I

Tool 

Costs and Effectiveness 

of Interventions to Reduce 

Motor Vehicle–Related Injuries 

and Deaths 

Supplement to Tool Documentation 

Liisa Ecola, Jeanne S. Ringel, Kathryn Connor, David Powell, 

Connor P. Jackson, Paul Ng, Candice Miller 

C O R P O R A T O N
For more information on this publication, visit www.rand.org/t/tl144z2 

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Preface 

In December 2015, RAND Corporation researchers produced an online tool called the Motor 

Vehicle Prioritizing Interventions and Cost Calculator for States (MV PICCS) for the National 

Center for Injury Prevention and Control at the Centers for Disease Control and Prevention 

(CDC). The purpose of this tool, which is available to the public at 

www.cdc.gov/motorvehiclesafety/calculator/ (CDC, 2015), is to support states and local 

communities in making evidence-based resource allocation decisions relating to the 

implementation of effective evidence-based interventions for preventing motor vehicle–related 

injury. 

In 2017, the team completed an update to the tool as part of a project led by the National 

Governors Association, funded by CDC, to work with states and governors’ offices to strengthen 

strategies to reduce highway and traffic injuries and fatalities. The work includes updates to 

some of the data used, updates to some of the assumptions and methods to derive the data, and a 

full redesign of the user interface of the tool. This report documents which data, assumptions, 

and methods we updated. The audience for this report is the users of the online tool, state and 

local health and safety officials seeking information on the effectiveness and costs of the 

14 motor vehicle injury prevention interventions included in MV PICCS. 

The more complete documentation, which includes discussion of how we selected 

interventions for inclusion in the tool and how we programmed the tool, is available in Ringel 

et al., 2015. 

RAND researchers have also undertaken related work that used the data in the tool to 

conduct policy analyses of traffic safety spending. This work was sponsored by the Robert Wood 

Johnson Foundation. The main report is Ecola, Batorsky, and Ringel, 2015. 

Four research briefs highlight those analyses: 

• Liisa Ecola, Benjamin Saul Batorsky, Jeanne Ringel, Johanna Zmud, Kathryn Connor, 

David Powell, Brian G. Chow, Christina Panis, and Gregory S. Jones, Which Behavioral 

Interventions Are Most Cost-Effective in Reducing Drunk Driving? Santa Monica, Calif.: 

RAND Corporation, RB-9826, 2015a 

• Liisa Ecola, Benjamin Saul Batorsky, Jeanne Ringel, Johanna Zmud, Kathryn Connor, 

David Powell, Brian G. Chow, Christina Panis, and Gregory S. Jones, A New Tool to 

Help Decisionmakers Select Interventions to Reduce Traffic Crash Deaths and Injuries, 

Santa Monica, Calif.: RAND Corporation, RB-9827, 2015b 

• Liisa Ecola, Benjamin Saul Batorsky, Jeanne Ringel, Johanna Zmud, Kathryn Connor, 

David Powell, Brian G. Chow, Christina Panis, and Gregory S. Jones, How to Get the 

Biggest Impact from an Increase in Spending on Traffic Safety, Santa Monica, Calif.: 

RAND Corporation, RB-9855, 2015c 

iii 
• Liisa Ecola, Benjamin Saul Batorsky, Jeanne Ringel, Johanna Zmud, Kathryn Connor, 

David Powell, Brian G. Chow, Christina Panis, Gregory S. Jones, Should Traffic Crash 

Interventions Be Selected Nationally or State by State? Santa Monica, Calif.: RAND 

Corporation, RB-9860, 2015d. 

The research reported here was conducted jointly in RAND Health and RAND Infrastructure 

Resilience and Environmental Policy. Questions or comments about this report should be sent to 

the project leader, Jeanne Ringel (Jeanne_Ringel@rand.org). 

RAND Health

RAND Health is a division of the RAND Corporation. It is one of the largest private health 

research groups in the world. Between 250 and 300 projects are currently under way, addressing 

a wide range of health care policy issues; the agenda emphasizes policy research that can 

improve the health of people around the world. RAND Health research studies are coordinated 

through two programs that focus on long-standing core areas of RAND Health’s policy research 

expertise: Health Services Delivery Systems and Population Health. Our work in Health Services 

Delivery Systems addresses issues surrounding the delivery of health care services, including the 

cost and quality of services, patient access, experience, and outcomes; the impact of new models 

of health care payment and delivery; medical workforce trends; and the impact that technological 

innovation has on service delivery and patient health. Our work in Population Health addresses 

public health issues, including social, environmental, and cultural influences on the health of 

populations; health inequities and disparities among different populations; community and 

population resilience; and relationships among environmental factors and individual health 

behaviors (e.g., eating habits, smoking) that influence the prevalence of chronic disease. 

A profile of RAND Health, abstracts of its publications, and ordering information can be 

found at www.rand.org/health. 

RAND Infrastructure, Resilience, and Environmental Policy

RAND Infrastructure Resilience and Environmental Policy performs analyses on 

urbanization and other stresses. This includes research on infrastructure development; 

infrastructure financing; energy policy; urban planning and the role of public–private 

partnerships; transportation policy; climate response, mitigation, and adaptation; environmental 

sustainability; and water resource management and coastal protection. Program research is 

supported by government agencies, foundations, and the private sector. 

This program is part of RAND Justice, Infrastructure, and Environment, a division of the 

RAND Corporation that conducts research and analysis in civil and criminal justice, 

infrastructure development and financing, environmental policy, transportation planning and 

technology, immigration and border protection, public and occupational safety, energy policy, 

iv 
science and innovation policy, space, telecommunications, and trends and implications of 

artificial intelligence and other computational technologies. 

For more information about RAND Infrastructure Resilience and Environmental Policy, see 

www.rand.org/jie/irep or contact the director at irep@rand.org.

v 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Contents 

Preface............................................................................................................................................iii 

Figures............................................................................................................................................ ix 

Tables............................................................................................................................................. xi 

Summary......................................................................................................................................xiii 

Acknowledgments....................................................................................................................... xvii 

Abbreviations............................................................................................................................... xix 

Chapter One. Introduction .............................................................................................................. 1 

Project Objectives......................................................................................................................................1 

Chapter Two. Selecting Interventions............................................................................................. 5 

Chapter Three. Changes to Effectiveness Data and Analysis....................................................... 11 

Numbers of Deaths and Injuries per State...............................................................................................11 

New Methods for Counting Total Deaths ...........................................................................................11 

New Methods for Estimating Total Injuries........................................................................................12 

The Effect on Estimates of Lives Saved and Injuries Prevented.............................................................13 

The Monetary Value of Saving a Life or Preventing an Injury...............................................................14 

Estimates of Monetary Value..............................................................................................................14 

The Effect of These Adjustments on Estimates of Monetary Value of Lives Saved and Injuries 

Prevented ......................................................................................................................................15 

Chapter Four. Cost Data, Estimates, and Analysis ....................................................................... 21 

Updated Cost Inputs and Assumptions ...................................................................................................21 

Inflation ...............................................................................................................................................21 

Wages ..................................................................................................................................................22 

Updated Data on Populations ..................................................................................................................22 

Population............................................................................................................................................22 

The Number of Licensed Drivers........................................................................................................22 

The Number of Registered Motorcycles .............................................................................................23 

Driving-While-Intoxicated Arrest Data...................................................................................................23 

A New Method of Calculating Driving-While-Intoxicated Arrests....................................................23 

The Method’s Effect on Cost Estimates..............................................................................................25 

Automated Enforcement..........................................................................................................................26 

New Assumptions About the Number of Cameras .............................................................................26 

The Assumptions’ Effect on Cost Estimates.......................................................................................26 

Chapter Five. Tool Redesign ........................................................................................................ 29 

Map..........................................................................................................................................................29 

Selecting Interventions ............................................................................................................................30 

Model Outputs.........................................................................................................................................31 

Fines and Fees .........................................................................................................................................33 

vii 
 

 

 

Sensitivity Analysis.................................................................................................................................34 

Key Results and Graphics .......................................................................................................................36 

Additional Intervention Information .......................................................................................................40 

Chapter Six. Limitations and Future Refinements........................................................................ 45 

Possible Future Refinements ...................................................................................................................46 

Conclusion...............................................................................................................................................46 

Appendix A. Interventions Considered but Not Included ............................................................ 49 

Appendix B. Productivity and Medical Adjustments to the Value of a Life................................ 61 

Bibliography ................................................................................................................................. 65 

viii 
Figures 

Figure 5.1. The Input Screen with Map ........................................................................................ 30 

Figure 5.2. Intervention Selection................................................................................................. 31 

Figure 5.3. The Intervention Details Table................................................................................... 33 

Figure 5.4. Sensitivity Analysis Inputs......................................................................................... 35 

Figure 5.5. Call-Out Box with Model Run Results ...................................................................... 36 

Figure 5.6. Pie Charts of Benefits................................................................................................. 38 

Figure 5.7. Bar Chart of Implementation Costs and Fines and Fees ............................................ 39 

ix 

 

 

Tables 

Table S.1. Summary of Changes to MV PICCS.......................................................................... xvi 

Table 1.1. Summary of Changes to MV PICCS ............................................................................. 3 

Table 2.1. Interventions for Analysis.............................................................................................. 5 

Table 2.2. Intervention Status, by State .......................................................................................... 8 

Table 3.1. Injury-to-Fatality Ratios for MV PICCS 2.0 and 3.0 .................................................. 13 

Table 3.2. Average, Low, and High Values of State-Specific Price Multipliers, of Saving a 

Life, and of Preventing an Injury.......................................................................................... 15

Table 3.3. Comparison of Benefits Between MV PICCS 2.0 and 3.0 for Georgia, Based on 

Changes to Three Inputs ....................................................................................................... 17

Table 4.1. Federal Bureau of Investigation–Reported and –Estimated Driving-WhileIntoxicated Arrests, by State, 2015....................................................................................... 24

Table 4.2. Comparison of Costs Between MV PICCS 2.0 and 3.0 for Georgia, Based on 

Changes to Inputs and Assumptions..................................................................................... 27

Table A.1. State Bans on Cell Phone Use, 2017........................................................................... 53

Table B.1. State-Specific Price Multipliers and Values of Saving a Life and Preventing an 

Injury..................................................................................................................................... 62 

xi 

 

 

 

 

 

 

 

Summary 

Motor vehicle crashes account for a large number of deaths and injuries. In the United States, 

in 2015, more than 35,000 people were killed and approximately 2.44 million were injured in 

motor vehicle crashes (National Center for Statistics and Analysis, 2016b). Crash deaths rose yet 

again in 2016, to more than 37,000 (National Center for Statistics and Analysis, 2017). In 2010, 

the economic costs associated with motor vehicle crashes in the United States were substantial, 

estimated to be $242 billion (Blincoe et al., 2015). Fortunately, a wide range of evidence-based 

interventions, including both policies and programs, can help prevent motor vehicle–related 

injuries and deaths. 

In 2014, RAND researchers developed, for the Centers for Disease Control and Prevention 

(CDC), an online tool for decisionmakers—primarily state health, transportation, and safety 

officials—to use in determining the costs and effectiveness of various interventions to reduce 

injuries and deaths from motor vehicle crashes and in determining what interventions together 

generate the largest reductions in the numbers of injuries and deaths for a given implementation 

budget. The tool is called Motor Vehicle Prioritizing Interventions and Cost Calculator for States 

(MV PICCS) and is available at www.cdc.gov/motorvehiclesafety/calculator/ (CDC, 2015). 

Developing MV PICCS required collecting and analyzing data related to the selection of 

interventions, their effectiveness, and their cost (specific to each state), as well as programming 

the tool to select interventions based on a specified budget and the cost-effectiveness of 

interventions in a specific state. 

Since 2014, we have updated the tool twice. The first update was released in 2015; it added 

two new interventions and produced a series of reports about allocating traffic safety funds 

(listed in the preface). In this report, we refer to this update (the interventions and reports 

together) as MV PICCS 2.0. 

In 2017, we conducted the second update, described in this report, for the National 

Governors Association (NGA), working with the National Center for Injury Prevention and 

Control at CDC. NGA commissioned RAND to update the MV PICCS tool, with the following 

goals: 

• Determine whether new interventions should be added, based on the most-recent 

evidence available. 

• Update information on the effectiveness of interventions. 

• Update cost data. 

• Redesign the tool’s user interface to be more user-friendly. 

This documentation summarizes the changes made to MV PICCS in these four areas. We 

refer to this most recent update as MV PICCS 3.0. 

xiii 
 

 

 

New Interventions 

We reviewed seven interventions for potential addition to the tool. We considered adding one 

new intervention out of those: lowering the legal limit for a driving-while-intoxicated (DWI) 

charge from a blood alcohol concentration of 0.08 to 0.05 g per deciliter. However, following 

discussions with NGA and CDC, we opted not to include it, for two main reasons: It would be 

difficult to (1) develop a specific estimate of how many lives could be saved and (2) incorporate 

it with similar drunk-driving interventions (sobriety checkpoints and saturation patrols). A more 

detailed discussion of the findings from our literature review of studies on blood alcohol 

concentration can be found in Appendix A. 

During the research for the 2015 update, we reviewed the literature on cell phone and texting 

bans to prevent distracted driving. At the time, we determined that the evidence was not strong 

enough to include these interventions in the tool. We also scanned the literature in 2017 to 

determine whether the research findings suggested any new understanding of their effectiveness, 

and we found that the research was still mixed. This review is provided in Appendix A. 

Effectiveness Data Updates

Given the extensive review we conducted during the original research and the 2015 update, 

we did not update any of the effectiveness data (the percentage by which the number of deaths 

due to specific crash causes would decline if the intervention were implemented). We did update 

the state-specific data on the number of deaths in different crash categories by state, as well as 

our estimates of injuries by state, with the most-recent data available, 2015. These updates 

provide more-realistic estimates because most states have experienced an increase in traffic crash 

deaths from 2010 to 2015. We also believe that our current method for estimating injuries 

provides a more accurate number of injuries per state. 

Cost Data Updates

We made some changes to the data that determine the cost per intervention. These include 

adjusting all dollar figures to 2017 dollars and using more-recent data for cost elements, such as 

state employee wages, number of drivers by state, and the value of saving one life. We also made 

two more-significant changes. First, because of known undercounting in the data, we adjusted 

figures for the number of people arrested for DWI. The new figures should provide a more 

realistic estimate of the number of arrests, which leads to more-realistic implementation costs, 

especially in those states where undercounts were very pronounced. Second, we updated our 

method for estimating the number of red-light and speed cameras assumed to be deployed in 

each state. This resulted in reductions in the estimated number of cameras, which translates into 

lower costs for these two interventions in all states. Both of these updates should lead to morerealistic estimates. 

xiv 
 

 

 

 

User Interface Redesign

Finally, we completely redesigned the user interface to streamline it and make it more 

intuitive to use. This includes the way in which users enter information and the way the tool 

displays the results. We have also created links within the tool to fact sheets about each 

intervention and a new feature that allows the user to generate a Portable Document Format 

version of the MV PICCS results. We believe that the user interface is now more intuitive in 

terms of inputting data, as well as in providing outputs that are easier to interpret and share. 

Table S.1 summarizes the changes made to MV PICCS. 

xv 
Table S.1. Summary of Changes to MV PICCS

Methods and Data from MV PICCS 2.0 MV PICCS 3.0 Revisions to Methods and Data

Interventions 

Identified 14 interventions No change to the list of interventions

 Updated information on which interventions are currently implemented 

for each state

Effectiveness 

Informed by the literature, developed an No change to these estimates 

estimate of the percentage of lives 

saved by each intervention 

Identified the number of people killed in Updated FARS data from 2010 to 2015 

each state, by crash type, using 2010 

FARS data 

Estimated the number of people injured Used a different method to develop injury estimates and applied it 

in each state, by crash type using FARS and General Estimates System data from 2015

Estimated the monetary value of lives Updated state-specific adjustment factors and updated costs to 2017

saved and injuries prevented, by state dollarsX no change to underlying injury and fatality dollar estimates

Costs 

Developed ten cost components No change to the list of cost components

Defined cost components for each No change to cost components per intervention 

intervention 

Developed cost estimates for each Updated all costs to 2017 dollars 

intervention 

 Updated state-specific cost elements (wage rates, population, number 

of licensed drivers, and number of registered motorcycles) to the most 

recent year available

 Developed a new method to estimate the number of DWI arrests per 

state, which affects the costs of four DWI-related interventions

 Revised the method of estimating the number of red-light and speed 

cameras per state, resulting in lower costs

Online tool 

Created a user interface with two types Combined both forms of analysis into one model run with one 

of analysis (cost-effectiveness analysis complete set of cost-effectiveness outputs that provides a summary of 

and portfolio analysis) the portfolio analysis results

Updated ways for users to supply data Streamlined data inputs and changed the order so that users input the 

inputs desired parameters before model runsX changed defaults to 

unimplemented interventions and excluded fines and feesX provided a 

state map

Streamlined the sensitivity analysis, Streamlined sensitivity analysis such that users can change only two 

allowing users to change some default types of inputs and allowed users to access sensitivity inputs on any 

inputs model run

 Created additional graphics to display information, and provided links 

to information on each intervention

NOTE: FARS = Fatality Analysis Reporting System. 

xvi 
 

Acknowledgments 

Many thanks to our sponsors at the National Governors Association: Jeffrey Locke, who 

managed the project for the association, and staff members Kalyn Hill and Lauren Dedon. They 

provided valuable insights based on their work with state users of the tool. 

We also thank the Centers for Disease Control and Prevention (CDC) officials and staff who 

provided technical assistance. Erica Spies, our main contact, kept us on track, answered every 

question patiently, and provided extremely good advice. Erin K. Sauber-Schatz, our liaison for 

the previous work on the Motor Vehicle Prioritizing Interventions and Cost Calculator for States, 

provided important continuity and institutional memory. Jessica Burke, Melvin Crum, Kevin 

Webb, and Nimeshkumar Patel helped us understand the technical requirements for 

programming the website to be compatible with CDC infrastructure and transferring it to CDC. 

Kristin Belcher and Yamile Underwood assisted with thinking through some of the best ways to 

communicate the information in the tool to its users. 

Tonja Lindsey and Ruby Li at the National Highway Traffic Safety Administration walked 

us through some of the statistical complexities of fatality and injury data. 

At RAND, we thank Michael Robbins, who developed a statistical method to assist with our 

data analysis, as well as Rosanna Smart, Janet M. Hanley, and Sascha Ishikawa for their 

thoughtful and constructive reviews of the report and the tool. Their input improved the final 

product significantly. 

xvii 

Abbreviations 

BAC blood alcohol concentration 

BLS Bureau of Labor Statistics 

CDC Centers for Disease Control and Prevention 

DMV department of motor vehicles 

DOT U.S. Department of Transportation 

DWI driving while intoxicated 

FARS Fatality Analysis Reporting System 

FBI Federal Bureau of Investigation 

GES General Estimates System 

GHSA Governors Highway Safety Association 

HVE high-visibility enforcement 

IIHS Insurance Institute for Highway Safety 

MV PICCS Motor Vehicle Prioritizing Interventions and Cost Calculator for States 

NGA National Governors Association 

NHTSA National Highway Traffic Safety Administration 

PDF Portable Document Format 

RBT random breath testing 

WISQARS Web-Based Injury Statistics Query and Reporting System 

xix 
 

 

 

 

 

Chapter One. Introduction 

Project Objectives

Traffic crashes have long been a leading cause of death in the United States, especially 

among people ages 16 to 25 (National Center for Statistics and Analysis, 2016a). After years of 

declining, traffic fatalities began to rise in 2015, when 35,092 people were killed and 

2.44 million were injured on the nation’s roadways (National Highway Traffic Safety 

Administration [NHTSA], 2017). Crash deaths rose yet again in 2016, to more than 37,000 

(National Center for Statistics and Analysis, 2017).1 In 2010, the economic costs associated with 

motor vehicle crashes were substantial, estimated to be $242 billion (Blincoe et al., 2015). 

Fortunately, a wide range of evidence-based interventions, including both policies and programs, 

can help prevent motor vehicle–related injuries and deaths. 

In the United States, many of the available evidence-based interventions to prevent motor 

vehicle–related injuries can be implemented at the state level. Given limited resources for 

implementing interventions, states must prioritize interventions and choose those that will give 

them the greatest reduction in injuries and deaths for their implementation dollars. To do this 

prioritization, states require state-specific information on the costs and effects of the 

interventions. Although considerable evaluation work has identified evidence-based 

interventions to prevent motor vehicle–related injuries and estimated the costs of motor vehicle– 

related injuries and deaths in the United States, little has been done to identify the levels of 

economic resources needed to implement these interventions. Consequently, decisionmakers—a 

term we use broadly throughout this report to include state health, transportation, and safety 

officials, as well as other officials who might use the tool2

—cannot fully assess the costs and 

effects of different interventions and select the most cost-effective ones. 

The purpose of the original project was to support states and local communities across the 

United States in making evidence-based resource allocation decisions related to the 

implementation of effective interventions to prevent motor vehicle–related injuries and deaths. 

We achieved this by building an interactive online tool called the Motor Vehicle Prioritizing 

Interventions and Cost Calculator for States (MV PICCS), available at 

www.cdc.gov/motorvehiclesafety/calculator/ (Centers for Disease Control and Prevention 

1 This document does not include data on injuries for 2016. 

2 The full list of potential decisionmakers could include governors, state legislators, state public safety secretaries, 

state public health commissioners, state transportation secretaries, governors advisers (criminal justice, health, 

transportation), and state highway safety officials. 

1 
 

 

 

 

[CDC], 2015). Since the most recent update in December 2015,3 states have been able to assess 

state-specific costs and effects of different interventions designed to prevent motor vehicle– 

related injuries and deaths and to select interventions that are most effective for a given 

implementation budget. 

We conducted the original project in five steps: 

1. We selected a set of evidence-based interventions based on systematic reviews and 

evaluated against a set of predetermined criteria. 

2. We examined existing literature to estimate the interventions’ effectiveness in reducing 

injuries and deaths and followed the National Cooperative Highway Research Program 

Report 622 (Preusser et al., 2008) methodology in estimating the effect for each 

individual state. 

3. We extended a methodology used to estimate the costs of motor vehicle–related injuries 

at the national level to account for state-level variation in these costs and calculated these 

costs for each state to account for state-level variation. 

4. We developed a methodology to estimate the cost components and subcomponents for 

implementing each intervention through a review of the literature to identify existing 

implementation cost estimates and scaled the costs to the state level. 

5. We built an online tool that state decisionmakers could use to generate a variety of statespecific cost-effectiveness analyses, whose outcomes include estimates for costs and 

effectiveness, automatic prioritization according to effectiveness–cost ratio, and selection 

of the most beneficial package of interventions to implement for a given budget 

according to portfolio analysis. 

In 2017, the National Governors Association (NGA), working with the National Center for 

Injury Prevention and Control at CDC, commissioned RAND to update the MV PICCS tool, 

with the following goals: 

• Determine whether new interventions should be added, based on the most-recent 

evidence available. 

• Update annual or regularly updated data sources to reflect more-recent circumstances. 

• Update other data sources as needed. 

• Redesign the tool’s user interface to be more user-friendly. 

The revision provided an opportunity to revisit some of our earlier methods and assumptions 

and make changes as needed to make the tool as useful as possible. Table 1.1 summarizes the 

changes made to MV PICCS. 

3 The first version of MV PICCS was publicly released in October 2014. The December 2015 update added two new 

interventions and updated some data but did not change the underlying methods or the user interface of the tool. We 

refer to the 2015 update as MV PICCS 2.0 and this most recent update as MV PICCS 3.0. 

2 
Table 1.1. Summary of Changes to MV PICCS

Methods and Data from MV PICCS 2.0 MV PICCS 3.0 Revisions to Methods and Data

Interventions 

Identified 14 interventions No change to the list of interventions

 Updated information on which interventions are currently implemented 

for each state

Effectiveness 

Informed by the literature, developed an No change to these estimates 

estimate of the percentage of lives 

saved by each intervention 

Identified the number of people killed in Updated FARS data from 2010 to 2015 

each state, by crash type, using 2010 

FARS data 

Estimated the number of people injured Used a different method to develop injury estimates and applied using 

in each state, by crash type FARS and GES data from 2015

Estimated the monetary value of lives Updated state-specific adjustment factors and updated costs to 2017

saved and injuries prevented, by state dollarsX no change to underlying injury and fatality dollar estimates

Costs 

Developed ten cost components No change to the list of cost components

Defined the cost components for each No change to cost components per intervention 

intervention 

Developed cost estimates for each Updated all costs to 2017 dollars 

intervention 

 Updated state-specific cost elements (wage rates, population, number 

of licensed drivers, and number of registered motorcycles) to the most 

recent year available

 Developed a new method to estimate the number of DWI arrests per 

state, which affects the costs of four DWI-related interventions

 Revised the method of estimating number of red-light and speed 

cameras per state, resulting in lower costs

Online tool 

Created a user interface with two types Combined both forms of analysis into one model run with one 

of analysis (cost-effectiveness analysis complete set of cost-effectiveness outputs that provides a summary of 

and portfolio analysis) the portfolio analysis results

Updated ways for users to supply data Streamlined data inputs and changed the order so that users input the 

inputs desired parameters before model runsX changed defaults to 

unimplemented interventions and excluded fines and feesX provided a 

state map

Streamlined sensitivity analysis allowing Streamlined sensitivity analysis such that users can change only two 

users to change some default inputs types of inputs, and allowed users to access sensitivity inputs on any 

model run

 Created additional graphics to display information and provided links to 

information on each intervention

NOTE: FARS = Fatality Analysis Reporting System. GES = General Estimates System. DWI = driving while 

intoxicated.

3 
 

The remaining chapters discuss the changes made to MV PICCS. Chapter Two discusses 

why we chose not to add any new interventions and explains how we updated the information 

about which interventions are implemented in which states. Chapter Three discusses our updates 

to the data on effectiveness and their overall effect on the model output. Chapter Four discusses 

updates to the cost estimates and their overall effect on the model output. Chapter Five explains 

our updates to the user interface. Chapter Six reviews the tool’s limitations and potential future 

refinements. Appendix A provides more information about the interventions we reviewed. 

Appendix B lists the values we assigned to prevented injuries and deaths. 

4 
 

Chapter Two. Selecting Interventions 

We selected 14 interventions for MV PICCS 2.0, as shown in Table 2.1. 

Table 2.1. Interventions for Analysis

Intervention Short Name Description

Automated red light– Red-light 

camera enforcement camera

Automated speed- Speed camera

camera enforcement

Alcohol interlocks Alcohol 

interlocks 

Sobriety checkpoints Sobriety 

checkpoints 

Saturation patrols Saturation 

patrols 

Bicycle helmet law Bicycle helmet

for children

Universal motorcycle Motorcycle 

helmet law helmet

Primary enforcement Primary 

seat belt law enforcement

seat belt law

High-visibility Seat belt 

enforcement for seat enforcement 

belts and child campaign

restraint law

License plate License plate 

impoundment impoundment

This camera captures an image of a vehicle whose driver fails to stop for a 

red light. Tickets are generally sent to offenders by mail.

This camera captures an image of a vehicle whose driver is driving in 

excess of the posted speed limit. Unlike red-light cameras, which are 

deployed only at intersections, mobile speed cameras are often used to 

cover multiple road segments.

This device prevents a vehicle from starting until the driver has blown into a 

tube and the device determines that the driver’s BAC is below the stateallowed level (0.02 in most jurisdictions). This intervention calls for 

interlocks to be installed on the vehicles of convicted repeat DWI offenders, 

as well as high-BAC and first offenders, depending on state legislation.

Here, teams of police officers stop cars at a specific location to check 

drivers for alcohol levels. States generally publicize such events to 

discourage drivers from drinking, particularly during times when drunk 

driving is more common than usual (such as holiday weekends).

This consists of an increased police presence in selected locations where 

police patrol the area looking for suspicious driving behavior. In contrast to 

sobriety checkpoints, they do not stop every vehicle.

To reduce the likelihood of trauma to the head and its related 

consequences, this mandates the use of helmets by children while they are 

riding bicycles.

This law requires all motorcyclists, regardless of age or experience level, to 

wear a helmet that meets safety standards set by DOT. It contrasts with 

partial helmet laws, which typically apply only to riders below a certain age.

States with seat belt laws vary in their enforcement. A primary law allows 

police to ticket an offender exclusively for not wearing a seat belt. A 

secondary law allows police to write a ticket for not wearing a seat belt only 

if the driver has been pulled over for a different offense.

High-visibility enforcement is a technique that combines intense 

enforcement over a fixed period (for example, one or two weeks) with a 

publicity campaign. A campaign focused on restraint use generally includes 

all forms of restraints: seat belts, child safety seats, and child restraints.

a

This intervention requires a driver who has been convicted of DWI to 

surrender the vehicle’s license plate, which is either impounded or 

destroyed. In some jurisdictions, the license plate is not physically removedX 

rather, the officer places a sticker on the license plate to indicate that it is 

invalid. The stickers are designed so that, if someone tries to remove one, it 

leaves a visible pattern on the plate. Because it is relatively easy for police 

to observe whether a vehicle has a license plate or the sticker, this 

intervention deters convicted DWI offenders from driving that vehicle.

5 
 

 

 

 

Intervention Short Name Description

Limits on diversion 

and plea agreements

Vehicle 

impoundment

In-person license 

renewal

Increased seat belt 

fines

Limits on 

diversion

Vehicle 

impoundment

In-person 

renewal

Increased seat 

belt fines

Although all states have penalties for DWI, many states have additional 

programs that allow some offenders to be diverted out of the normal 

procedures or to plead guilty to a lesser offense and receive a lighter 

sanction. These programs most often target first-time offenders, with the 

goal of reducing the DWI case load by diverting people who are thought to 

be unlikely to reoffend. Limits on diversion and plea agreements would 

increase the number of DWI arrestees convicted of more-serious DWIrelated charges.

This intervention results in the vehicle of a DWI offender being confiscated 

for a period of time and stored in a public impound lot. The offender can 

either reclaim or surrender the vehicle when the impoundment period ends.

This intervention requires all drivers over age 70 to renew their drivers’ 

licenses in person at DMVs, instead of using mail-in or online renewal.

This intervention adds $75 to a state’s existing fine, which represents a 

significant increase over existing seat belt fines in most states.

NOTE: BAC = blood alcohol concentration. DOT = U.S. Department of Transportation. DMV = department of motor 

vehicles.

a Child restraint includes both child car seats and booster seats. For simplicity, we refer generally to child restraint. 

Informed by our knowledge of the literature and in consultation with NGA and CDC, we 

reviewed seven potential new interventions for inclusion in the tool: DWI courts, vision testing 

at DMVs, cognitive screening at DMVs, referring older drivers to licensing agencies, 24/7 

sobriety programs, lowering BAC limits for repeat offenders only, and lowering the legal BAC 

limit from 0.08 to 0.05 for all DWI charges. With the exception of lower BAC limits for all DWI 

charges, our review of existing literature could not establish a strong enough evidence base to 

justify including any of these additional interventions. 

After the initial literature review, we intended to include lowering BAC limits from 0.05 to 

0.08 as a new intervention because several countries have adopted this level, and the National 

Transportation Safety Board has formally recommended its adoption (National Transportation 

Safety Board, 2013). However, following discussions with NGA and CDC, we opted not to 

include it, for two main reasons: 

• First, it is difficult to distill the existing evidence about its effectiveness into a single 

point estimate of the proportion of lives that would be saved with its implementation. 

Although there have been studies of lowering the BAC limit, many of them have been 

undertaken for different changes (for example, from 0.1 to 0.08) or the change in BAC 

has been accompanied by extensive enforcement efforts that make it difficult to separate 

out the effect of the actual lowered limit from that of the intensity of enforcement. 

• Second, our list of interventions already includes two that focus on identifying drunk 

drivers on the road: sobriety checkpoints and saturation patrols.4 Given that any 

assumptions about lowering the BAC would include a similar type of enhanced 

enforcement, it would be difficult to integrate this with our existing assumptions about 

4 We include both because some states forbid sobriety checkpoints. 

6 
 

 

 

 

 

 

the effectiveness of sobriety checkpoints and saturation patrols. We use two different 

estimates of fatality reduction: 18 percent for saturation patrols and 8.1 percent for 

sobriety checkpoints. If the model selects both, the sum is 26.1 percent. Because of the 

adjustment, the adjusted total reduction in fatalities is 24.8 percent, to account for the 

overlap between the two. This combination is already among the most-effective 

interventions and higher than almost all estimates of the effectiveness of lowering the 

BAC on its own. 

Appendix A provides a more detailed discussion of the findings from our literature review of 

BAC studies. 

During previous work on MV PICCS, we also looked at whether bans on cell phones and 

texting were effective. Informed by that review, we determined that the evidence of effectiveness 

was not strong enough to support inclusion in MV PICCS. Appendix A provides a fuller 

discussion of that evidence, updated with more-recent studies. 

The one change we made to the data about the interventions themselves was to update the list 

of which interventions are in use in which states, as shown in Table 2.2. We use an asterisk to 

indicate those that changed from the previous status. The data are current as of 2017 for all 

interventions except in-person license renewal (2016), license plate impoundment (2008), limits 

on diversion (2013), and vehicle impoundment (2008). For license plate impoundment and 

vehicle impoundment, we found no data updated since MV PICCS 2.0. We could not locate any 

information on the status of saturation patrols and seat belt enforcement campaigns. As in the 

prior version of the tool, users can change the default information about which interventions are 

currently implemented in the state, recognizing that these laws can change over time. 

7 
Table 2.2. Intervention Status, by State

State

Red-Light 

Camera

Speed 

Camera

Alcohol 

Interlock

Sobriety 

Checkpoint

Saturation 

Patrol

Bicycle 

Helmet

Motorcycle 

Helmet

Primary 

Enforcement 

Seat Belt 

Laws

Seat Belt 

Enforcement 

Campaign

License Plate 

Impoundment

Limits on 

Diversion

Vehicle 

Impoundment

In-Person 

License

Renewal

Higher Seat 

Belt Fines

Ala. 1 1 1 1 9 1 1 1 9 0 0 1 0 0 

Alaska 0 0 1 0 9 0 0 1 9 0 0 1 0 0 

Ariz. 1 1 1 1 9 0 0 0 9 0 1 1 0 0 

Ark. 0 0 1 1 9 0 0 1 9 1 1 0 1 0 

Calif. 1 0 0a 1 9 1 1 1 9 0 1 1 0 0 

Colo. 1 1 1 1 9 0 0 0 9 0 1 0 0 1 

Conn. 0 0 1 1 9 1 0 1 9 0 0 1 0 0 

Del. 1 0 1 1 9 1 0 1 9 1 0 1 0 0 

D.C. 1 1 1a 1 9 1 1 1 9 0 0 0 0 0 

Fla. 1 0 0a 1 9 1 0 1 9 0 1 1 0 0 

Ga. 1 0 0a 1 9 1 1 1 9 1 0 0 0 0 

Hawaii 0 0 1 1 9 1 0 1 9 1 0 0 0 0 

Idaho 0 0 0 0 9 0 0 0 9 0 0 0 1 0 

Ill. 1 1 1 1 9 0 0 1 9 1 0 1 0 0 

Ind. 0 0 0 1 9 0 0 1 9 0 0 0 0 0 

Iowa 1a 1a 0 0 9 0 0 1 9 1 0 1 0 0 

Kan. 0 0 1 1 9 0 0 1 9 1 1 1 1 0 

Ky. 0 0 0 1 9 0 0 1 9 1 1 0 1 0 

La. 1 1 1 1 9 1 1 1 9 0 0 0 0 0 

Maine 0 0 1a 1 9 1 0 1 9 1 0 1 1 0 

Md. 1 1 1 1 9 1 1 1 9 1 0 1 0 0 

Mass. 0 0 0a 1 9 1 1 0 9 1 0a 0 0 0 

8 
State

Red-Light 

Camera

Speed 

Camera

Alcohol 

Interlock

Sobriety 

Checkpoint

Saturation 

Patrol

Bicycle 

Helmet

Motorcycle 

Helmet

Primary 

Enforcement 

Seat Belt 

Laws

Seat Belt 

Enforcement 

Campaign

License Plate 

Impoundment

Limits on 

Diversion

Vehicle 

Impoundment

In-Person 

License

Renewal

Higher Seat 

Belt Fines

Mich. 0 0 0

a 0 9 0 

0

a 1 9 1 1 0 0 0 

Minn. 0 0 0

a 0 9 0 0 1 9 1 0 1 1 0 

Miss. 0 0 1

a 1 9 0 1 1 9 0 1 1 0 0 

Mo.1

a

1

a 1 

0

a 9 0 1 0 9 0 0 1 1 0 

Mont. 0 0 0

a 0 9 0 0 0 9 0 0 0 0 0 

Neb. 0 0 1 1 9 0 1 0 9 1 0 1 0 0 

Nev. 0 0 1

a 1 9 0 1 0 9 0 1 0 0 0 

N.H. 0 0 1 1 9 1 0 0 9 0 0 0 0 0 

N.J.0

a 0 

0

a 1 9 1 1 1 9 0 0 1 1 0 

N.M.1

a

1

a 1 1 9 1 0 1 9 0 1 1 1 0 

N.Y. 1 1

a 1 1 9 1 1 1 9 0 1 0 0 0 

N.C. 1 0 0

a 1 9 1 1 1 9 0 0 0 0 0 

N.D. 0 0 0 1 9 0 0 0 9 1 0 0 0 0 

Ohio1

a

1

a 0 1 9 0 0 0 9 1 0 0 1 0 

Okla. 0 0 0

a 1 9 0 0 1 9 0 0 0 1 0 

Ore. 1 1 1 0 9 1 1 1 9 0 1 1 0 1 

Pa. 1 0 0

a 1 9 1 0 

0

a 9 0 0 0 0 0 

R.I. 1 0 1

a 0 9 1 0 1 9 0 

0

a 0 0 0 

S.C. 0 0 0

a 1 9 0 0 1 9 0 0 0 0 0 

S.D. 0 0 0 1 9 0 0 0 9 0 0 0 0 0 

Tenn. 1 1

a 1 1 9 1 1 1 9 0 

0

a 0 0 0 

Texas 1 0 1 0 9 0 0 1 9 0 0 0 0 1 

Utah 0 0

a 1 1 9 0 0 1 9 0 

1

a 0 0 0 

Vt. 0 0 1

a 1 9 0 1 0 9 0 0 0 0 0 9 
State

Red-Light 

Camera

Speed 

Camera

Alcohol 

Interlock

Sobriety 

Checkpoint

Saturation 

Patrol

Bicycle 

Helmet

Motorcycle 

Helmet

Primary 

Enforcement 

Seat Belt 

Laws

Seat Belt 

Enforcement 

Campaign

License Plate 

Impoundment

Limits on 

Diversion

Vehicle 

Impoundment

In-Person 

License

Renewal

Higher Seat 

Belt Fines

Va. 1 0 1 1 9 0 1 0 9 0 0 1 0 0 

Wash. 1 1 1 0 9 0 1 1 9 0 0 1 0 1 

W.Va. 0 0 1 1 9 1 1 1a 9 0 0 0 0 0 

Wis. 0a 0 0a 0 9 0 0 1 9 0 0 1 0 0 

Wyo. 0 0 0a 0 9 0 0 0 9 0 1 1 0 0 

SOURCES: RedQlight and speed cameras, Insurance Institute for Highway Safety (IIHS), 2017d[ alcohol interlocks, IIHS, 2017b[ motorcycle helmets, IIHS, 2017c[ 

primary enforcement seat belt laws and higher seat belt fines, IIHS, 2017e[ bicycle helmets, Bicycle Helmet Safety Institute, 2017[ sobriety checkpoints, Governors 

Highway Safety Association (GHSA), undated (c)[ license plate and vehicle impoundment, McKnight et al., 2008[ limits on diversion and plea agreements, NHTSA, 

2017[ inQperson license renewal, AAA Foundation for Traffic Safety, 2016. 

NOTE: 1 = in force. 9 = unknown. 0 = not in force. a A change in implementation status since the documentation report for MV PICCS 2.0. 

10 
 

 

 

Chapter Three. Changes to Effectiveness Data and Analysis 

Effectiveness data refers to all of the data that help determine the numbers of lives saved and 

injuries prevented for a specific intervention in a specific state. Our estimates of effectiveness 

include four elements: 

• the calculation of the number of lives lost and injuries sustained in each state 

• for each intervention, the estimated effect on lives saved and injuries prevented 

• for each intervention, the estimated number of lives saved and injuries prevented for each 

state 

• the monetary value of saving a life or preventing an injury. 

To reach an overall estimate of the benefit of a particular intervention in a specific state, we 

multiply the estimated numbers of lives saved and injuries prevented by the monetary value. 

We did not change the second of these elements because we estimated them for MV 

PICCS 2.0 based on an extensive literature review. The details of how we arrived at the 

proportions of lives saved and injuries prevented are provided in Chapter Four of Ringel et al., 

2015. These are assumed to be consistent across states (that is, implementing a universal 

motorcycle helmet law will reduce deaths in motorcycle crashes by the same percentage in every 

state). 

We made changes to the other three effectiveness elements, as detailed in this chapter. 

Numbers of Deaths and Injuries per State

New Methods for Counting Total Deaths

In MV PICCS 2.0, we relied on two data sources to estimate the numbers of lives saved and 

injuries prevented. The number of lives saved is based on the number of fatalities that occurred 

because of various crash types (e.g., alcohol-related crashes) taken from FARS. FARS provides 

an actual count of all fatalities on public roads in the United States each year. The previous MV 

PICCS made calculations based on 2010 FARS data; we updated to 2015 data, the most recent 

available at the time of this work. We calculate the number of lives that a particular intervention 

saves based on the number of deaths in the relevant crash type and the estimated effectiveness of 

the intervention. For example, for sobriety checkpoints, the number of lives saved is based on the 

number of alcohol-related deaths in a particular state and the estimated effectiveness that 

sobriety checkpoints have in reducing deaths. 

We made minor changes to the way we calculate the number of deaths for two categories: 

• bicyclist deaths. In the 2015 MV PICCS, we counted all bicyclist fatalities, even though 

the intervention is a law designed to require children to wear helmets. We updated our 

11 
 

 

 

 

 

 

 

 

 

 

 

 

methods to count only deaths of bicyclists who were 15 or younger, and we also updated 

this for estimating injuries to include only bicyclists age 15 or younger. 

• deaths at signalized intersections. Our current definition of a crash occurring at a 

signalized intersection requires that the crash occur at an intersection and that a traffic 

control signal of some sort be present at that intersection.5 We used this definition for 

creating injury estimates as well. 

New Methods for Estimating Total Injuries 

We updated our method for estimating the total number of injuries in each state to address 

limitations of the injury data. There is no equivalent data source to FARS for counting all traffic 

crash injuries. Most estimates of the number of people injured in traffic crashes are based on 

GES. This database is a sample of all injury crashes,6 representing approximately 1 percent of all 

police crash reports in each year that occur around the country. GES classifies injuries by 

severity on a scale that includes death as the most serious outcome. 

Before we can estimate the number of injuries that an intervention prevents, we need a total 

number of injuries by state and by type. Lacking an actual count of all injuries, we instead rely 

on ratios of fatalities to injuries. For example, using GES data, we determined that, nationwide, 

for every fatality due to speeding, there are 36 injuries due to speeding. We then multiply by 36 

the actual number of fatalities in a state, as reported in FARS, to estimate the total number of 

injuries. Although this assumes that these ratios are consistent across states, which might or 

might not be the case, without state-specific information, we believe that this is the most 

reasonable assumption under the circumstances. 

For the previous MV PICCS, we used the weighted estimates of fatalities and injuries for 

each crash type to develop this ratio. However, we found that GES was undercounting fatalities 

compared with the counts in FARS. This meant that, when we multiplied our total ratio of 

fatalities to injuries by the number of fatalities in FARS, the estimated total number of injuries 

was around 3.7 million. NHTSA’s published figures estimate 2.4 million injuries in 2015 

(National Center for Statistics and Analysis, 2016b), meaning that our ratios would provide 

overly high numbers of injuries. 

Instead, we used the weights for each crash type in GES to develop a total number of 

injuries, then divided that number by the number of fatalities in FARS to determine the ratio. We 

also excluded from GES injury counts any fatal injuries because otherwise the ratio would be 

skewed. Table 3.1 compares these ratios with the ratios we used in MV PICCS 2.0. 

5 In MV PICCS 2.0, we considered only whether the crash occurred at an intersection, not the presence of any sort 

of traffic signal. 

6 Generally, crashes are classified into three types: fatal crashes (those that cause deaths), injury crashes (those that 

cause injuries but no deaths), and property damage crashes (those that damage the vehicles or other property but do 

not result in any injuries or deaths). GES data include both fatal and injury crashes, but we excluded fatal crashes 

from our calculations because we relied instead on FARS data. 

12 
 

 

 

Table 3.1. Injury-to-Fatality Ratios for MV PICCS 2.0 and 3.0

Ratio Developed with 

2010 GES Data Used in 

Ratio Developed with 

2010 GES Data Calculated 

Category MV PICCS 2.0 with the Updated Method MV PICCS 3.0 Ratio

Total 106.54 68.21 70.02

Alcohol-related 36.18 16.63 14.10

Motorcycle 264.54 18.77 18.18

Bicycle 171.54 158.48 145.08

Occurred at an intersection 85.86 228.62 223.31

with a light

Vehicle occupants 105.55 87.53 94.65

Driver over age 70 90.83 56.33 61.03

Speed-related 82.60 44.09 36.43

NOTE: The MV PICCS 2.0 ratio is based on FARS and GES data for 2010. The MV PICCS 3.0 ratio is based on 

FARS and GES data for 2015. The middle column is shown for comparisonU these would have been the ratios used in 

the 2015 tool had we used the method described in this section. We have not used these figures in either version of 

MV PICCS.

As the table shows, some of these estimates changed quite dramatically, and all but one 

decreased. As shown in the “Calculated with the Updated Method” column of Table 3.1, the 

majority of this difference can be attributed to using the FARS count of fatalities, rather than the 

GES estimate of fatalities, in our ratio. The largest decrease is in the ratio of motorcycle fatalities 

to injuries. Previously, our ratio was more than 250, and now it is 18. This difference is largely 

due to a coding error in MV PICCS 2.0 that we recently discovered and corrected in the current 

version. Updating the data from 2010 to 2015 affects the ratios as well (comparing the 

“Calculated with the Updated Method” and “MV PICCS 3.0 Ratio” columns of Table 3.1), but 

the change is small in comparison to the change in methods. 

The Effect on Estimates of Lives Saved and Injuries Prevented

The total number of motor vehicle–related fatalities in the United States increased from 

32,999 in 2010 to 35,092 in 2015 (NHTSA, 2017). These increases were not uniform across the 

country, or across crash types, so the effects of implementing any given intervention in any state 

include slight changes to the number of lives saved, lower in some cases and higher in others. 

The lower injury-to-fatality ratios for all but one category means that the number of injuries 

estimated for each state is lower in the updated model, so, in turn, the number of injuries that 

each intervention prevents is also lower. 

13 
 

 

 

 

 

 

 

 

 

The Monetary Value of Saving a Life or Preventing an Injury

Estimates of Monetary Value 

For MV PICCS 2.0, we estimated the value of a life saved or injury prevented based on 

national-level estimates provided in Blincoe et al., 2015. We relied on these same estimates for 

MV PICCS 3.0 because this detailed work on the costs of traffic crashes is widely cited. We 

incorporated three important changes to these costs: 

• First, we adjusted the unit costs for inflation to generate estimates in 2017 dollars (we 

used 2012 dollars for MV PICCS 2.0). 

• Second, we adjusted some of the costs to account for state-level variation using updated 

state-specific adjusters. 

• Third, we aggregated the costs by injury severity into one metric (we made no change to 

this part of the method). 

The Blincoe et al., 2015, estimates include nine cost components: medical, emergency 

services, market productivity, household productivity, insurance administration, workplace costs, 

legal costs, travel delay, and property damage. We made state-specific adjustments for market 

productivity, household productivity, and medical costs. We used updated state-specific price 

adjustments employed by CDC’s Web-Based Injury Statistics Query and Reporting System 

(WISQARS) cost-of-injury reports computed using ACCRA Cost of Living Index data and 

population data.7 We modified the market and household productivity estimates using the 

WISQARS work-loss adjustments and modified the medical estimates using the WISQARS 

medical adjustments. 

As Table 3.2 shows, for the majority of states, the productivity factor increased, while states 

were roughly split on whether the medical factor increased or decreased. Table B.1 in 

Appendix B shows these data for all states. 

7 ACCRA previously stood for American Chamber of Commerce Research Association. The organization is now 

called the Council for Community and Economic Research, and it compiles what is now called the Cost of Living 

Index. 

14 
 

 

 

 

 

 

 

 

Table 3.2. Average, Low, and High Values of State-Specific Price Multipliers, of Saving a Life, and

of Preventing an Injury

MV MV MV MV MV MV 

PICCS 2.0 PICCS 3.0 PICCS 2.0 PICCS 2.0 PICCS 3.0 PICCS 3.0 

Value

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 2.0 

Multiplier: 

Medical

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 3.0 

Multiplier: 

Medical

Value of 

Preventing 

an Injury, 

in Dollars

Value of 

Saving a 

Life, in 

Dollars 

Value of 

Preventing 

an Injury, 

in Dollars

Value of 

Saving a 

Life, in 

Dollars

Average 0.97 1.00 1.03 0.99 22,052 1,282,982 22,073 1,603,394

Lowest 0.75 0.89 0.87 0.77 19,897 1,042,033 20,480 1,385,639

Highest 1.58 1.28 1.41 1.69 27,281 1,924,863 24,924 2,124,519

SOURCES: Multipliers, CDC, 2014, § 4.4. Others, calculations by the authors.

The Effect of These Adjustments on Estimates of Monetary Value of Lives Saved and 

Injuries Prevented 

For almost every state, the value of preventing an injury changed only slightly, while the 

value of saving a life increased. This is because, although we include the same nine components 

(e.g., medical, emergency services, market productivity) in both injury and fatality calculations, 

the market productivity component makes up a larger portion of the fatality estimate. As a result, 

the value of saving one life increased in all but one state (Connecticut) and the District of 

Columbia. A few other states had decreases in productivity but increases in the medical 

adjustment factor, so they had small increases in the value of a life saved. 

How this changes the overall monetary benefit within a state depends also on the injury-tofatality ratio for a particular intervention and the numbers of lives saved and injuries prevented. 

For example, in Georgia (a typical state in terms of the magnitude of the changes from MV 

PICCS 2.0 to MV PICCS 3.0), MV PICCS 2.0 calculated a benefit of $1.449 billion if all 

14 interventions were implemented. The updated MV PICCS 3.0 estimates a total benefit of 

$1.442 billion. The difference is the result of three contributing factors: 

• The total number of injuries prevented declined. The total number of injuries decreased 

for most crash types because the estimated ratios of injuries per fatality went down. 

Injuries prevented by red-light cameras increased because the estimated ratios of injuries 

per fatality went up. Injuries prevented by seat belt laws increased because of the large 

increase in the number of people who died in passenger vehicle crashes (from 907 in 

2010 to 1,034 in 2015). 

• Fatalities increased in all crash types (for example, the number of people who were killed 

because of alcohol-involved driving increased from 299 in 2010 to 366 in 2015). 

Therefore, a greater number of lives is saved with the same interventions. 

• The value of saving one life increased from $1.3 million to $1.5 million, and the value of 

preventing one injury increased from $19,740 to $21,411. 

Table 3.3 shows the effects of these three types of changes. Table 3.3 shows the effects of 

these three types of changes. The column “Total Benefit in MV PICCS 2.0, in Thousands” 

15 
 

displays the total benefit per intervention as calculated for MV PICCS 2.0. The next pair of 

columns shows what the benefit would have been had we used the updated injury-to-fatality 

ratios. In most cases, this declines because the ratios are lower. The second pair of columns 

updates the first pair with fatalities from 2015 instead of 2010. Finally, the third pair of columns 

shows the current tool values, with both of these updates as well as the new value of life. 

16 
Table 3.3. Comparison of Benefits Between MV PICCS 2.0 and 3.0 for Georgia, Based on Changes to Three Inputs

Total Benefit in MV 

PICCS 2.0, in 

Total Benefit, in 

Thousands of 

Percentage Change 

from Total Benefit in 

Total Benefit, in 

Thousands of 

Percentage Change 

from Total Benefit in 

Total Benefit, in 

Thousands of 

Percentage Change 

from Total Benefit in 

Thousands Dollars, with MV PICCS 2.0 Dollars, with MV PICCS 2.0 Dollars, with MV PICCS 2.0

InjuryFtoFFatality 

Ratios:

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Number of 

Fatalities:

No Change from 

MV PICCS 2.0

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Value of Life:

No Change from 

MV PICCS 2.0

No Change from 

MV PICCS 2.0

Updated to MV 

PICCS 3.0

Intervention

Alcohol interlock 42,013 32,914 –22 40,102 –5 46,936 12

Bicycle helmet 12,647 11,236 –11 429 –97 471 –96

Higher seat belt 

fines

220,834 206,782 –6 235,735 7 267,062 21

InCperson license 

renewal

53,970 43,698 –19 42,571 –21 48,914 –9 

License plate 

impoundment

47,264 37,028 –22 45,114 –5 54,180 15

Limits on 19,256 15,085 –22 18,380 –5 22,351 16

diversion

Motorcycle helmet 239,298 60,810 –75 72,783 –70 86,187 –64

Primary 

enforcement seat 

214,700 201,038 –6 229,187 7 259,726 21

belt law

RedClight camera 129,736 247,361 91 114,465 –12 127,321 –2 

Saturation patrol 104,448 81,830 –22 103,275 –1 123,403 18

Seat belt 165,626 155,086 –6 176,801 7 201,076 21

enforcement 

campaign

Sobriety 

checkpoint

71,911 46,639 –35 58,862 –18 69,217 –4 

Speed camera 74,504 51,318 –31 64,872 –13 75,333 1 

17 
Total Benefit in MV 

PICCS 2.0, in 

Thousands

Total Benefit, in 

Thousands of 

Dollars, with

Percentage Change 

from Total Benefit in 

MV PICCS 2.0

Total Benefit, in 

Thousands of 

Dollars, with

Percentage Change 

from Total Benefit in 

MV PICCS 2.0

Total Benefit, in 

Thousands of 

Dollars, with

Percentage Change 

from Total Benefit in 

MV PICCS 2.0

InjuryFtoFFatality 

Ratios:

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Number of 

Fatalities:

No Change from 

MV PICCS 2.0

Updated to MV 

PICCS 3.0

Updated to MV 

PICCS 3.0

Value of Life:

No Change from 

MV PICCS 2.0

No Change from 

MV PICCS 2.0

Updated to MV 

PICCS 3.0

Intervention

Vehicle 

impoundment

53,216 41,690 –22 50,795 –5 59,982 13

Total 1,449,423 1,232,515 –15 1,253,371 –14 1,442,145 –1 

SOURCE: All values calculated by the authors.

NOTE: The “Total Benefit in MV PICCS 2.0” column displays the total benefit per intervention as calculated for MV PICCS 2.0. The next “Total Benefit” column 

shows what the benefit would have been had we used the updated injuryCtoCfatality ratios, accompanied by the corresponding percentage changes in the next 

column. The pair after that has that update plus the number of fatalities updated, and the final pair has those updates plus the value of life updated. In most cases, 

these values and percentages decline because the ratios are lower.

18 
The changes that occur to benefits in Georgia are similar to those in other states. The changes 

to the ratios affect all states in the same direction. If the number of fatalities in a particular 

category changes by a large percentage, this would change the benefit in the same direction (the 

higher the number of fatalities, the higher the number of lives we estimated to be saved). Finally, 

most states saw large increases in their values of saving a life and very minor changes in the 

values of preventing an injury. 

19 

 

 

 

 

 

 

 

 

Chapter Four. Cost Data, Estimates, and Analysis 

Much of the work on developing the cost side of MV PICCS was related to developing a cost 

structure with ten cost components: 

• publicity 

• police or highway patrol time 

• court system 

• DMV 

• equipment 

• fines and fees 

• probation 

• education programs 

• vehicle impoundment 

• program management. 

Because we did not add any new interventions that might have necessitated a review of the 

cost-estimating structure, we did not make changes to the list of components, to which 

components were associated with which intervention, or to the assumptions about the percentage 

of people violating a law. Our previous work developing these cost estimates is provided in 

Chapter Three of Ringel et al., 2015. 

However, we did make some important changes to the cost inputs and calculations. The most 

basic is that we updated most costs in MV PICCS from 2012 dollars to 2017 dollars.8 We also 

updated some data sources to the most-recent figures available (in most cases, this was 2015 

data; if we updated it to a different year, we note that in the discussion about that source). 

In reviewing our previous data sources and assumptions, we determined that two changes 

would provide better overall estimates: adjustments to DWI arrest data and changes to our 

assumptions regarding automated enforcement.9 

Updated Cost Inputs and Assumptions

Inflation

We used the Bureau of Labor Statistics (BLS) Consumer Price Index inflation calculator to 

change cost data in the tool to 2017 dollars (BLS, undated [a]). 

8 Because states rarely update the amount of a fine because of inflation, we did not update any of the fines or fees, 

even to inflate them to 2017 dollars. 

9 State terminology varies; a DWI charge against a drunk driver is the same as a charge of driving under the 

influence (generally referred to as DUI). For the sake of consistency, this report uses DWI. 

21 
 

 

 

 

 

 

Wages

We used BLS data to determine the average wage in each state for three groups of workers: 

• probation officers and correctional treatment specialists 

• police officers 

• office and administrative support workers employed by state governments. 

For the first two groups, we obtained data through the BLS Occupational Employment Statistics 

query system, searching by the appropriate occupation code.10 Data for probation officers and 

correctional treatment specialists were missing for Connecticut, Delaware, and Rhode Island, so 

we used the average pay of all states as a proxy for the real cost for these states. 

For the third group, office and administrative support workers employed by state 

governments, we used a different BLS data source, Occupational Employment Statistics research 

estimates by state and industry (BLS, undated, 2016). (Although BLS produces estimates of 

wage and employment by state and industry, these have small samples and have potential for 

systematic error.11) All BLS wage data were retrieved for 2016 and inflated to 2017 dollars. 

Updated Data on Populations

We also updated the number of individuals, drivers, and registered vehicles. 

Population 

We updated two sets of population data because they are used in two different areas of MV 

PICCS: 

• state population: We updated state population by age for 2015 (U.S. Census Bureau, 

2016). This affects the cost estimates for sobriety checkpoints and higher seat belt fines. 

• population by metropolitan area: We updated metropolitan area population, which is used 

in calculating costs for the red-light camera intervention (U.S. Census Bureau, 2017). 

The Number of Licensed Drivers 

We updated the number of licensed drivers per state (Office of Highway Policy Information, 

2016, Table DL-22). The number of licensed drivers affects the costs of all interventions with 

publicity costs because we assume that costs fluctuate with the number of drivers to be reached 

by the publicity campaign. This includes red-light and speed cameras, saturation patrols and 

10 BLS occupation 21-1092 for probation officers and correctional treatment specialists and BLS occupation 33-

3051 for police and sheriff’s patrol officers. 

11 From this data set, we used May 2016 data for the relevant industry 4 sector according to the North American 

Industry Classification System, 999200, state government (excluding schools and hospitals). From the employment 

data for state governments, we selected the occupation category 43-0000, office and administrative support 

occupations. 

22 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

sobriety checkpoints, motorcycle and bicycle helmet laws, and primary enforcement seat belt 

laws and seat belt enforcement campaigns. The number of drivers over age 70 also factors into 

the cost of in-person license renewal. 

The Number of Registered Motorcycles 

We updated the number of registered motorcycles, which affects the costs of implementing 

motorcycle helmet laws, in each state to 2015 data (Office of Highway Policy Information, 2016, 

Table MV-1). 

Driving-While-Intoxicated Arrest Data

A New Method of Calculating DrivingBWhileBIntoxicated Arrests 

The most comprehensive set of arrest data for DWI is the one compiled by the Federal 

Bureau of Investigation (FBI).12 The FBI does not collect these data directly; it compiles data 

provided by police agencies into a consistent data set. The FBI acknowledges the limitations of 

these data with a disclaimer that states, “Because the number of agencies submitting arrest data 

varies from year to year, users are cautioned about making direct comparisons between 2015 

arrest totals and those published in previous years’ editions of Crime in the United States” (FBI, 

2015, Table 69 footnote). 

Errors in DWI arrest data are due to incomplete submittals by jurisdictions, meaning that 

some states are undercounting the number of arrests. The data set includes the number of 

jurisdictions that report arrest numbers, as well as the population represented across those 

jurisdictions. This allows us to adjust the number of arrests systematically. We calculated 

adjusted arrest counts for each state by multiplying the number of reported DWI arrests by the 

ratio of the total population of the state (as reported in U.S. Census Bureau, 2016) with the 

population of those jurisdictions that reported arrest data. 

This straightforward extrapolation still might not reflect actual arrests because it is not 

necessarily the case that the rate of arrests across reporting jurisdictions will equal the 

corresponding rate across jurisdictions that do not report. However, we deemed this method to be 

preferable to using the reported numbers without adjustment.13 Table 4.1 presents our resulting 

data. 

12 We believe that these arrest figures include exclusively arrests for driving while intoxicated with alcohol because 

laws about drugged driving vary widely from state to state (GHSA, undated [b]). However, the FBI does not 

comment on this point in the data declaration for the relevant table. 

13 We tried two other methods to determine whether there were better methods to adjust DWI arrests to be more 

accurate. First, we cross-checked our estimates with those that Casanova-Powell et al., 2015, obtained directly from 

the states, and there were still over- and undercounts. Second, we considered using DWI per capita fatalities but did 

not find a clear relationship between arrest rates and fatalities that might have led to more-reliable arrest figures. 

23 
 

 

 

Table 4.1. Federal Bureau of Investigation–Reported and –Estimated Driving-While-Intoxicated 

Arrests, by State, 2015 

Ratio of Reporting Jurisdictions to 

State FBI-Reported DWI Arrests State Population, as a Percentage Estimated DWI Arrests 

Alabama 7,863 75 10,428

Alaska 3,163 100 3,179

Arizona 22,367 75 29,896

Arkansas 6,919 90 7,669

California 141,458 99 142,316

Colorado 25,562 94 27,320

Connecticut 8,148 92 8,863

Delaware 386 100 386

District of Columbiaa 1,346 100 1,346

Florida 31,783 100 31,859

Georgia 19,217 76 25,278

Hawaii 5,250 81 6,456

Idaho 5,844 92 6,382

Illinois 3,659 22 16,356

Indiana 14,428 71 20,306

Iowa 9,028 80 11,269

Kansas 7,186 65 11,107

Kentucky 17,825 99 18,001

Louisiana 5,339 53 10,105

Maineb 5,756 100 5,766

Maryland 17,100 76 22,597

Massachusetts 8,258 91 9,058

Michigan 26,845 93 28,718

Minnesota 20,830 96 21,706

Mississippi 6,889 40 17,163

Missouri 19,449 88 22,054

Montana 3,674 86 4,253

Nebraska 5,348 62 8,652

Nevada 7,612 100 7,612

New Hampshire 4,746 92 5,162

New Jersey 22,201 91 24,502

New Mexico 8,542 76 11,290

Ultimately, we determined that our population-based adjustment method was the most objective way to adjust for 

undercounted arrest figures without introducing other statistical uncertainty. 

24 
 

 

 

 

Ratio of Reporting Jurisdictions to 

State FBI-Reported DWI Arrests State Population, as a Percentage Estimated DWI Arrests 

New York 28,988 53 54,760

North Carolina 35,967 69 51,932

North Dakota 6,351 98 6,458

Ohio 34,254 71 48,049

Oklahoma 11,101 91 12,151

Oregon 9,019 48 18,932

Pennsylvania 44,615 94 47,677

Rhode Island 2,591 100 2,591

South Carolina 16,272 83 19,499

South Dakota 7,305 92 7,934

Tennessee 23,150 96 24,101

Texas 64,971 98 66,116

Utah 8,813 95 9,322

Vermont 2,144 93 2,300

Virginia 20,477 95 21,659

Washington 24,627 91 26,985

West Virginia 4,543 58 7,815

Wisconsin 24,588 95 25,789

Wyoming 3,157 89 3,532

Total 865,616 1,033,310

SOURCE: FBI-reported arrests, FBI, 2015, Table 69. Estimated arrests, author calculations. a FBI, 2015, does not include any arrest data from the District of Columbia’s municipal police force. For this one 

jurisdiction, we have substituted arrest figures from the city’s own reporting (Metropolitan Police Department, 2016). b The percentages are rounded, but the actual ratio is 99.8 percent, which is why there was an adjustment of ten 

arrests.

The Method’s Effect on Cost Estimates 

The fact that some states underreported DWI arrest data meant that previous MV PICCS 

estimates for certain states were underestimating the cost of implementing four alcohol-related 

interventions because we used the number of arrests to estimate the cost of state police time.14 

The cost-effectiveness ratios in states with undercounted DWI arrests were far higher than those 

in states with more-complete arrest data. For example, in Arkansas, a typical state, the costeffectiveness ratio of license plate impoundment was previously 163, when most other states 

were in the double digits. The cost of implementation was only $181,000. After this revision, the 

cost is $205,000 and the ratio is 146 ($30 million in benefits divided by the cost). 

14 Costs for sobriety checkpoints and saturation patrols are not based on existing arrest rates for DWI, so this new 

calculation does not affect their costs. 

25 
 

 

 

 

 

 

 

 

Automated Enforcement

New Assumptions About the Number of Cameras 

Creating cost estimates for the number of red-light and speed cameras in the previous MV 

PICCS was challenging because there is no reliable data source on how many red-light or speed 

cameras are typically used to implement this intervention. In addition, we expect red-light 

cameras to be used largely in urban areas, so it is difficult to develop statewide estimates. 

Ultimately, we used state population and road network in miles within the state as explanatory 

variables in a count regression model that predicts the number of cameras that a state would use. 

In actual implementations, decisionmakers would probably select areas that studies identified 

with sufficient traffic or red light–running behavior to have an impact, but, at the state level, 

these data are not readily available and therefore are not included in the simplified regression 

model. 

Using road network–miles meant that we were underestimating the number of cameras in 

some states (such as the District of Columbia and Delaware) and overestimating them in large 

rural states. The overestimation in large states created very large dollar figures for 

implementation (because of the number of cameras) and high fine revenue (due to our 

assumption about the number of speeding drivers who would be ticketed, per camera). 

When we first developed these estimates, we created high, medium, and low estimates for 

each state, based on the intensity of implementation (high implementation assumed that the state 

would deploy a higher number of cameras than those at medium or low implementation levels). 

Although, at one point, we had considered the idea that a user might be able to select an 

implementation level to analyze, this proved too complex, and we programmed the tool with the 

medium-level estimate for all states. To address this issue and reflect many states’ shift away 

from camera usage, we have now changed these estimates to the low level of deployment. 

The Assumptions’ Effect on Cost Estimates 

This change means that the number of cameras is now estimated to be lower and that fewer 

drivers receive tickets.15 For example, the cost in North Dakota to implement speed cameras was 

previously estimated at $18.7 million, with fines of $70 million; in the revised tool, the cost is 

$6.9 million and the fines are $3.3 million. Red-light camera costs decreased by proportionally 

15 It is certainly possible that a change in the number of cameras deployed would result in different driver behavior. 

If people grew accustomed to a high number of cameras, over time, one might expect to see a lower number of 

violations as people drive more slowly and stop more often at red lights. We could not, however, find estimates in 

the literature of these long-term changes, so we programmed the tool with estimates we identified of the number of 

violations per camera regardless of long-term behavioral changes. Across all interventions, it was too complex to 

attempt to model longer-term changes in behavior. 

26 
 

 

similar amounts. All states have decreased cost estimates and lower fines collected, but the 

decreases are of lower magnitude in smaller states. 

Table 4.2 summarizes the changes in cost estimates and how the final costs for exact 

interventions changed. Like in Table 3.3 in Chapter Three, we selected a typical state, Georgia, 

to demonstrate. Because the cost formulas are more complex than the effectiveness formulas, we 

did not calculate all of the interim potential costs. 

Table 4.2. Comparison of Costs Between MV PICCS 2.0 and 3.0 for Georgia, Based on Changes to 

Inputs and Assumptions

Total Cost in Changes to Total Cost in 

MV PICCS 2.0, Cost Updates That Noncost Inputs MV PICCS 3.0, Percentage

in Thousands Affect This That Affect This in Thousands Change from 

Intervention of Dollars Intervention Intervention of Dollars 2015 Costs

Alcohol 

interlock

Bicycle helmet

Higher seat belt 

fines

In-person 

license renewal

License plate 

impoundment

Limits on 

diversion

Motorcycle 

helmet

Primary 

enforcement

seat belt law

Red-light

camera

124

519

0 

520

453

17,885

1,858

6,756

22,075

Increase in state 

employee wages

Increase in state 

employee wagesd 

publicity costs adjusted 

for inflation

Noned we assume this 

intervention to have no 

cost

Increase in state 

employee wages

Increase in state 

employee wages

Increase in state 

employee wagesd court, 

probation, and education 

costs adjusted for 

inflation

Increase in police and 

state employee wagesd 

publicity costs adjusted 

for inflation

Publicity costs adjusted 

for inflation

Increase in police and 

state employee wagesd 

equipment and publicity 

costs adjusted for 

inflation

Update and 

adjustments to the 

number of DWI 

arrests

Update to the state 

population

None

Update to the 

number of licensed 

drivers

Update and 

adjustments to the 

number of DWI 

arrests

Update and 

adjustments to the 

number of DWI 

arrests

Update to the 

numbers of licensed 

drivers and 

motorcycle 

registrations

Update to the state 

population

Decrease in the 

number of cameras 

deployedd update to 

the number of 

licensed drivers

114

619

0 

631

369

15,132

2,081

7,292

5,422

–8 

19

Not applicable

21

–19

–15

12

8 

–75

27 
 

Intervention

Total Cost in 

MV PICCS 2.0, 

in Thousands 

of Dollars

Cost Updates That 

Affect This 

Intervention

Changes to 

Noncost Inputs 

That Affect This 

Intervention

Total Cost in 

MV PICCS 3.0, 

in Thousands 

of Dollars

Percentage

Change from 

2015 Costs

Saturation 

patrol

8,146 Increase in police and 

state employee wagesd 

court, equipment,

publicity, probation, and 

education costs adjusted 

for inflation

Update to the 

number of licensed 

drivers

8,709 7 

Seat belt 

enforcement 

campaign

2,638 Publicity costs adjusted 

for inflation

Update to state 

population

2,769 5 

Sobriety 

checkpoint

6,066 Increase in police and 

state employee wagesd 

court, equipment, 

publicity, probation, and 

education costs adjusted 

for inflation

Update to the 

numbers of licensed 

drivers and state 

population

6,717 11

Speed camera 27,378 Increase in police and 

state employee wagesd 

equipment costs 

adjusted for inflation

Decrease in the 

number of cameras 

deployedd update to 

the number of 

licensed drivers

11,049 –60

Vehicle 

impoundment

19,985 Increase in state 

employee wagesd towing 

cost adjusted for 

inflation

Update and 

adjustments to the 

number of DWI 

arrests

16,902 –15

Total 114,403 77,806 –32

Certain costs, such as inflation, increase uniformly across states, and all states experienced 

some increase in their total populations and numbers of licensed drivers, which affect all 

interventions that we assumed would contain a publicity component (red-light and speed 

cameras, saturation patrols and sobriety checkpoints, and motorcycle and bicycle helmet laws). 

Most states experience a substantial decline in the costs of red light– and speed-camera 

enforcement because of the change in the assumed number of deployed cameras from medium to 

low. The four interventions whose costs are partially based on the number of DWI arrests 

(alcohol interlocks, license plate impoundment, limits on diversion, and vehicle impoundment) 

could see increases or decreases in costs, depending on whether the adjusted number is higher or 

lower than the number of DWI arrests in 2011. (In Georgia, arrests declined from 31,176 in 2011 

to 25,278 in the 2015 adjustment, so all four of these costs went down.) 

28 
 

 

Chapter Five. Tool Redesign 

A major portion of this effort was redesigning the MV PICCS tool interface to make it more 

intuitive for users to request an analysis and more easily interpret the results. Both NGA and 

CDC had discussed the tool with officials from individual states who suggested that it could be 

made more intuitive for users, in terms of both inputting information and understanding the 

model results. To address these concerns, we made seven changes: 

• created a new map showing states’ current traffic safety profiles 

• redesigned the way in which users input interventions for analysis 

• combined cost-effectiveness and portfolio analysis into one set of model outputs 

• changed the way in which fine and fee information is provided 

• streamlined the sensitivity analysis 

• displayed the key results in boxes and provided graphics of some results 

• incorporated additional information about interventions. 

This chapter explains the changes that we made to streamline the tool and provides 

screenshots of the new user interface. 

Map

Instead of selecting a state from a drop-down list, we provided a color-coded map for users to 

select a state. The color-coding is based on the number of people killed in 2015 traffic crashes 

per 100,000 people. The underlying data are accessible in a sortable table on a separate tab. See 

Figure 5.1. 

29 
Figure 5.1. The Input Screen with Map

When the user clicks on a state, the tool provides an input screen for that state. Underneath 

the map, a tab shows which state is currently being analyzed. The user can have more than one 

state open at a time and easily switch between them by clicking on the name of the state. 

Selecting Interventions

After the user selects a state (this example uses Pennsylvania), the tool displays a list of 

14 interventions. As a default, the interventions with a check mark are those that are not already 

implemented in that state. (The tool also indicates these interventions using bold italics.) The 

user can check or uncheck boxes as desired, but our goal was that the default would allow the 

user to see the results that would occur if the selected state implemented all interventions that are 

not already in place. A toggle allows the user to return to the selection of only unimplemented 

interventions. 

30 
 

 

The user must enter a budget for analysis. If desired, the user can also check the Use Fines 

and Fees box, but the default is that it is unchecked. This screen also allows the user to select the 

sensitivity analysis. This screen is shown in Figure 5.2. 

Figure 5.2. Intervention Selection

Model Outputs

The tool runs when the user selects Run Model. One major difference from MV PICCS 2.0 

is that there are no longer separate model runs for cost-effectiveness and portfolio analysis. The 

31 
 

 

 

 

 

 

 

 

 

 

 

difference between these two types of runs was that the cost-effectiveness model analyzed each 

intervention separately, while portfolio analysis accounted for the fact that some interventions 

were related to each other and might double-count some of the benefits. For example, if both 

saturation patrols and sobriety checkpoints were implemented, one would not expect the full 

benefits from each because some people who would have been deterred from driving with 

saturation patrols would also have been deterred from driving in the presence of sobriety 

checkpoints. 

Given that some users reported that it was difficult to understand the differences between the 

two model runs and the fact that they could not be compared side-by-side within the tool, we 

now include all results in one model run.16 An intervention table (see Figure 5.3) shows the 

individual cost and effectiveness results for each intervention that the user selected. If the model 

selected the intervention, the row is shaded in blue. At the bottom of the table are two rows of 

totals, TOTAL OF ALL SELECTED INDIVIDUAL INTERVENTIONS and ADJUSTED 

TOTAL OF SELECTED INTERVENTIONS. 

The TOTAL OF ALL SELECTED INDIVIDUAL INTERVENTIONS row sums all of 

the costs of the model-selected interventions (the equivalent of the cost-effectiveness analysis). If 

two or more interventions are related, the ADJUSTED TOTAL OF SELECTED 

INTERVENTIONS row shows the total value but adjusted to account for potential doublecounting. The values in the ADJUSTED TOTAL OF SELECTED INTERVENTIONS row 

should always be equal to or lower than those in the TOTAL OF ALL SELECTED 

INDIVIDUAL INTERVENTIONS row. If they are equal, it means that none of the 

interventions are related, so selecting them at the same time does not lead to any adjustments. If 

two or more related interventions are selected by the tool, the ADJUSTED TOTAL OF 

SELECTED INTERVENTIONS row will show that fewer lives are saved, fewer injuries 

prevented, and overall benefits are lower.17 

16 In MV PICCS 2.0, the model could select different interventions for the same state budget depending on whether 

it was running in the cost-effectiveness mode or the portfolio analysis mode. In practice, this seldom occurred. The 

current configuration does not allow the model to select different sets of interventions but rather runs the cost 

adjustments on the interventions selected in the cost-effectiveness mode. 

17 Only the primary enforcement seat belt law and seat belt enforcement campaign interventions are programmed to 

adjust the cost to implement if both are selected, because the increased seat belt fine intervention always has a cost 

of 0. If one or more seat belt interventions is selected, the net cost will be higher because the fines and fees are 

lower. We did not adjust for cost to implement and fines and fees for the other sets of related interventions because 

the ways in which they are implemented differ, while the seat belt interventions are more similar to each other. 

32 
 

 

 

Figure 5.3. The Intervention Details Table

Five previous model outputs are no longer displayed. One is the cost, by cost component 

(publicity, police costs, and so forth). This was previously displayed only in the aggregate, so it 

was not helpful to users who were interested in specific cost components for specific 

interventions. In consultation with NGA and CDC, we determined that the space needed to 

display this could be better used for graphical display of other results. 

The second is the cost–effectiveness ratio. Although this is the basis for the model’s selection 

of specific interventions, discussions with NGA and CDC indicated that reducing the number of 

outputs displayed to the user would increase the utility of the tool. Some users also found it 

difficult to interpret a negative cost–effectiveness ratio, which was the case when the net costs 

were negative. Third, we deleted cumulative cost, which users can derive on their own, if 

desired. Finally, we deleted two lines that showed other types of costs: offender costs (costs that 

an offender bears other than fines and fees, such as installing an alcohol interlock) and 

compliance costs (costs that an individual bears to comply with a new law, such as purchasing a 

motorcycle helmet). These costs were not part of the implementation costs, so no calculations 

have changed as a result. 

Fines and Fees

Including the fines and fees as part of the net cost is no longer the default option. When the 

user checks the box to include fines and fees, these amounts are displayed in the Intervention 

Details table (Figure 5.3). However, the original cost to the state (Cost to Implement) is still 

displayed separately. User feedback indicated that negative costs were confusing. Although a 

value in the Net Cost column can still be negative when the fines and fees are larger than the 

implementation cost, now the individual costs and fines and fees are shown separately. 

33 
 

 

Sensitivity Analysis

The sensitivity analysis was previously available for only the portfolio analysis run but is 

now available for the full run. The user can now input user-specified values for two types of 

inputs: the percentage of fatalities and injuries that a particular intervention would reduce and the 

value of saving a life or preventing an injury. These values are now displayed in a way that users 

should find more intuitive (the values for each intervention are side by side and use percentage 

notation [17% rather than 0.17]). See Figure 5.4. 

We eliminated from the sensitivity analysis the ability to change the values for the 

implementation cost of an intervention and the injury-to-fatality ratio. Because many separate 

costs are included in the implementation costs (including, in some cases, the fines and fees), we 

were concerned that users would have difficulty producing alternative credible total costs. (Even 

with MV PICCS 2.0, the tool’s programming grew too complex for us to allow users to change 

individual cost components.) Also, we were concerned that users might use unrealistic injury-tofatality reduction ratios and skew the results. 

34 
Figure 5.4. Sensitivity Analysis Inputs

35 
 

 

 

 

 

Key Results and Graphics

Several key pieces of information are now displayed in graphic form, along with being 

shown in the Intervention Details table. We have added four types of graphics: 

• a color-coded map of the United States, which displays fatalities and fatality rates when 

the user hovers over a state (see Figure 5.1). 

• call-out boxes listing the interventions that the model selected and summarizing the total 

benefits (lives saved, injuries prevented, monetary benefit) and the total costs 

(implementation cost, fines and fees collected, and net cost) (see Figure 5.5). 

• pie charts of lives saved, injuries prevented, and monetary benefits. These show the user 

how the total breaks down among the interventions that the model selected (see 

Figure 5.6). 

• a bar chart of implementation costs. To reduce possible confusion, this shows the 

implementation costs on a right-hand bar and the fines and fees on a left-hand bar. The 

net cost, which is the total implementation cost minus the fines and fees, is shown above 

the bar chart in text (see Figure 5.7). 

Figure 5.5. Call-Out Box with Model Run Results

The first two pie charts also display a new piece of information that was not available in the 

previous MV PICCS: the percentages of all lives saved and injuries reduced for a suite of 

interventions. This gives the user a sense of the magnitude of the potential reductions. In no state 

36 
would implementing all of these interventions reduce traffic deaths and injuries to 0, but the user 

can now easily see whether the potential impact is a 5-percent reduction or a 30-percent one. 

37 
Figure 5.6. Pie Charts of Benefits

38 
Figure 5.7.'Bar'Chart'of'Implementation'Costs'and'Fines'and'Fees

39 
 

 

 

Additional Intervention Information

In several places, the user can hover over or click on buttons that provide more details. In the 

selected intervention box, as well as in the box where the user determines which interventions to 

analyze, a small “information” button next to the intervention name provides a sentence about 

the intervention when the user hovers over it. Similar buttons are provided for the injury-tofatality reduction ratios in the sensitivity analysis. Hovering over individual slices in the pie 

charts provides the exact number that corresponds to that slice; if many interventions have been 

selected, the smallest ones appear as Other, with details provided directly below.18 Finally, 

clicking the icon next to the name of each intervention in the Intervention Details table brings the 

user to the fact sheet about that intervention, which opens in a separate tab. 

Finally, we used the PRINT REPORT button to allow the user to generate a Portable 

Document Format (PDF) report of the main calculations and graphics for any model run. 

A sample PDF is provided beginning on the next page. 

18 The program we used to create these pie charts, Google Charts, cannot be modified to display all of the numbers. 

40 
Motorcycle Helmet

License Plate

Impoundment

14

In Person Renewal

16

Increased Seat Belt Fine

59

Alcohol Interlocks

12

Speed Camera

65

Seat Belt Enforcement

Campaign

44

Saturation Patrols

65

Total: 326

MV PICCS Model Run Results for Pennsylvania with 

$10,000,000 Budget, and Fines Included 

Pennsylvania Motor Vehicle Crash Deaths and Injuries in 2015 

Total number of deaths: 1,200 

Total number of injuries: 84,028 

Death rate (per 100k people): 9.4 

Data Sources: Fatality Analysis Reporting System, General Estimates System, and U.S. Census. See MV PICCS tool for details. 

The charts below show the potential number of lives saved, injuries prevented, monetary benefit, and 

implementation costs of a particular combination of traffic safety interventions. The MV PICCS tool selected this 

combination of interventions based on which of the 14 available interventions were identified by the user for 

analysis, as well as the budget provided by the user. The user is responsible for including only those interventions 

that are not already implemented in Pennsylvania. 

Potential Lives Saved with Selected Interventions 

This chart shows the number of lives saved by each intervention. These interventions would reduce the number of people who are killed in vehicle crashes every year 

in Pennsylvania by 27.2 percent. 

These results were generated by CDC's online tool Motor Vehicle Prioritizing Interventions and Cost Calculator for States. 

1 of 4 

41 
Total: 15,296

Other

47,505,000

Motorcycle Helmet

935

In Person Renewal

1,005

Increased Seat Belt Fine

5,548

Total: $828.45M

Saturation Patrols

118,794,000

Motorcycle Helmet

97,865,000

In Person Renewal

46,196,000

Increased Seat Belt Fine

210,448,000

Speed Camera

150,186,000

Seat Belt Enforcement

Campaign

157,456,000

Speed Camera

2,361

Other

367

Seat Belt Enforcement

Campaign

4,161

Saturation Patrols

919

Potential Injuries Prevented with Selected Interventions 

This chart shows the number of injuries prevented by each intervention. These interventions would reduce the number of people who are injured in vehicle crashes 

every year in Pennsylvania by 18.2 percent. The grey 'Other' slice consists of the following interventions: Alcohol Interlocks (173), License Plate Impoundment (194). 

Potential Monetary Benefit of Selected Interventions 

This is the value of all lives saved and injuries prevented, based on an assumed value of saving a life and preventing an injury. These values are based on estimates of 

things like medical costs, lost productivity, and insurance. The grey 'Other' slice consists of the following interventions: Alcohol Interlocks ($22.00M benefit), License 

Plate Impoundment ($25.50M benefit). 

These results were generated by CDC's online tool Motor Vehicle Prioritizing Interventions and Cost Calculator for States. 

2 of 4 

42 
Total Cost: S37.33M

Total Fees and Fines: S28.44M

Total Net Cost = Total Cost • Total Fees and Fines: S8.89M

Cost

Alcohol Interlocks ($0.16M)

Increased Seat Belt Fine ($0.00M)

In Person Renewal ($1.94M)

License Plate Impoundment ($0.85M)

Motorcycle Helmet($3.79M)

Saturation Patrols ($14.43M)

Seat Belt Enforcement Campaign ($4.54M)

Speed Camera ($11.61M)

Fines and Fees

-SOM

Dollars $

25M

 

 

 

Potential Implementation Cost of Selected Interventions 

The cost bar, $37.33M, is the total cost to Pennsylvania to implement all interventions in the legend. The cost of each individual intervention is shown in the legend. 

Costs include things like police and other staff costs, equipment, and publicity. The fines and fees bar, $28.44M, is the amount of money that would be paid to state 

and local jurisdictions by violators. 

Intervention Details denotes selected by model 

Intervention Lives Saved Injuries Prevented Cost to Implement Fines and Fees Net Cost Benefits 

*Alcohol Interlocks 12 173 $164,000 $0 $164,000 $22,004,000 

*Limits on Diversion 6 79 $32,765,000 $16,116,000 $16,649,000 $10,839,000 

*Primary Enforcement Seat Belt Law 57 5394 $14,617,000 $5,071,000 $9,546,000 $204,056,000 

*Increased Seat Belt Fine 59 5548 $0 $7,774,000 -$7,774,000 $210,448,000 

*In Person Renewal 16 1005 $1,942,000 $0 $1,942,000 $46,196,000 

*License Plate Impoundment 14 194 $851,000 $8,497,000 -$7,646,000 $25,501,000 

*Motorcycle Helmet 51 935 $3,786,000 $203,000 $3,583,000 $97,865,000 

*Saturation Patrols 65 919 $14,433,000 $6,204,000 $8,229,000 $118,794,000 

*Seat Belt Enforcement Campaign 44 4161 $4,544,000 $1,031,000 $3,513,000 $157,456,000 

*Speed Camera 65 2361 $11,607,000 $4,727,000 $6,880,000 $150,186,000 

*Vehicle Impoundment 16 219 $31,828,000 $26,328,000 $5,500,000 $29,084,000 

TOTAL OF ALL SELECTED INDIVIDUAL INTERVENTIONS 326 15296 $37,327,000 $28,436,000 $8,891,000 $828,450,000 

ADJUSTED TOTAL OF SELECTED INTERVENTIONS 320 14950 $37,327,000 $27,809,000 $9,518,000 $811,080,000 

This table shows only the interventions that were selected for analysis. The interventions in bold italics and marked with an asterisk are not currently implemented in 

Pennsylvania. The shaded rows are the ones selected by MV PICCS based on two criteria. First, only interventions whose net cost is less than the budget provided 

were selected. Second, interventions are prioritized based on their cost-effectiveness ratio, which is the benefit divided by the net cost. 

The row 'TOTAL OF ALL SELECTED INDIVIDUAL INTERVENTIONS' sums all of the shaded rows. 

The row 'ADJUSTED TOTAL OF SELECTED INTERVENTIONS' makes some adjustments based on the selection of related interventions. This is a more realistic 

assessment because some interventions target the same groups of drivers (for example, alcohol impaired drivers). 

These results were generated by CDC's online tool Motor Vehicle Prioritizing Interventions and Cost Calculator for States. 

3 of 4 

43 
Sensitivity Analysis Input Values 

Benefit of preventing one injury: $21,770 

Benefit of saving one life: $1,519,802 

Interventions Injury Reduction Factor Fatality Reduction Factor 

Alcohol Interlocks 24% 24% 

Increased Seat Belt Fine 7.2% 7.2% 

In Person Renewal 9% 9% 

License Plate Impoundment 27% 27% 

Limits on Diversion 11% 11% 

Motorcycle Helmet 28.9% 28.9% 

Primary Enforcement Seat Belt Law 7% 7% 

Saturation Patrols 17.9% 17.9% 

Seat Belt Enforcement Campaign 5.4% 5.4% 

Speed Camera 12% 12% 

Vehicle Impoundment 30.4% 30.4% 

Through sensitivity analysis, MV PICCS allows users to change the defaults in the tool for two types of values: the benefit of saving one life or preventing one injury, 

and for the effectiveness of any intervention (the percent of deaths or injuries that it would reduce if implemented). The table above shows the default values; any 

changes made by the user are highlighted in yellow. Other default values cannot be changed. 

These results were generated by CDC's online tool Motor Vehicle Prioritizing Interventions and Cost Calculator for States. 

4 of 4 

44 
 

Chapter Six. Limitations and Future Refinements 

Building a model of this type, particularly for the state level, requires many assumptions. In 

many cases, the available data and evidence base do not provide as much information as would 

be ideal, and there is certainly room for reasonable disagreement about the assumptions we have 

made. For example, the existing studies do not always report the intervention’s effect on the 

outcomes of most interest. In fact, we have an estimate of the effect on injuries for only one 

intervention, so, in the absence of better information, we assume that the reductions in the 

number of injuries for other interventions are proportional to the reductions in the number of 

deaths. Moreover, the estimates of the effects are based on studies identified in our original 

literature review conducted in 2014 and thus might not reflect the most up-to-date evidence from 

the literature. 

As another example, the cost-effectiveness estimates assume the level and characteristics 

(e.g., whether there was a publicity campaign) of implementation of the successful intervention 

in the jurisdiction where it was originally implemented and evaluated. If the intervention is not 

implemented at the same level (e.g., not as much publicity about a seat belt enforcement 

campaign) as assumed in the tool, the estimated costs and effects reported in the tool might not 

reflect actual results. 

The data that are used in the analysis also have limitations. We were able to partially address 

some of these limitations in this update to the tool. For example, when we originally built the 

tool, we could not identify a data set that provides comprehensive information on motor vehicle– 

related injuries. The available data sources that provide information on injuries describe only a 

sample of crashes. We therefore had to make a set of assumptions to translate the available data 

into the information needed for the tool. Although we still could not identify a comprehensive 

source for the injury data, in this update to the tool, we were able to refine the methods to 

generate more-accurate estimates of the total number of injuries (see Table 3.1 in Chapter 

Three). Similarly, the FBI DWI arrest data are incomplete and thus understate the number of 

arrests. In this update, we adjusted the DWI arrest data by assuming that the arrest rate is 

constant across reporting and nonreporting jurisdictions in a particular state and extrapolating 

that to a total for the state. 

Given the number of assumptions and the limitations noted, we have tried to be very 

transparent, describing our assumptions and calculations in detail in both the original report and 

this update, so readers can assess the assumptions themselves. Those who disagree with the 

assumptions can conduct a limited set of sensitivity analyses with the tool by adjusting the model 

parameters (i.e., value of an injury prevented, value of a fatality prevented, and percentages of 

injuries and fatalities that an intervention reduces) and use that analysis to inform their selections 

of the most cost-effective interventions. 

45 
 

Possible Future Refinements

Several other refinements to MV PICCS could be made in future revisions. These include 

• the ability to compare multiple intervention combinations within a state. Right now, 

MV PICCS can display the results of only one model run per state. A user who wants to 

compare different combinations of interventions, different budgets, or different inputs 

using sensitivity analysis must run each model separately and save or print the results in a 

PDF for a side-by-side comparison. One future refinement would be the capability of 

making these comparisons within the tool itself. 

• the ability to produce machine-readable data for further analysis. The current Print 

Report button creates a PDF with the same graphics and data tables that are displayed on 

screen. A future refinement would be to allow the user to download the results as an 

Excel file to conduct any further analysis. 

• the ability to reverse-engineer the budget. Currently, the user has to submit a specific 

budget, and the tool selects interventions based on that budget. In a reverse-engineered 

tool, the user would be able to ask, “What budget do I need to reduce fatalities in my state 

by X percent?” The tool would be able to provide a budget and a suite of interventions to 

reach that target. 

Some other desirable characteristics are probably too difficult to enact without creating an 

entirely new tool. Because of the complexity of the cost calculations, it would be difficult to add 

specific cost elements to the list of inputs that users could change via the sensitivity analysis. 

Similarly, it would be conceptually difficult to add in the long-term consequences of enacting 

certain interventions (for example, rates of drunk driving might go down with aggressive use of 

drunk-driving interventions, which would then change the number of drunk-driving arrests per 

year, which would lead to changes in costs and arrest rates). However, given that the literature 

generally focuses on short-term responses, many more assumptions would be required, and the 

complexity would increase enormously. 

Conclusion

The estimates that the tool provides are approximations. They are meant to give 

decisionmakers a sense of the relative costs and effects of different interventions under 

consideration. The tool might not capture some other costs and benefits that should be 

considered (e.g., the improved employment or quality of life among people who are deterred 

from driving while drunk) or political issues that make some interventions more feasible than 

others, such as opposition to certain types of laws. In essence, the outputs are designed to be one 

category of information in a decisionmaking process about which interventions to implement. 

Despite the necessary reliance on assumptions to build the model, we believe that the tool 

continues to be of great use to state decisionmakers. Although information about which 

interventions are effective has been generally available, MV PICCS is the first effort to estimate 

the implementation costs across a broad array of interventions and to translate these costs to the 

46 
state level according to a specific state’s demographics and traffic crash profile. States need 

information on both the potential costs and effects of interventions to make informed resource 

allocation decisions. 

47 

 

 

 

 

 

 

 

Appendix A. Interventions Considered but Not Included 

As noted in Chapter Two, we ultimately decided not to include two new interventions: 

lowering BAC limits to 0.05 from 0.08 and cell phone and texting bans. This appendix 

summarizes the literature we reviewed for both interventions. 

Lowering Blood Alcohol Concentration Limits to 0.05

Currently in the United States, all states and the District of Columbia use a BAC limit of 0.08 

(measured in grams of alcohol per 100 deciliters of blood) as the basis for a DWI charge. This 

intervention would lower that BAC level from 0.08 to 0.05. Many other countries have BAC 

levels of 0.05 or even lower (World Health Organization, 2014).19 

The jurisdictions where 0.05 BAC limits have been shown to be effective use enhanced 

enforcement of various types. Australia, which has the largest number of studies, has long used 

random breath testing (RBT) (Henstridge, Homel, and Mackay, 1997). An Austrian study found 

that the 0.05 limit decreased injury crashes by 7.6 percent, noting that “lowering the legal BAClimit from .08% to .05% in combination with intensive police enforcement and reporting in the 

media leads to a positive short term effect” (Bartl and Esberger, 2000). The paper does not 

explain how this intensive police enforcement was applied. In contrast, in Canada, a lack of longterm effectiveness in lowering the BAC limit to 0.05 was attributed to the fact that “perceived 

certainty and perceived celerity did not seem to be increased much, nor for very long” (Vingilis 

et al., 1988, p. 16). 

The main ways to increase enforcement are through various types of sobriety checks, which 

give police the authority to stop a driver to administer a breath test, regardless of whether the 

vehicle is being driven erratically. These checks can be distinguished by the operational 

characteristics, the level of publicity, and the intensity of use. MV PICCS already contains 

fatality reduction estimates for two types of checkpoint programs: sobriety checkpoints and 

saturation patrols. Australia uses a type of checkpoint not used in the United States: RBT. 

19 The state of Utah passed a law lowering the BAC limit to 0.05, but the law does not take effect until 

December 31, 2018 (Fell and Voas, 2017). 

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RBT allows police to pull over any driver for immediate breath testing, regardless of the 

driver’s behavior. It can be carried out on either a mobile or stationary basis; generally, the 

enforcement is along the roadside, with police vehicles available to follow drivers who attempt 

to evade the checkpoint (Ferris et al., 2015). One important distinction separates RBT from the 

types of checkpoints generally operated in the United States: 

In the Random Breath Testing (RBT) program in Australia, cars are stopped at 

checkpoints or by roving units, regardless of suspicion of alcohol use, and a 

breath alcohol test is administered. In the United States, sobriety checkpoints 

stop cars randomly, and breath alcohol tests are administered only if the use of 

alcohol is suspected through odor, actions, presence of containers, or other 

means. The key feature of both interventions is that vehicles are stopped 

regardless of the suspicion of alcohol use, but in the Australian RBT all drivers 

who are stopped receive a breath test while only drivers suspected of drinking are 

tested in the United States. (Peek-Asa, 1999, p. 58) 

We identified five studies that conducted meta-analyses of checkpoints; many of them 

include the same original studies: 

• The most recent, reported in Bergen et al., 2014, reviewed studies only from 2000 to 

2012, finding a median reduction of 8.9 percent in fatalities related to drunk driving. 

(This study combined findings from both checkpoints and RBT because only one RBT 

study was identified. All other studies were conducted in the United States.) 

• The Bergen et al. work updated a review by Shults et al., 2001, that found decreases in 

fatal crashes by 20 and 26 percent with sobriety checkpoints in two studies and a median 

22-percent decrease in fatal crashes with RBT. (Elder et al., 2002, analyzes the same 

group of studies and reaches similar figures.) 

• In Erke, Goldenbeld, and Vaa, 2009, the authors conducted a meta-analysis of studies of 

various checkpoint types, including RBT, looking at their effectiveness in reducing 

crashes. Overall, when controlling for publication bias, they found that, on average, 

checkpoints reduced crashes (both injury and fatality) by 14 percent. Broken down by 

crash type, checkpoints reduced injury crashes by 16 percent and fatal crashes by 

6 percent. Comparing different countries, they found that Australia had the best results, 

with a 17-percent reduction, while the U.S. reduction was 8 percent. (The report did not 

provide a figure for Australian programs by reduction in injury versus fatal crashes.) 

• A meta-analysis of various police interventions found that RBT reduced the percentage of 

injury crashes by 30.6 percent, while checkpoints reduced them by 22.8 percent (Blais 

and Dupont, 2005). 

The estimate in the original MV PICCS is from Fell, Tippetts, and Levy, 2008, which looks 

across multiple U.S. states. Our own interpretation of that analysis led to our estimate of the 8.1-

percent reduction that we use in the tool for sobriety checkpoints. 

We think that the Australian evidence presents a good proxy for U.S. conditions (compared 

with European countries, Australia has higher rates of driving and more rural driving), and it has 

been successful in reducing traffic crash fatalities. When Australia made 0.05 the legal BAC 

limit throughout the country in 1991 (it had previously been enacted by some but not all 

50 
 

 

 

 

 

 

provinces), there were approximately 12.2 deaths from crashes per 100,000 people (Bureau of 

Infrastructure, Transport and Regional Economics, 2012, Table 29), a rate similar to the current 

U.S. rate (11.3 for 2015). Since then, the country has seen a substantial decrease in crash 

fatalities. By 2011, it had fallen to 5.1 (Bureau of Infrastructure, Transport and Regional 

Economics, 2016, Table 2.1). 

Because both RBT and 0.05 BAC limits have been in use for several decades in Australia, 

sorting out their effects is difficult. One early and often-cited study found large reductions for 

both RBT and 0.05: For Henstridge, Homel, and Mackay, 1997, the authors found reductions in 

the number of fatal crashes ranging from 24 to 48 percent (depending on the province), but these 

large reductions were not sustained over time. The 0.05 limit was also found to contribute to 

reductions in the number of fatal crashes, even when controlling for RBT: 8 percent in one 

province and 18 in another. The report did not look at whether these reductions were sustained 

over time. 

On the other hand, a more recent study found different results. Nghiem, Commandeur, and 

Connelly found that RBT in Queensland province (both its introduction in 1988 and its 

expansion in 1998) was associated with decreases in fatalities of 11.3 and 26.2 percent, while the 

Safe4Life program that prioritizes enforcement of drunk- and drugged-driving laws reduced 

fatalities by 14.3 percent (Nghiem, Commandeur, and Connelly, 2016). However, they noted, 

“We found that there was not enough statistical evidence to confirm the effects of the first 

initiatives to limit BAC to 0.1 in 1968, and to 0.05 in 1982” (Nghiem, Commandeur, and 

Connelly, 2016, p. 71). 

A study comparing results of RBT across all Australian provinces found that the program 

considered the most effective is in the province of New South Wales. The main differentiator is 

the ratio of RBT to licensed drivers (1 to 1), meaning that, on average, in a given year, a driver 

can expect to be pulled over once. This intensity has increased over time, and the crash 

reductions have been maintained (Ferris et al., 2015). 

Challenges in Incorporating Lower Blood Alcohol Concentration Limits into MV 

PICCS 3.0

Informed by this review, we considered two ways in which lower BAC limits could be 

incorporated into the tool. Each has significant drawbacks. 

Restrict 0.05 to Be Selected Only When Sobriety Checkpoints Are Selected

One way to incorporate lower BAC limits into MV PICCS is to allow users to select the 0.05 

intervention only when sobriety checkpoints are selected. This could be programmed into the 

tool relatively easily—the 0.05 check box would be grayed out unless sobriety checkpoints were 

also selected. (Alternatively, we could have two sobriety checkpoint boxes, one for 0.08 and one 

for 0.05, and the user could select only one.) 

51 
 

 

 

 

 

The drawback to this solution is that, when sobriety checkpoints are used, the tool already 

uses an assumption of an 8.1-percent reduction in the number of fatalities related to drunk 

driving (Fell, Langston, et al., 2008). This is higher than most studies of the effectiveness of 

0.05. Henstridge, Homel, and Mackay found an 18-percent reduction in one Australian province 

and 8 percent in another (Henstridge, Homel, and Mackay, 1997); Bartl and Esberger found 

7.6 percent in Austria (Bartl and Esberger, 2000); and Blais, Bellavance, et al. found 3.7 percent 

across most Canadian provinces (Blais, Bellavance, et al., 2015). The abstract for Fell and 

Scherer, unpublished draft, states that the authors, in their meta-analysis, found an average 11.1-

percent reduction in fatal crashes, but this includes BAC levels lower than 0.05. We have not 

been able to review the full paper; at the time this report was written, it was not published, and 

the authors provided only the abstract. 

Given the overlap between BAC and enforcement, it does not seem appropriate to attribute 

all of the potential reductions in the number of fatal crashes to reductions in the BAC limit alone. 

We have been unable to locate any checkpoint studies in countries where we are confident that 

there was a BAC limit of 0.05 when the study was conducted. One problem is that many of the 

studies cited in Erke, Goldenbeld, and Vaa, 2009, which we used as a starting point, are quite old 

(e.g., Mercer, 1985; Mercer, Cooper, and Kristiansen, 1996; L’Hoste, Duval, and Lassarre, 1983; 

Törnros, 1995; Derby and Hurst, 1987; and Mathijssen and de Craen, 2004) and do not report the 

BAC limit at the time. Also, disappointingly, some of these studies—many of which were short 

term in nature—did not find significant reductions. 

Combine a 0.05 Blood Alcohol Concentration Limit with Random Breath Testing

Another strategy would be to assume that a 0.05 BAC limit would be combined with RBT. 

Ferris et al. found that New South Wales province in Australia had the most successful program 

within that country (Ferris et al., 2015). RBT was introduced in 1982 (the BAC limit had been 

lowered to 0.05 in 1980—Henstridge, Homel, and Mackay, 1997). Over time, the frequency of 

RBT has increased to 4.42 million stops for a population of 4.89 million licensed drivers, 

meaning that a driver can expect to be stopped an average of once per year for a breath test 

(Ferris et al., 2015). An earlier study noted the importance of pairing a 0.05 BAC limit with 

RBT: “any effects of the .05 law may not have been sustained if RBT had not been introduced 

two years later” (Homel, 1994, p. 147). 

Ferris et al., 2015, also provides a list of best practices for RBT. To be as effective as 

possible, RBT should be 

• highly visible and random, such that drivers perceive a high likelihood of being pulled 

over 

• emphasized during times when people are likely to be drinking 

• difficult for drivers to avoid 

• certain in terms of punishment (that is, all drivers who are over the limit should face 

consequences) 

52 
 

 

 

• supported by effective publicity. 

Henstridge, Homel, and Mackay found a 15-percent reduction in all fatal crashes (not 

exclusively those related to drunk driving) in effect a decade after RBT was introduced 

(Henstridge, Homel, and Mackay, 1997, Table 3.8). This rate plateaued in the late 1990s, but 

analysis for 2000 through 2012 found reductions in the rate of alcohol-related crashes per 

100,000 licensed drivers, from 5.05 to 2.78 (Ferris et al., 2015). These are not directly 

comparable to Henstridge’s figures because the metric is alcohol-related crashes (presumably of 

any level of severity) and not fatal crashes. 

However, RBT appears to be unconstitutional, based on the U.S. Supreme Court decision in 

Mich. Dep’t of State Police v. Sitz (496 U.S. 444, 110 S. Ct. 2481, June 14, 1990). The court 

ruled that sobriety checkpoints did not constitute unreasonable search and seizure. However, only 

a driver who gave police officers reason to believe that he or she was drunk could be subject to 

breath testing (Fell, Lacey, and Voas, 2004). Therefore, it would be difficult to assume that states 

could adopt RBT, which might negate some of the effectiveness that Australia experienced after 

lowering its BAC limit. 

Cell Phone and Text Messaging Laws

Goodwin et al., 2015, includes information on laws about cell phone and text messaging 

along with high-visibility enforcement (HVE) of these laws. This section combines the law and 

enforcement sections verbatim from Goodwin et al., 2015, as well as other literature we review 

that was not mentioned in those sections. Table A.1 provides information on cell phone and 

texting bans, by state. 

Table A.1. State Bans on Cell Phone Use, 2017

State or Cell Phone Ban: Cell Phone Bans: Novice 

District Handheld Ban School Bus Drivers Drivers Text Messaging Ban

Ala. — — 16, or 17 with intermediate 

license <6 monthsa Yesa

Alaska — — — Yesa

Ariz. — Yesa — <18 in graduated 

driver’s license onlyb

Ark. 18–20 years olda Yesa <18b Yesa

Calif. Yesa Yesa <18b Yesa

Colo. — — <18a Yesa

Conn. Yesa Yesa <18a Yesa

Del. Yesa Yesa Learner or intermediate 

licensea Yesa

D.C. Yesa Yesa Learner’s permit

a Yesa

Fla. — — — Yesb

53 
State or Cell Phone Ban: Cell Phone Bans: Novice 

District Handheld Ban School Bus Drivers Drivers Text Messaging Ban

Ga. — Yesa <18a Yesa

Hawaii Yesa — <18a Yesa

Idaho — — — Yesa

Ill. Yesa Yesa <19a Yesa

Ind. — — <21a Yesa

Iowa — — Restricted or intermediate 

licensea Yesa

Kan. — — Learner or intermediate 

licensea Yesa

Ky. — Yesa <18a Yesa

La. Learner or 

intermediate licensec Yesa First year of licensed Yesa

Maine — — Learner or intermediate

licensea Yesa

Md. Yesa — <18b Yesa

Mass. — Yesa <18a Yesa

Mich. — Yesa Level 1 (learner’s permit) or 2 

(intermediate) licensea Yesa

Minn. — Yesa <18 with learner or provisional 

licensea Yesa

Miss. — Yesa Yesa

Mo. — — — <21 onlya

Mont. — — — — 

Neb. — — <18 with learner or 

intermediate licenseb Yesb

Nev. Yesa — — Yesa

N.H. Yesa — <18a Yesa

N.J. Yesa Yesa Permit or provisional licensea Yesa

N.M. — — Learner or provisional licensea Yesa

N.Y. Yesa — — Yesa

N.C. — Yesa <18a Yesa

N.D. — — <18a Yesa

Ohio — — <18a Yesb

Okla. Learner or 

intermediate licensea — — Yesa

Ore. Yesa — <18a Yesa

Pa. — — — Yesa

R.I. Yesa Yesa <18a Yesa

S.C. — — — Yes

a

S.D. — — Learner or intermediate 

licenseb Yesb

54 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

State or Cell Phone Ban: Cell Phone Bans: Novice 

District Handheld Ban School Bus Drivers Drivers Text Messaging Ban

Tenn. — Yesa Learner or intermediate 

licensea Yesa

Texas — Yes, with passenger

≤17a <18a Yesa

Utah — Yesa <18a Yesa

Vt. Yesa — <18a Yesa

Va. — Yesa <18b Yesa

Wash. Yesa — Learner or intermediate 

licensea Yesa

W.Va. Yesa — <18 with learner or 

intermediate licensea Yesa

Wis. — — Learner or intermediate 

licensea Yesa

Wyo. — — <18a Yesa

SOURCE: GHSA, undated (a). a Primary law. b Secondary law. c Regardless of age.

d Primary for <18.

On cell phone and text messaging laws, 

States have been very active in using legislation to address [the issue of cell 

phone use while driving]. Since 2000, every State has considered legislation to 

curtail distracted driving or driver cell phone use. In 2013, legislators in 40 States 

considered approximately 170 bills related to distracted driving (Teigen and 

Shinkle, 2014, p. 8). No State completely bans all types of cell phone use for all 

drivers. Bans on texting are more common than bans on hand-held cell phone 

use. Overall, public support is high for this legislation. In surveys of the general 

public, between 70% and 80% favor bans on hand-held cell phone use, and 88% 

to 96% support bans on texting while driving (AAA Foundation for Safety 

[AAFTS], 2013; Guarino, 2013; Schroeder, Meyers, and Kostyniuk, 2013). 

(Goodwin et al., 2015, p. 4-11) 

On HVE: 

Similar to sobriety checkpoints, the object [of HVE] is to deter cell phone use by 

increasing the perceived risk of [arrest]. The HVE model combines dedicated law 

enforcement with paid and earned media supporting the enforcement activity. 

Enforcement officers actively seek out cell phone users through special roving 

patrols, or through spotter techniques where a stationary officer will radio ahead 

to another officer when a driver using a cell phone is detected. Officers report 

that higher vantage points, [sport utility vehicles], and unmarked vehicles can 

assist in identifying violators ([Chaudhary et al., 2014]). Both earned and paid 

media are critical to ensure [that] the general public is aware of the enforcement 

activity, and to create the impression that violators will be caught. (Goodwin 

et al., 2015, p. 4-14) 

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History 

Cell phones have become an essential feature of modern life. In a NHTSA survey 

of more than 6,000 U.S. residents, 60% admitted to answering phone calls while 

driving and 51% reported making phone calls ([Schroeder, Meyers, and 

Kostyniuk, 2013]). Half (50%) of cell phone users reported no differences in 

their driving when using a cell phone. (Goodwin et al., 2015, p. 4-11) 

The most recent observational survey on electronic device use, conducted in 2015, found that 

3.8 percent of drivers were using handheld cell phones, the lowest percentage since the survey 

began in 2006 (the percentage peaked in 2007, when 6.2 percent of drivers were using handheld 

phones). However, the rate of visible-headset cell phone use has remaned flat, at 0.6 percent, 

while texting (defined as visible manipulation of a handheld device) remained at the 2014 peak 

of 2.2 percent (Pickrell, Li, and KC, 2016, Figure 1). 

Goodwin et al., 2015, discusses some of the literature on the distracting effect of cell phone 

use while driving. There is general consensus that cell phone use is a distraction, but debate 

centers around whether this distraction necessarily results in more crashes. 

As for HVE, this type of intervention has been used to significant effect for curbing alcoholimpaired driving and increasing seat belt use (Goodwin et al., 2015). The strategy departs from 

typical enforcement strategy by focusing on publicizing police presence and targeting of certain 

types of offenses. 

Implementation 

As of November 2017, talking on a handheld cell phone was prohibited in 15 states 

(California, Connecticut, Delaware, Hawaii, Illinois, Maryland, Nevada, New Hampshire, New 

Jersey, New York, Oregon, Rhode Island, Vermont, Washington, and West Virginia) and the 

District of Columbia.20 All but three states (Arizona, Missouri, and Montana) and the District of 

Columbia ban texting while driving. Most of these are primary bans (IIHS, 2017f). 

In addition, several local jurisdictions such as Chapel Hill, North Carolina; 

Chicago, Illinois; and Cheyenne, Wyoming, have enacted their own restrictions 

on cell phones. (Vegega, Jones, and Monk, 2013, p. 17) 

To date, only a handful of States have implemented high visibility enforcement 

programs to address talking and texting among drivers. (Goodwin et al., 2015, 

p. 4-14) 

Effectiveness 

Evaluations in New York, Connecticut, the District of Columbia, and . . . other 

countries consistently show that cell phone laws reduce hand-held phone use by 

about 50% shortly after the laws take effect ([McCartt, Hellinga, and Bratiman, 

2006]). Moreover, these reductions in hand-held cell phone use are maintained 3 

20 Rhode Island’s law takes effect in June 2018. 

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to 7 years later ([McCartt, Hellinga, Strouse, et al., 2010]). (Goodwin et al., 2015, 

p. 4-12) 

Reductions varied by jurisdiction; using Poisson regression, the McCartt, Hellinga, Strouse, 

et al., 2010, study found that, five years later, rates of handheld phone use were 43 percent lower 

in the District of Columbia than would have been expected without the ban, 65 percent lower in 

Connecticut 3.5 years later, and 24 percent lower in New York seven years later, based on 

observed use by drivers. The actual observed rates of cell phone use were quite low: 1.1 percent 

immediately after the ban in New York (McCartt and Geary, 2004) and 3.5 percent in 

Washington, D.C. (McCartt and Hellinga, 2007). McCartt, Hellinga, Strouse, et al., 2010, 

suggests that one possible reason for this difference is differences in intensity of enforcement. 

However, it is unknown whether these laws lead to increased use of hands-free 

devices. (Goodwin et al., 2015, p. 4-12) 

The effectiveness of hand-held cell phone bans in reducing crashes is unclear. 

Nikolaev, Robbins, and Jacobson (2010) examined driving injuries and fatalities 

in 62 counties in New York State both before and after a hand-held cell phone 

ban took effect. Forty-six counties showed a significant decrease in injury 

crashes following the ban, and 10 counties showed a less-significant decrease in 

fatal crashes. Although encouraging, the study did not include a control group to 

account for other factors that may have decreased crashes. A study by the 

Highway Loss Data Institute (HLDI) investigated State-level automobile 

insurance collision claims in California, Connecticut, New York and the District 

of Columbia. When compared to neighboring States, there was no change in 

collision claim frequency after these jurisdictions implemented hand-held cell 

phone bans ([“Hand-Held Cellphone Laws and Collision Claim Frequencies,” 

2009]). However, the data from the Highway Loss Data Institute is proprietary 

and an independent analysis of the data has not been conducted. Also, not all 

crashes result in a collision claim, so collision claim rates may differ from crash 

rates. (Goodwin et al., 2015, p. 4-12) 

A 2012 study comparing the effectiveness of a cell phone ban in urban versus rural areas 

found that the ban decreased crashes in the urban area by 1.6 per 1,000 residents but was actually 

associated with a small, nonsignificant increase in the number of crashes in the rural area (0.5 per 

1,000 residents) (Jacobson et al., 2012). A 2013 study of New Jersey’s cell phone ban, 

comparing three years pre- and postban, found no significant change in fatalities due to cell 

phone use (Maher and Ott, 2013). Burger, Kaffine, and Yu did not find any evidence that a 2008 

ban on handheld phone use in California reduced crashes, comparing six months before and after 

the ban (Burger, Kaffine, and Yu, 2014). A 2014 review of 11 existing studies on the 

effectiveness of bans (seven cell phone bans, two texting bans, and two types of multiple-factor 

bans) found mixed results. Of the seven cell phone bans, two showed signification reductions in 

crash rates for the jurisdictions studied, one found no significant reduction, and four found 

signification reductions in some locations and circumstances (e.g., poor weather conditions) but 

not in others. For the texting and multiple-factor bans, results were also mixed. All studies had 

57 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

some methodological concerns, some more problematic than others (McCartt, Kidd, and Teoh, 

2014). 

A 2016 study looked at a period of 19 years in multiple states at four types of bans: primary 

cell phone ban, secondary cell phone ban (meaning that the driver can be pulled over only for 

committing another offense), primary texting bans, and secondary texting bans. The authors 

found that primary cell phone bans reduced fatalities by 10 percent, while secondary bans had 

essentially zero impact. Texting bans were less effective than primary cell phone bans. The 

results were consistent even when controlling for the effects of the 2008–2009 recession and the 

price of gasoline, but the magnitude of the estimated effect was substantially reduced (e.g., the 

primary cell phone ban reduced fatalities by 3 percent) when controlling for state-specific time 

trends (Rocco and Sampaio, 2016). 

A 2016 review specifically of cell phone bans and their effectiveness for young drivers 

looked at 11 studies and determined, “Overall, evidence of the effectiveness of young drivers’ 

cell phone restrictions is inconclusive” (Ehsani, Ionides, et al., 2016, p. 35). Lim and Chi reached 

a similar conclusion: “We found that handheld cell phone bans targeting all drivers reduced fatal 

crashes involving young drivers, but there was insufficient evidence that complete cell phone 

bans targeting only young drivers reduced fatal crashes” (Lim and Chi, 2013). 

Two studies have examined the effectiveness of laws prohibiting texting while 

driving. One study evaluated the effect of a texting ban in Michigan ([Ehsani, 

Bingham, et al., 2014]); the other examined insurance collision claims in States 

with texting bans compared to neighboring States without such bans ([“Texting 

Laws and Collision Claim Frequencies,” 2010]). Both studies found small 

increases in various types of crashes and collision claims following enactment of 

texting bans. One possible explanation is that texting drivers attempt to avoid 

detection by hiding their phones from view, which may result in more time with 

drivers’ eyes off the roadway. (Goodwin et al., 2015, p. 4-12; emphasis in the 

original) 

A study in 2014 showed that states with primary texting bans had, on average, a 3-percent 

reduction in fatalities (Ferdinand et al., 2014). 

Results from the NHTSA HVE program suggest [that] hand-held cell phone use 

among drivers dropped 57% in Hartford and 32% in Syracuse (Chaudhary et al., 

2014). The percentage of drivers observed manipulating a phone (e.g., texting or 

dialing) also declined. Public awareness of distracted driving was already high 

before the program, but surveys suggest [that] awareness of the program and 

enforcement activity increased in both Hartford and Syracuse. Surveys also 

showed [that] most motorists supported the enforcement activity. In California 

and Delaware, similar reductions in cell phone use were observed following the 

campaign, although decreases were also noted in comparison communities 

([Schick, Vegega, and Chaudhary, 2014]). Although the results are encouraging, 

the effect of these HVE campaigns on crashes is unknown. Note that the 

evidence for effectiveness is based on community and smaller statewide 

programs that targeted hand-held cell phone use. There is no evidence available 

58 
 

 

 

 

 

 

 

 

 

 

 

that HVE programs targeting texting will be as effective. (Goodwin et al., 2015, 

pp. 4-14–4-15) 

An issue complicating the enforcement of texting bans is the difficulty in detecting whether a 

driver is texting. Wilson and Stimpson, 2010, makes the point that drivers who are texting might 

drive normally for long periods of time, complicating detection compared with detecting drunk 

drivers, who are more consistently impaired. 

Measuring Effectiveness 

As described above, there is considerable difficulty detecting the impact that cell phone and 

texting bans have on crashes. Most studies thus far have focused on the effect of a ban on cell 

phone usage, but this is difficult to translate into such a ban’s effect on crashes (McCartt and 

Geary, 2004; McCartt and Hellinga, 2007). Abouk and Adams, 2013; Ferdinand et al., 2014; and 

Rocco and Sampaio, 2016, use national crash data to detect effects, which could be a method for 

future evaluation. 

A further complication is variation in road conditions; worse conditions increase the danger 

of driving and can make the use of cell phones more risky. One study found that laws permitting 

only hands-free use reduce crashes, but only in bad weather or wet road conditions (Kolko, 

2009). 

As for HVE, no studies have measured its effectiveness at crash reduction. It might be 

difficult to disentangle the effect of a cell phone ban from that of an HVE campaign. 

Costs 

Goodwin et al., 2015, explains that the cost of instituting a cell phone ban consists of the 

publicizing and enforcement costs. Although not all bans are instituted in conjunction with an 

HVE campaign, the costs of the HVE campaign could likely be scaled according to local 

preferences. 

Regarding these HVE costs: 

High visibility enforcement campaigns are expensive. They require time from 

law enforcement officers to conduct the enforcement. In addition, time is needed 

from State highway safety office and media staff and often from consultants to 

develop, produce, and distribute advertising, educational materials, and other 

communications tools. In the NHTSA demonstration program, both Connecticut 

and New York received $200,000 to implement and evaluate the program, and 

each State contributed an additional $100,000 to the Federal funds. Paid media 

costs for the program in the two States were over $500,000. (Goodwin et al., 

2015, p. 4-15) 

59 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Time to Implement 

A cell phone ban can be implemented at any time. Again, it is in enforcement that issues of 

cost and timeline become more relevant. Goodwin et al., 2015, suggests that an HVE campaign 

can require four to six months to plan and implement. 

Other Issues 

Cell phone blockers: In recent years, several manufacturers have created systems 

that can block a cell phone from making (or receiving) calls while a person is 

driving. These systems detect when the phone is in motion. During that time, 

incoming calls are automatically diverted to voicemail and incoming texts are not 

shown until the driver has stopped moving. Typically, these systems allow 

exceptions for phone calls from pre-specified numbers, and all allow emergency 

calls to 911. Although these systems are potentially applicable to all drivers, they 

have largely been marketed to parents of teen drivers. Researchers at the Texas 

[A&M] Transportation Institute (TTI) tried to evaluate a cell phone disabling 

device for teens; however, they encountered difficulty recruiting families and 

very strong resistance by parents and teens to the device ([Benden, Fink, and 

Stafford, 2012]). NHTSA funded a study examining the effect of a 

filtering/blocking application on the cell phones of 44 Michigan DOT employees. 

When the application was active, participants placed and answered fewer calls 

while their [vehicles were] in motion. However, participants were not very 

accepting of the application, and the application was not completely reliable 

([Funkhouser and Sayer, 2013]). (Goodwin et al., 2015, p. 4-13) 

Voice-to-text technology: There are several applications that allow drivers to 

send and receive text messages using voice rather than manual entry. Although 

the research on these applications is limited, it appears [that] voice-to-text 

technology may offer little or no safety benefit. In a recent study, 42 participants 

drove instrumented vehicles on a closed course while texting manually or using 

one of two voice-to-text applications. In all three conditions, reaction times were 

slower and drivers spent more time looking away from the roadway (Yager, 

2013). More research is needed, but the findings suggest [that] texting impairs 

driving performance, regardless of what method of texting is used. (Goodwin 

et al., 2015, p. 4-13) 

60 
 

Appendix B. Productivity and Medical Adjustments to the Value of

a Life

Table B.1 shows the full list of adjusters and values of preventing an injury and saving a life. 

61 
Table B.1. State,Specific Price Multipliers and Values of Saving a Life and Preventing an Injury

MV MV MV 

PICCS 2.0 MV PICCS 3.0 MV MV PICCS 3.0 PICCS 3.0 

State

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 2.0 

Multiplier: 

Medical

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 3.0 

Multiplier: 

Medical

PICCS 2.0 

Value of 

Preventing 

an Injury

PICCS 2.0 

Value of 

Saving a 

Life

Value of 

Preventing 

an Injury, in 

Dollars

Value of 

Saving a 

Life, in 

Dollars

Ala. 0.839 0.898 0.8692 0.8456 20,409 1,136,673 20,488 1,385,639

Alaska 1.045 1.282 1.4063 1.0885 24,160 1,365,258 24,875 2,124,519

Ariz. 0.855 0.976 1.0203 0.8415 20,962 1,155,854 21,428 1,592,587

Ark. 0.779 0.893 0.8868 0.8162 19,897 1,073,258 20,480 1,409,378

Calif. 1.077 1.071 1.1151 1.0814 23,272 1,392,753 23,002 1,725,499

Colo. 1.063 1.010 1.0393 1.0564 22,827 1,376,185 22,420 1,621,340

Conn. 1.402 1.119 1.1203 1.3899 26,158 1,736,869 24,287 1,736,533

Del. 1.052 1.160 1.0760 1.0173 23,554 1,369,027 22,494 1,671,122

D.C. 1.582 1.058 1.0310 1.6863 27,281 1,924,863 24,924 1,617,953

Fla. 0.996 0.979 1.0009 0.9653 22,118 1,304,619 21,807 1,567,579

Ga. 0.867 0.989 0.8692 0.8456 21,130 1,168,892 21,411 1,569,985

Hawaii 1.016 1.061 1.4063 1.0885 22,725 1,328,136 23,393 1,817,048

Idaho 0.808 0.919 1.0203 0.8415 20,273 1,104,607 20,972 1,522,845

Ill. 1.044 0.993 0.8868 0.8162 22,581 1,355,647 22,292 1,592,044

Ind. 0.871 0.890 1.1151 1.0814 20,624 1,170,179 21,165 1,518,318

Iowa 0.907 0.893 1.0393 1.0564 20,931 1,208,227 21,383 1,498,474

Kan. 0.952 0.893 1.1203 1.3899 21,295 1,255,677 21,478 1,493,235

Ky. 0.806 0.938 1.0760 1.0173 20,360 1,103,061 20,685 1,455,114

La. 0.900 0.923 1.0310 1.6863 21,038 1,201,734 21,212 1,489,029

Maine 0.873 1.019 1.0009 0.9653 21,341 1,176,107 22,549 1,750,955

Md. 1.192 1.005 1.0036 0.8633 23,842 1,512,060 22,964 1,585,503

Mass. 1.271 1.250 1.1820 1.0734 25,812 1,602,615 23,904 1,753,836

62 
MV MV MV 

PICCS 2.0 MV PICCS 3.0 MV MV PICCS 3.0 PICCS 3.0 

State

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 2.0 

Multiplier: 

Medical

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 3.0 

Multiplier: 

Medical

PICCS 2.0 

Value of 

Preventing 

an Injury

PICCS 2.0 

Value of 

Saving a 

Life

Value of 

Preventing 

an Injury, in 

Dollars

Value of 

Saving a 

Life, in 

Dollars

Mich. 0.909 0.937 0.9697 0.8082 21,187 1,211,639 20,984 1,472,385

Minn. 1.063 0.986 1.0179 1.0581 22,697 1,375,474 22,540 1,642,641

Miss. 0.747 0.978 0.9659 0.8617 20,101 1,042,033 20,631 1,482,759

Mo. 0.891 0.936 0.9507 0.9390 21,036 1,192,629 21,379 1,521,415

Mont. 0.841 0.969 0.9466 0.9688 20,811 1,140,884 21,792 1,647,731

Neb. 0.945 0.906 0.9202 0.8146 21,309 1,248,681 21,295 1,449,998

Nev. 1.048 1.038 0.9441 0.9072 22,858 1,361,197 22,312 1,659,076

N.H. 1.075 1.205 1.1350 0.9389 23,984 1,394,612 23,149 1,746,076

N.J. 1.274 1.043 1.0115 1.2337 24,711 1,599,650 23,589 1,675,891

N.M. 0.815 0.963 1.1340 1.2743 20,568 1,113,291 21,328 1,569,591

N.Y. 1.227 1.070 0.9323 0.8694 24,478 1,550,890 24,247 1,892,353

N.C. 0.871 1.004 1.0548 1.0617 21,244 1,173,554 21,718 1,621,711

N.D. 0.902 0.926 0.9408 0.7692 21,070 1,203,932 21,912 1,551,432

Ohio 0.903 0.947 0.9677 0.9115 21,192 1,205,608 21,218 1,495,665

Okla. 0.885 0.947 1.0603 0.8688 21,047 1,186,628 21,283 1,509,638

Ore. 0.901 1.043 0.9150 0.9730 21,698 1,206,342 22,577 1,780,744

Pa. 1.005 0.974 1.0675 0.9857 22,163 1,313,961 21,770 1,519,802

R.I. 1.022 1.114 1.1300 1.0944 23,061 1,336,032 22,883 1,729,045

S.C. 0.803 1.000 1.0770 1.2855 20,673 1,101,733 21,155 1,558,656

S.D. 0.878 0.932 1.0035 0.8430 20,909 1,178,803 21,830 1,591,142

Tenn. 0.862 0.912 1.2358 1.1998 20,671 1,161,340 20,933 1,460,075

Texas 0.963 0.950 1.0412 0.8803 21,693 1,268,964 21,432 1,514,964

Utah 0.808 0.918 0.9887 1.0100 20,267 1,104,578 20,693 1,479,173

Vt. 0.950 1.008 0.9490 0.9011 21,904 1,256,973 22,307 1,659,737

63 
MV MV MV 

PICCS 2.0 MV PICCS 3.0 MV MV PICCS 3.0 PICCS 3.0 

State

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 2.0 

Multiplier: 

Medical

Multiplier: 

Productivity 

(Market and 

Household)

MV 

PICCS 3.0 

Multiplier: 

Medical

PICCS 2.0 

Value of 

Preventing 

an Injury

PICCS 2.0 

Value of 

Saving a 

Life

Value of 

Preventing 

an Injury, in 

Dollars

Value of 

Saving a 

Life, in 

Dollars

Va. 1.071 0.935 0.9592 0.9011 22,484 1,382,400 22,438 1,569,279

Wash. 1.047 1.149 1.1570 0.9113 23,454 1,363,429 23,329 1,809,018

W.Va. 0.765 0.915 0.9656 1.0112 19,904 1,059,148 20,872 1,488,239

Wis. 0.934 1.030 1.1180 1.0477 21,894 1,240,754 22,049 1,640,972

Wyo. 1.120 0.954 0.9958 0.8125 22,984 1,434,630 22,468 1,535,349

SOURCE: Productivity and medical adjustment factors, CDC, 2014, § 4.4. Others, calculations by the authors.

64 
 

 

 

 

 

 

 

 

 

 

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