalities Divided by Population 

Table A10: Fatalities Divided by 

Population 

Good Model – State Characteristics Only 

Independent Variables Coefficients R2 = 0.86 

VMT/Population 0.01495 p < .0001 

Area/Population 0.00151 p = .0035 

Area/Vehicles -0.00116 p = .0326 

% Minority 0.00260 p = .0024 

% Minority2 -2.30E10-5 P = .0307 

% Employed -0.00298 p = .0378 

Per Cap Income -4.75E10-6 p = .0004 

Rural Area2 -7.58E10-13 P = .0275 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Table A11: Fatalities Divided by 

Population 

Better Model – State Characteristics With 

Alcohol 

Independent Variables Coefficients R2 = 0.89 

VMT/Population 0.01460 p < .0001 

Area/Population 0.00155 p = .0005 

Area/Vehicles -0.00119 p = .0107 

% Rural Lane Miles 0.12932 p = .0001 

% Employed -0.00612 p < .0001 

% Minority2 2.44E10-5 p = .0020 

Rural Area2 -8.69E10-13 p = .0035 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.00801 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 

Table A12: Fatalities Divided by 

Population 

Best Model – State Characteristics With 

Alcohol and Safety Belt Use 

Independent Variables Coefficients R2 = 0.91

VMT/Population 0.01362 p < .0001 

Area/Population 0.00183 p < .0001 

Area/Vehicles -0.00153 p = .0008 

% Rural Lane Miles 0.09445 p = .0008 

% Minority2 -3.17E10-5 p = .0008 

Rural Area2 -6.93E10-13 p = .0189 

(Per Cap Income) * 

 (% Alcohol ≥ 0.08) -4.83E10-6 p = .0258 

(% Minority) * 

 (% Alcohol ≥ 0.08) 0.00984 p < .0001 

(% Employed) * 

 (% Unrestrained) -6.90E10-5 p = .0004 

% Unrestrained 0.00525 p < .0001 

Source: NHTSA/NCSA, FHWA, US 

Census/SAS 
Appendix B 

Variables Associated With Increasing Fatalities by State 

Legend 

NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

21
 Table B1 Variables Associated With Increasing Fatalities by State

NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

22
NHTSA’s National Center for Statistics and Analysis 400 Seventh St., SW., Washington, DC 20590 

23

Bibliography 

Glassbrenner, Donna, Safety Belt Use in 2003 – Use Rates in the States and Territories DOT HS 809 713, National 

Highway Traffic Safety Administration, Washington, DC, March 2004. 

Hosmer, D. W.. and Lemeshow, S., Applied Logistic Regression, John Wiley and Sons 1989. 

Tessmer, J. M., FARS Analytic Reference Guide 1975-2002, DOT HS 808 463, National Highway Traffic Safety 

Administration, Washington, D.C., June 2002. 

Traffic Safety Facts 2000, DOT HS 809 337, National Highway Traffic Safety Administration, April 2002. 

Traffic Safety Facts 2001, DOT HS 809 484, National Highway Traffic Safety Administration, December 2002. 

Traffic Safety Facts 2002, DOT HS 809 620, National Highway Traffic Safety Administration, January 2004. 

For additional copies of this technical report, please call 800-934-8517 or fax your request to 202-366-

3189. For questions regarding the data reported in this research, call Joseph Tessmer, 202-366-5820. 

This technical report and other general information on highway traffic safety may be found at: 

www-nrd.nhtsa.dot.gov/departments/nrd-30/ncsa/AvailInf.html. 

U.S. Department 

of Transportation

National Highway 

Traffic Safety 

Administration 

400 Seventh Street SW., NPO-100 

Washington, DC 20590
DOT HS 809 870 

November 2005 

National Highway Traffic Safety Administration’s 

National Center for Statistics and Analysis