Good quality data are essential to understanding the magnitude, characteristics, and trends of the impaired driving problem. It is needed to both guide and drive the research process and help set priorities.
The consequences of poor quality data are profound and affect decision-making throughout the system. Poor quality data can result in:
There are several key pieces of data policymakers should rely upon to provide a complete understanding of the impaired driving problem and solutions to address it. These pieces of data are referred to as indicators (variables used to measure change) and can be organized into four main categories:
Social cost indicators (direct and indirect). Facilitate comparisons of the impact of road injuries with outcomes in other policy areas such as:
Outcome indicators. These indicators are used to measure the final outcomes of impaired driving crashes, injuries, and deaths. When combined with exposure data (the quantity and quality of driving), outcome indicators can facilitate comparisons across jurisdictions and reveal the prevalence of impaired driving crashes across the nation. Some examples of outcome indicators are:
Safety performance indicators. These indicators are closely linked to outcome indicators. These indicators have a causal relationship with crashes as it is the behavior leading to the outcome. These indicators may include:
Process and implementation indicators. These indicators provide insight into how well road safety management is functioning and what interventions, policies, and programs are being implemented. It is important to note any gaps or missing data can influence the value of the indicator. Also, these indicators do not allow for the measuring of the impact on impaired driving – outcome evaluations are needed to determine whether impaired driving interventions are having the desired impact. Some process and implementation indicators include: