| Using Linked Data to Evaluate Motor Vehicle Crashes Involving Elderly Drivers in Connecticut |
| DOT HS 808 971 September 1999 NHTSA Report |
Crash Outcome Data Evaluation System (CODES) | ||
|
Technical Report Documentation Page | |||||
| 1. Report No. | 2. Government Accession No. | 3. Recipient's Catalog No. | |||
| DOT HS 808 971 | |||||
| 4. Title and Subtitle | 5. Report Date | ||||
| Using Linked Data to Evaluate Motor Vehicle Crashes Involving Elderly Drivers in Connecticut | September 1999 | ||||
| 6. Performing Organization Code | |||||
| 8. Performing Organization Report No. | |||||
| 7. Author(s) | |||||
| Gerald Zuckier Lenworth Jacobs, Lorna Thibeault | |||||
| 9. Performing Organization Name and Address | 10. Work Unit No. (TRAIS) | ||||
| The CT Healthcare Research and Education Foundation, Inc.
110 Barnes Road, P.O. Box 90 Wallingford, CT 06492 |
|||||
| 11. Contract or Grant No. | |||||
| DTNH-22-96-R-07266 | |||||
| 13. Type of Report and Period Covered | |||||
| 12. Sponsoring Agency Name and Address | Research Study | ||||
| National Highway Traffic Safety Administration 400 Seventh Street, S.W. Washington, DC 20590 |
|||||
| 14. Sponsoring Agency Code | |||||
| 15. Supplementary Notes | |||||
| 16. Abstract | |||||
| A deterministic algorithm was developed which allowed data from Department of Transportation motor vehicle crash records, state mortality registry records, and hospital admission and emergency department records to be linked for analysis of the impact of motor vehicle crashes on the elderly (65 years of age and over) population. Elderly drivers were involved in 8.4% of the motor vehicle crashes in Connecticut in 1995. Elderly drivers were associated with 5.2% of the linked medical records and 3.2% of the fatalities. Of the elderly drivers with linked hospital visits, 81% were treated in the emergency department and discharged; the rest were admitted to hospital, with median length of stay of 4 days. Geographically, crashes involving elderly drivers showed a bias towards more rural areas and away from the areas showing the highest overall motor vehicle crash rates. Logistic regression showed that, compared to the general population, crashes involving elderly drivers were more frequently correlated with driver illness (as reported by traffic enforcement personnel), a construction zone, violating traffic control, or failing to grant right of way, and less frequently with drinking or aggressive or dangerous driving. Conditions of diminished visibility were not identified as a significant factor, but elderly drivers were significantly more likely to be in a crash involving striking a deer. | |||||
| 17. Key Words | 18. Distribution Statement | ||||
| CODES, linked data, motor vehicle crashes, Connecticut, elderly drivers | |||||
| 19. Security Classif. (of this report) | 20. Security Classif. (of this page) | 21. No. of Pages | 22. Price | ||
| Form DOT F 1700.7 (8-72) | Reproduction of completed page authorized | ||||
This document is available to the public from the National Technical Information Service, Springfield, Virginia 22161
This publication is distributed by the U.S. Department of Transportation, National Highway Traffic Safety Administration, in the interest of information exchange. The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the Department of Transportation or the National Highway Traffic Safety Administration.
Appendix A
Appendix B
Table 1. Summary Of 1995 Collision Analysis Input Files 7
Table 2. DOT MVC File Crash Records, by Category 8
Table 3. CHIME® Database Records, by Motor Vehicle E-Code Category 9
Table 4. CT Mortality Database MVC Records, by Location of Crash and Location of Residency 9
Table 5. Merge Algorithm for DOT and CHIME® Database 11
Table 6. Linkage Rates (CHIME® and DOT) 14
Table 7. Mean Age and Mortality by Position in Motor Vehicle and Place of Death 18
Table 8. Total Crashes by Driver's Age Group 22
Table 9. MVC Characteristics Associated with Elderly Drivers 24
Table 10. Elderly Drivers Admitted to Hospital, by DOT Injury Classification 27
Table 11. Bivariate Analysis of Characteristics with Driver's Age Group 33
Table 12. Bivariate Analysis of Characteristics with DOT Injury Classification 37
Figure 1. Linkage Rate, by Linkage Level and Crash Severity 15
Figure 2. Percentage of Crashes in Connecticut, 1995, by Town or City 16
Figure 3. Rate of Injury for CT Motor Vehicle Crashes, by Town or City 17
Figure 4. Mortality by Position in Motor Vehicle and Place of Death 18
Figure 5. Mean Age of Fatalities by Place of Death 19
Figure 6. Fatality Rate of Motor Vehicle Crashes by Town or City 20
Figure 7. Percentage of Total Mortality by Town or City 21
Figure 8. Total Crashes by Driver's Age Group 22
Figure 9. Percentage of Crashes Which Involve Elderly Drivers 25
Figure 10. Percentage of Injured Drivers Identified as Elderly 26
Figure 11. Number of Elderly Drivers Admitted to Hospital, by DOT Injury Classification 28
Figure 12 Age Distribution of Elderly Drivers Admitted to Hospital, by DOT Injury Classification 28
Figure 13. Age Distribution of Fatalities 29
| AIS | Abbreviated Injury Score |
| CAAI | Collision Analysis Auxiliary Input Files |
| CHA | Connecticut Hospital Association |
| CHIME® | Connecticut Health Information and Management Exchange |
| CHREF | Connecticut Healthcare Research and Education Foundation, Inc. |
| CTMDS | Connecticut State Mortality Data Set |
| DOT | Department of Transportation |
| ED | Emergency Department |
| ICD-9-CM | International Classification of Disease, 9th Edition Clinical Modification |
| ICF | Intensive Care Facility |
| ISS | Injury Severity Score |
| LOS | Length of Stay |
| MVC | Motor Vehicle Crash |
| SNF | Skilled Nursing Facility |
A deterministic algorithm was developed which allowed data from Department of Transportation motor vehicle crash records, state mortality registry records, and hospital admission and emergency department records to be linked for analysis of the impact of motor vehicle crashes on the elderly (65 years of age and over) population. Elderly drivers were involved in 8.4% of the motor vehicle crashes in Connecticut in 1995. Elderly drivers were associated with 5.2% of the linked medical records and 3.2% of the fatalities. Of the elderly drivers with linked hospital visits, 81% were treated in the emergency department and discharged; the rest were admitted to hospital, with median length of stay of 4 days. Geographically, crashes involving elderly drivers showed a bias towards areas that are more rural and away from the areas showing the highest overall motor vehicle crash rates. Logistic regression showed that, compared to the general population, crashes involving elderly drivers were more frequently correlated with driver illness (as reported by traffic enforcement personnel), a construction zone, violating traffic control, or failing to grant right of way, and less frequently with drinking or aggressive or dangerous driving. Conditions of diminished visibility were not identified as a significant factor, but elderly drivers were significantly more likely to be in a crash involving striking a deer.
This report examines motor vehicle crashes occurring in Connecticut during 1995, using several linked data sets. The findings reported herein illustrate the usefulness of using linked data sets to perform this type of analysis. Alone, each data set could not provide the type and depth of information provided by the group of linked data sets.
Data sets used for the study include:
The CHIMEÒ dataset identifies all people involved in a MVC (motor vehicle crash) who had inpatient, emergency, or ambulatory surgery treatment at a Connecticut facility regardless of the state in which the MVC occurred. The DOT dataset identifies all MVCs and people involved in a crash, regardless of whether or not they had treatment at a hospital. The mortality dataset identifies deaths from MVCs. It includes all deaths from MVCs in Connecticut, whether the fatality was a resident of Connecticut or not, in addition to deaths of Connecticut residents who died in MVCs outside Connecticut which were reported by the state where they died.
Linking these data sets allows in-depth analysis of motor vehicle crashes involving the elderly driver. For instance, using the Department of Transportation data set alone, we would be able to identify elderly drivers and the location of those crashes; however, no information would be available to analyze the medical outcomes and mortality stemming from the crash, or the individual and total charges to the hospital system. Linking to the CHIME® and mortality databases allowed these analyses.
What follows are a description of the linking, a statistical analysis of the data, and a summary of our findings.
This study was funded in part by the National Highway Traffic Safety Administration as part of the CODES demonstration project(1), and performed in collaboration by the Connecticut Healthcare Research and Education Foundation (CHREF, a non-profit affiliate of the Connecticut Hospital Association), the State of Connecticut Department of Transportation (DOT), and Hartford Hospital.
Motor Vehicle Crash (MVC) Data
The MVC data were obtained from the 1995 Collision Analysis Auxiliary Input (CAAI) Files. This is a database of motor vehicle crash data, owned by the State of Connecticut Department of Transportation.
There are six different record formats in the DOT files, described as follows:
Record Types 1, 2 and 4 were used for this analysis. Record Type 1 contains information pertinent to the crash as a whole, such as date and time, location and other crash-specific information. Record Type 2 identifies each vehicle or pedestrian involved in a crash, defined as a vehicle involved in a crash or a pedestrian who was struck by a vehicle involved in a crash. Record Type 4 contains information about vehicle operators, struck pedestrians, passengers, and witnesses. If more than four persons were involved in a crash, more than one person-record was created. Table 1 summarizes the number of records in these files.
Table 1. Summary Of 1995 Collision Analysis Input Files
| File Type | Number of Records |
| Type 1: Crash Summary Records | 72,677 |
| Type 2: Traffic Unit Information Records | 136,165 |
| Type 4: Included Person Records (1 - 4 persons each) | 79,931 |
The working MVC data file was constructed based on Type 1, 2 and 4 records in the DOT file. Type 1 records were merged with Type 2 records, to produce a file of one record per vehicle or pedestrian involved in a crash. The Type 4 records were converted from one record for each 1 to 4 involved persons into one record per involved person (i.e., if there were 4 people involved in a crash, the original file had one Type 4 record but the converted file has 4 records), then merged with the file of involved vehicles or pedestrians. This process produced one record for each involved person, containing all the data describing that person, as well as the specific crash and the specific vehicle. _Ref417808263 ~ categorizes the records contained in the DOT file.
Table 2. DOT MVC File Crash Records, by Category
| Number | Percent of Total | |
| Drivers | 132,918 | 72.5% |
| Passengers | 48,919 | 26.7% |
| Pedestrians | 1,518 | 0.8% |
| Witnesses | 3 | 0.0% |
| Total | 183,358 | 100.0% |
The CHIME® database was used for this analysis. Included in the CHIME® database is demographic, clinical and financial information about each patient visit occurring in Connecticut acute care hospitals.
Data were extracted from this database in a two step process. In the first step, an index file containing information about Connecticut hospital ED visits, ambulatory surgery visits, and inpatient stays during 1995 was created for all patients having an ICD-9-CM code ranging from E810 to E819 (motor vehicle traffic crash E-codes), as detailed in _Ref414764408 ~.
Table 3. CHIME® Database Records, by Motor Vehicle E-Code Category
| E-Code Category | Number | Percent of Total |
| Motor Vehicle, Driver | 23,219 | 56.79% |
| Motor Vehicle, Passenger | 11,659 | 28.52% |
| Motorcyclist | 1,191 | 2.91% |
| Other, Unspecified | 2,430 | 5.94% |
| Pedalcyclist | 697 | 1.70% |
| Pedestrian | 1,687 | 4.13% |
| Total | 40,883 | 100% |
In the second step, a medical history file containing the previous year's hospital visit information for those patients having an MVC in the index year was created. There were 40,883 records in the index CHIME® database and 12,280 records in the history CHIME® database.
Mortality data for victims of motor vehicle crashes were derived from the State of Connecticut Mortality Database (CTMDS). This database is offered to individuals and institutions from the State of Connecticut Department of Public Health, Office of Planning & Evaluation, Vital Records Bureau, and offers a comprehensive view of primary causes of mortality in Connecticut.
There were a total of 390 records selected from the state of Connecticut 1995 mortality database as possessing a motor vehicle crash related cause of death. _Ref417716030 ~ details these records by location of residency and location of crash.
Table 4. CT Mortality Database MVC Records, by Location of Crash and Location of Residency
| Residency | Location of MVC | Number | Percent of Total |
| Connecticut | Connecticut | 321 | 82% |
| Connecticut | Out of State | 51 | 13% |
| Out of State | Connecticut | 18 | 5% |
| Total | Total | 390 | 100% |
A proprietary deterministic matching algorithm was developed in the FOCUS language to merge these databases. Key variables used to link the crash and hospital data were date of crash, date of birth, date of ED visit, date of inpatient admission(s), date of death, gender, and towncode of crash. Because passenger DOT records do not specify a gender, three steps of merging were employed. The first step included only driver and pedestrian records, with gender identified in the DOT database. The second step included passenger records from the DOT database, for which gender cannot be used as a linking variable. The third step included all unmatched records from the first and second steps. This algorithm did not allow for fuzzy or probabilistic linking; however, since crash date and ED or inpatient admission date would not always be expected to match exactly, four levels of date window were allowed within each matching step.
One hundred percent complete linkage is not expected when linking the DOT crash database to the CHIME® database; for instance, if a motor vehicle crash occurred outside the state of Connecticut and the victim was taken to a Connecticut emergency room, or admitted to a Connecticut hospital, the patient would be included in the CHIME® database but not the DOT database. Conversely, anyone who had a crash occurring in the state of Connecticut and was admitted to a hospital or ED outside of Connecticut would be included in the DOT database but not in the CHIME® database. The result of this slight disjunction between the underlying pools of subjects is that the maximum linkage rate attainable will be reduced below 100% by an unknown amount, since we do not have a count of persons involved in either out of state crashes, or out of state hospital visits.
The mortality registry contains some records of Connecticut residents who die in other states, dependent on the other state's reporting them. Therefore, similarly to the above, Connecticut residents who die out of state in a crash might appear in the mortality database, but not in the DOT or CHIME® databases. Conversely, a person injured in a crash in Connecticut and admitted to a Connecticut hospital, but who eventually dies out of state, might appear in the DOT and CHIME® databases, but not in the mortality registry. Again, this would reduce the maximum attainable rate of linkage to the mortality registry, by an amount that we are not able to predict.
Table 5 describes the matching steps and levels in the merging algorithm. The output linked-dataset was inspected to verify the quality of the match.
Table 5. Merge Algorithm for DOT and CHIME® Database
| Level | Matching Strategy |
| First Step: Merge Driver Or Pedestrian Records Which Include Gender | |
| 1 | Matching variables: birth date, gender, towncode date adjustment window of 0 days (date of hospital visit equal to date of crash). |
| 2 | Matching variables: birth date, gender, towncode date adjustment window of +7 days (date of hospital visit within 7 days after date of crash). |
| 3 | Matching variables: birth date, gender, towncode date adjustment window of +30 days (date of hospital visit within 30 days after date of crash). |
| 4 | Matching variables: birth date, gender, towncode date adjustment window of +30/-1 days (date of hospital visit within 30 days after or 1 day before date of crash). |
| Second Step: Merge Passenger Records Which Do Not Include Gender | |
| 5 | Matching variables: birth date, towncode date adjustment window of 0 days (date of hospital visit equal to date of crash). |
| 6 | Matching variables: birth date, towncode date adjustment window of +7 days (date of hospital visit within 7 days after date of crash). |
| 7 | Matching variables: birth date, towncode date adjustment window of +30 days (date of hospital visit within 30 days after date of crash). |
| 8 | Matching variables: birth date, towncode date adjustment window of +30/-1 days (date of hospital visit within 30 days after or 1 day before date of crash). |
| Third Step: Merge Records With Gender Unknown Or Missing | |
| 9 | Matching variables: birth date, towncode date adjustment window of 0 days (date of hospital visit equal to date of crash). |
| 10 | Matching variables: birth date, towncode date adjustment window of +7 days (date of hospital visit within 7 days after date of crash). |
| 11 | Matching variables: birth date, towncode date adjustment window of +30 days (date of hospital visit within 30 days after date of crash). |
| 12 | Matching variables: birth date, towncode date adjustment window of +30/-1 days (date of hospital visit within 30 days after or 1 day before date of crash). |
This study was divided into two phases. The first phase analyzed all eligible DOT records to determine the distribution of the variables under examination and identify significant predictors of these variables and their odds ratios. The second phase was restricted to cases that successfully linked or merged, with a primary goal of determining the clinical events after MVCs.
The outcome variable for the first phase was the frequency of elderly drivers (defined as a driver 65 years of age or older) in MVCs and injuries. Outcome variables for the second phase of the study included length of stay (LOS), total hospital charge, mortality, and severity of injury.
Drivers' age was categorized into five subgroups: age less than 25 years, 25 to 44, 45 to 64, 65 to 74, and greater than 74 years. Length of stay was categorized into three groups: ED treated and released, inpatient with length of stay equal to 1 day, and inpatient with length of stay greater than 1 day. Total hospital charge was calculated on an unadjusted basis only, due to lack of cost/charge ratio information. Mortality was categorized as died at the crash site, Emergency Department death (died in hospital with zero length of stay), died as inpatient (died in hospital with length of stay equal to or greater than 1 day), and died after discharge. Type of injury was categorized into 5 levels (K, fatal injury; A, incapacitating injury, B, non-incapacitating injury; C, possible injury; and N, no injury), based on the DOT file's injury classification code. This classification was made at the time of the crash, based on either an involved person's self-report or the investigator's visual assessment; however, persons involved in a crash but categorized as not injured may seek treatment, and, conversely, persons categorized as injured may decline to seek hospital treatment.
Independent variables in this study were drawn from two sources, the DOT data file and the CHIME® database. Those variables included demographic, geographic, subjective, and objective factors, road and weather/season condition, police judgment/investigation, and clinical variables. Demographic variables included age (categorized into five age groups as described above), and gender (female or male). Geographic variables included location of the crash and location of the fixed object struck. Subjective factors included were speeding, following too closely, violating traffic controls, unsafe use of highway by pedestrian, etc. Objective factors included driver illness, vehicle involved in emergency, etc. Road condition included construction and road surface. Weather/seasonal variables included snow and rain. Police judgment/investigation included whether or not the driver had been drinking, and lighting conditions. Clinical variables included having at least 1 MVC and a hospital visit and admission diagnosis codes within past 1 year or 6 months. Other variables included type of motor vehicle, collision type, and injury classification. All categorical variables were converted into binary variables, as required for the analysis.
For the first phase of the study, the frequency for each outcome in the studied cohort was determined. The bivariate associations with outcome of road condition, weather/season condition, police judgment/investigation, demographic, geographic, subjective, objective, and clinical variables were evaluated, then a stepwise logistic regression model with a group of independent variables was developed, to find the significant predictors. Candidate independent variables were selected from the variables identified in the bivariate analysis as having an association with p < 0.10.
All stepwise models were constructed with an entry significance level of 0.01 and an exit significance level of 0.05, chosen to identify a parsimonious set of independent variables in the models. Partial residual plots were used to evaluate potential problematic areas of fit. Goodness-of-fit was evaluated by comparing fitted probabilities with observed value of dependent variables within deciles of probability, and calculating the corresponding observed chi-square statistic. In addition, an area under the receiver operator curve for logistic models was calculated to evaluate the predictive power of the models.
An adjusted odds ratio was derived in which each odds ratio was adjusted for all other independent variables listed. An odds ratio less than 1 indicates that a crash event with that characteristic has a lower likelihood of association with the outcome variable than without that characteristic, while an odds ratio higher than 1 indicates that a crash event with that characteristic has a higher likelihood of association with the outcome variable than without that characteristic. For each of the studies, the logistic regression model's odds ratios and 95 percent confidence intervals for predictors were reported. In addition, a chi-square test or non-parametric test was performed for each bivariate analysis.
All calculations were performed using the software systems SASÒ 6.12 (SAS Institute, Cary, NC) and STATAÒ 3.0 (STATA Corporation, College Station, TX).
There were 40,883 records selected from the CHIMEÒ data set as having motor vehicle crash related E-codes, as detailed in _Ref414764408 ~. Of these, 35,832 records (87.6%) were linked and merged. After deleting duplicate records (1,054, 2.9%), 34,778 records remained (85.1%). Of these records, 364 (1%) were excluded from future analysis due to unreliable key variables.
_Ref404488264 ~ and _Ref404488289 ~ show the linkage/merging rate of CHIME® records for each of the linkage levels described in _Ref414763821 ~, classified by crash severity index in the Type 1 record of the DOT file. Since gender is such a useful linking variable, levels 1 through 4 link drivers and pedestrians only; levels 5 through 12 link passengers (who do not have gender recorded by the DOT) and individuals with gender unrecorded by reason of incomplete or defective records.
Table 6. Linkage Rates (CHIME® and DOT)
| Level | Fatality Records Linked as % of CHIME® Records | Injury Records Linked as % of CHIME® Records | Property Damage Records Linked as % of CHIME® Records | Number Linked | Cumulative Total Linked | Cumulative Linkage Rate (%) |
| 1 | 0.5 | 38.1 | 3.4 | 17,158 | 17,158 | 42.0 |
| 2 | 0.2 | 10.7 | 0.6 | 4,726 | 21,884 | 53.5 |
| 3 | 0.0 | 0.4 | 0.0 | 144 | 22,028 | 53.9 |
| 4 | 0.0 | 0.4 | 0.1 | 202 | 22,230 | 54.4 |
| 5 | 0.1 | 9.0 | 9.6 | 7,690 | 29,920 | 73.2 |
| 6 | 0.1 | 3.6 | 2.8 | 2,633 | 32,553 | 79.6 |
| 7 | 0.0 | 1.1 | 1.2 | 923 | 33,476 | 81.9 |
| 8 - 12 | 0.0 | 1.2 | 2.0 | 1,302 | 34,778 | 85.1 |
As discussed in the Methods section, one hundred percent complete linkage is not expected when linking DOT, CHIME® database, and mortality registry files. Without a measure of the incidence of out of state crashes, hospitalizations, and deaths, the maximum possible linkage rate cannot be determined for comparison with the observed rate of 85.1%.
A total of 329 records (84% of the 390 motor vehicle crash related fatalities) from the Connecticut Mortality dataset were successfully linked and merged with the DOT and CHIME® files.
Overall, there were a total of 72,639 motor vehicle crashes reported to the DOT in the state of Connecticut during calendar 1995 (38 records of the total 72,677 were excluded due to duplication), involving 136,165 vehicles or pedestrians and 183,358 individual persons (_Ref404488369 ~ and _Ref417808263 ~); of the total persons involved in crashes, 34,778 (19%) were successfully linked to an ED visit or hospitalization (Table 6), and 329 to a mortality entry.
_Ref404488679 ~ shows a geographical view of the percentage of total crashes by town or city, calculated as the number of crashes in the index town or city divided by total crashes in the state. As can be seen, the highest rates occur in towns and cities surrounding Interstate 91 (I-91), Interstate 95 (I-95) between the New York border and New Haven, Route 15, Interstate 84 (I-84), and Interstate 395 (I-395) between I-95 and Route 6.
There are 169 towns or cities recorded in the DOT files, with crash rates ranging from 0.01% to 5.1%. The five lowest towns or cities were Lyme (0.01%), Warren (0.01%), Colebrook (0.02%), Hampton (0.02%), and Hartland (0.02%), while the five highest were New Haven (5.07%), Hartford (5.00%), Bridgeport (4.81%), Stamford (3.20%), and Norwalk (2.93%).
Rate of injury was determined as number of injuries divided by total crashes in the index town or city. _Ref404488742 ~ shows the rate of injury by town or city in the state of Connecticut. Presence of injury was determined from the DOT Type 1 record injury severity code, including fatalities or any type of injuries, but excluding property damage only.
Overall, the injury rate ranged from 23% to 70%; the five lowest town or cities were Old Lyme (22.89%), Madison (23.71%), Chester (25.00%), Essex (25.25%), and Guilford (27.17%), while the five highest were Sterling (69.57%), Hartford (63.38%), Hampton (62.50%), Windsor Locks (60.81%), and New Haven (59.82%).
_Ref417287778 ~ and _Ref418063176 ~ show mortality by position in vehicle (driver, passenger, or pedestrian) and place of death (at the crash site, emergency department [LOS = 0], inpatient [LOS > 0], or after discharge).
| Death at Crash Site | ED Death | Inpatient Death | Death After Discharge | Total | |
| Driver | 112 | 70 | 40 | 16 | 238 |
| Passenger | 26 | 12 | 5 | 2 | 45 |
| Pedestrian | 16 | 17 | 10 | 3 | 46 |
| Total | 154 | 99 | 55 | 21 | 329 |
| Mean age | 38.7 | 41.2 | 53.8 | 38.5 | 42.0 |
_Ref416689382 ~ and _Ref418063176 ~ show mean age of fatalities by place of death. Inpatient deaths tended to be older than the other classes of fatalities. There was no significant difference between males and females.
_Ref417287825 ~ shows fatality rate of crashes by town or city, determined as the number of deaths divided by number of crashes in each town or city. The mortality rate ranged from 0 to 10%, the five highest areas being Lyme (10%, 1 killed in 10 crashes), Hampton (6.25%, 1 killed in 16 crashes), Andover (4.76%, 2 killed in 42 crashes), Pomfret (4.23%, 2 killed in 71 crashes), and Canaan (4.12%, 1 killed in 24 crashes).
Figure 7 shows mortality by town or city where crash occurred, as a percent of total state mortality. By this measure, Hartford, New Haven, Bridgeport, Waterbury, and Bristol accounted for 29.5% of total state mortality. There were 59 towns or cities where mortality was zero (no one killed by crashes in those areas during 1995).
There were a total of 132,918 drivers in the DOT crash database; 6,543 (5%) did not have age recorded, leaving 126,375 for study. Their age distribution is broken down in Table 8 and Figure 8. There were 48,915 (39%) female drivers, and 77,460 (61%) male; overall, there were no significant differences in distribution of driver's age groups between males and females. Elderly drivers were defined as drivers with age greater than 64 years (10,615, 8.4%), for this study.
| Age Group | Number | Percent |
| 15 - 24 | 27,317 | 21.6% |
| 25 - 44 | 61,009 | 48.3% |
| 45 - 64 | 27,434 | 21.7% |
| 65 - 74 | 6,664 | 5.3% |
| 75 + | 3,951 | 3.1% |
| Total | 126,375 | 100.0% |
The bivariate associations between the age groups and the independent variables are detailed in Appendix . Table 9 shows the odds ratios of the independent variables (significant variables only), based on a stepwise logistic model derived from a multiple regression analysis in which each odds ratio was adjusted for all other independent variables listed. Characteristics listed are taken from the DOT motor vehicle crash reports. The odds for finding each characteristic associated with a crash involving elderly drivers (defined as 65 years of age or older) were compared against the odds for drivers 64 years of age or younger. An odds ratio less than 1 indicates that a crash event with that characteristic has a lower likelihood of association with an elderly driver, while an odds ratio higher than 1 indicates that a crash event with that characteristic has a higher likelihood of association with an elderly driver.
Table 9. MVC Characteristics Associated with Elderly Drivers
|
Characteristic |
Lower 95% Confidence Limit |
Odds Ratio |
Upper 95% Confidence Limit |
|
Vehicle type: automobile |
3.893 |
4.612 |
5.465 |
|
Contributing factor: driver illness |
2.542 |
3.208 |
4.048 |
|
No indication drinking |
1.829 |
2.411 |
3.178 |
|
1st object struck: deer |
1.209 |
1.734 |
2.488 |
|
Vehicle type: truck |
1.425 |
1.723 |
2.084 |
|
Light condition: daylight |
1.092 |
1.593 |
2.324 |
|
Vehicle type: passenger van |
1.261 |
1.591 |
2.008 |
|
At-fault traffic unit #1 |
1.078 |
1.377 |
1.760 |
|
Construction |
1.118 |
1.294 |
1.498 |
|
Contributing factor: violated traffic control |
1.159 |
1.288 |
1.431 |
|
Other roadway feature: intersection with public roadway |
1.108 |
1.231 |
1.368 |
|
Other roadway feature: intersection with private roadway |
1.097 |
1.176 |
1.262 |
|
Contributing factor: failed to grant right of way |
1.060 |
1.156 |
1.259 |
|
At intersection |
0.838 |
0.913 |
0.994 |
|
Injury type: possible injury |
0.837 |
0.896 |
0.959 |
|
Female |
0.794 |
0.827 |
0.862 |
|
Contributing factor: speed too fast |
0.621 |
0.687 |
0.759 |
|
Contributing factor: following too closely |
0.619 |
0.678 |
0.743 |
|
Light condition: dawn |
0.359 |
0.594 |
0.981 |
|
Light condition: dark - lighted |
0.394 |
0.578 |
0.847 |
|
MVC within past 1 year |
0.303 |
0.487 |
0.783 |
|
Collision type: moving object |
0.249 |
0.483 |
0.938 |
|
Collision type: overturn |
0.224 |
0.430 |
0.989 |
|
Vehicle type: motorcycle |
0.082 |
0.201 |
0.494 |
|
Based on multiple logistic regression with backward stepwise selection |
|||
Aside from vehicle type, the most significant predictor for a crash involving an elderly driver was driver illness (as identified by the investigating officer) as a contributing factor. Other significant predictors of involvement of elderly drivers in crashes were striking a deer, on a road under construction, while violating traffic control, at an intersection with public or private roadway, or while failing to grant right of way. Significant predictors of elderly drivers not being involved in crashes were drinking, high rate of speed, or following too closely, or where the vehicle overturned. This pattern of associated characteristics suggests that motor vehicle crashes in the elderly are more likely to be a result of confusing or changing stimuli than of drinking and/or aggressive driving. Elderly drivers were also significantly more likely to have been involved in a crash during day-time, and significantly less likely to be involved in a crash that occurred at night on a lit roadway, or at dawn. This may be the result of their being more likely to drive during daylight hours than after dark, relative to the rest of the population. Based on linked hospital records, elderly drivers were also significantly less likely to have had a motor vehicle crash within the past year; this implies that repeated involvement in motor vehicle crashes is not a problem in the elderly driver population.
Figure 9 and Figure 10 show the crash and injury rate of elderly drivers by town or city. The crash rate ranged from 0 to 28.6% and the injury rate ranged from 0% to 33.3%. It is clear that rural areas were associated with higher crash and injury rate for the elderly drivers. This is consistent with the result shown in Table 9 that the most frequent first object struck for elderly drivers was deer.
Figure 9. Precentage of Crashes Which Involve Elderly Drivers
Figure 10. Precentage of Injured Drivers Identified as Elderly
Of 34,778 non-duplicate CHIMEÒ records that linked to the DOT file (Table 6, 25,184 (72%) were drivers, included in this study (excluded were 1% who had unreliable key variables and 27% who were not drivers). Mean age was 32 years, with standard deviation of 15.8. Of these, 1,318 (5%) drivers were classified as elderly (age greater than 64 years), of whom (81%) were treated in the ED and released (zero length of stay), and 249 (19%) were admitted as inpatients (LOS of at least one day). Females represented 49.4% of the elderly drivers, with males representing 50.6%. The median age of the elderly drivers was 72 years, for both females and males.
Eighty one percent of the elderly drivers who were involved in crashes and had hospital care were treated and released from the ED. For the 249 elderly drivers admitted as inpatients, the median length of stay was 4 days. Of these, 220 (89%) persons had been classified at the time of the crash (in the DOT Type 4 records) as having an injury or possible injury, in addition to 6 (2%) classified with a fatal injury and 23 (9%) classified as no injury, indicating that 90% of the hospital admissions were identified as injured by the traffic safety officer at the scene of the crash.
Figure 11
and Table 10 display the number of those elderly drivers with inpatient admissions, by DOT injury classification. As might be expected, few drivers classified as fatalities by the traffic safety officer at the scene were admitted to hospital; however 9% of those classified as no injury were subsequently admitted.Table 10. Elderly Drivers Admitted to Hospital, by DOT Injury Classification
|
DOT Injury Classification |
Number |
Percent of Total |
|
K = Fatal Injury |
6 |
|
|
A = Incapacitating Injury |
89 |
|
|
B = Non-Incapacitating Injury |
81 |
|
|
C = Possible Injury |
50 |
|
|
N = No Injury |
23 |
|
|
Total |
Figure 11. Number of Elderly Drivers Admitted to Hospital, by DOT Injury Classification
Figure 12 displays the mean age of those elderly drivers with inpatient admissions, by DOT injury classification. Drivers originally classified at the crash as having fatal injuries were the oldest of these groups, with mean age 77, although the numbers are too few to establish a reliable correlation between greater age and a higher probability of being classified as fatally injured.
Figure 12 Age Distribution of Elderly Drivers Admitted to Hospital, by DOT Injury Classification
Figure 13. Age Distribution of Fatalities
Figure 13 shows mortality by age distribution of the 329 persons killed in motor vehicle crashes. Of these deaths, 238 (72%) were drivers, of whom 42 (18%) were above 64 years in age. Among these elderly driver fatalities, 14 (33%) died at the crash site, 10 (24%) in the ED, 16 (38%) after inpatient admission, and 2 (5%) died after discharge. The median length of stay for those elderly drivers who died as inpatients was 3 days. Overall, mean total charges for elderly drivers were $4,317, with total individual charges for the elderly driver fatalities ranging from $638 to $212,711 with a mean of $37,801, and total charges for those who survived ranging from $45 to $270,992 with a mean of $3,590.
This project demonstrated that the individual data sets (CAAI data, CHIME® database, ED data, and CTMDS data) can be successfully linked together, permitting sophisticated analyses that would otherwise be impossible.
The capability of linking different databases makes possible numerous important and interesting investigations. The medical database generates useful information on the type and severity of injury to organ systems that have been damaged, as well as the length of stay in the Emergency Department, the Intensive Care Units, and the hospital. The value and utility of the medical database are greatly enhanced by the ability to identify and correlate specific environmental elements, such as road conditions and time of day or night, physical conditions such as type of car and type of object struck, personal conditions such as the use of seat belts or air bags, and specific injuries to the people involved.
One benefit of this linkage is that it allows study of how similar events occurring in a crash affect different population subgroups differently. It is now possible to examine the impact of environmental and physical forces on different groups of patients and determine the differences in cost and outcome, including how elderly patients with degenerating physiology and anatomy compare to younger healthier patients. Trauma has classically been thought of as a problem of the young, since it is the leading cause of death in the younger decades; however, it has become a major problem for the elderly as well, as people live longer, are more independent, have more leisure time and more disposable income with which to enjoy their retirement. The linked databases can be used to determine what, if any, chronic diagnoses the patient had at the time of hospitalization; since certain conditions, e.g. cardiovascular disease and diabetes, can be identified as predating the crash, the linked data allow for study of how patients with differing baseline medical status fare with respect to specific types of crash injuries.
Overall, there were 72,639 drivers involved in motor vehicle crashes in the state of Connecticut during 1995, 8.4% (10,615) of whom were more than 64 years old; this is significant both in number and as a percentage. As the population ages, the percentage of elderly will increase, particularly in the coming decades as the large cohort of baby boomers graduates into the over 65 age group. It is essential to begin to identify the factors that cause motor vehicle crashes associated with elderly drivers, as well as to determine how these factors differ from those affecting younger drivers. Understanding these factors will lead to appropriate recommendations for prevention and minimization of problems.
For elderly who have been involved in a crash, it is important to determine whether they are injured and visit the hospital at a higher frequency than younger victims, and whether they generate longer lengths of stay and higher costs. In general, the elderly have more brittle bones, a higher incidence of osteoporosis and osteoarthritis, and are more susceptible to musculoskeletal injuries and fractures,. Similarly, there is a higher incidence of heart disease, diabetes, and other pre-existing medical conditions, causing higher admission rates with longer lengths of stay, higher mortality, and significantly greater costs for the elderly. Of the elderly drivers in this study involved in crashes with linked hospital records, 81% were treated in the ED and released, while 19% were admitted as inpatients. In a companion study of the same linked data files, 92% of the general population involved in a crash with linked hospital records were treated in the ED and released, while only 8% were admitted. Median length of stay for the elderly admissions was 4 days, and 3 days for the general population. Even more significantly, mean total charges for elderly drivers were $4,317, while for the general population mean total charges were only $1,779.
Driver illness was strongly associated with crashes involving the elderly, as well as with a higher severity of injury, and with striking an object, even adjusting for other factors. This finding may suggest that drivers, particularly the elderly, should be educated regarding the risks of driving while ill; on the other hand, this finding may just reflect a tendency by traffic safety officers to routinely code driver illness for any otherwise unexplained crash involving an elderly driver. The linked record allows for more detailed study of the medical condition and history of illness of drivers in crashes identified as caused by driver illness. If driver illness is reliably identified as a cause of motor vehicle crashes, it may be necessary to advise medical professionals regarding what advice to give their aging patients re driving, as a routine part of administering medical care. A related factor affecting the ability to control a vehicle is medication usage,. As patients’ pharmaceutical utilization records are incorporated into the CHIME® database, they can be merged with the rest of the linked dataset to allow identification of specific medications which, individually or in combination with other factors, are particularly problematic.
The data indicate that striking deer, construction zones, intersections with public and private roadways, violating traffic controls, and failure to grant right of way were associated with motor vehicle crashes in the elderly. These findings identify complex and confusing situations and stimuli as predictors of motor vehicle crashes involving elderly drivers, suggesting that the elderly might benefit from specific intervention regarding keeping control of the vehicle under emergency conditions. The ability to cope with multiple rapidly changing environmental stimuli can be a challenge for anyone, but this becomes more difficult with advancing age. While a large animal, such as a deer, suddenly entering the roadway can present a challenge for any driver, this may represent a special risk for the elderly. Two factors may contribute to making crashes involving deer an especially important risk factor for elderly drivers; not only are the elderly less likely to maintain control of their vehicles under confusing conditions, they are also more likely to have crashes in rural areas. In the past few years, an expanding deer population has presented many new difficulties for rural and suburban Connecticut residents; increased involvement in motor vehicle crashes may be another such contemporary problem.
This data linkage project has demonstrated that large databases from the highway safety domain and the medical domain can be linked successfully. It has shown that mortality, morbidity, cost, and outcome data can be integrated with environmental and physical crash data to yield important information. This information can be helpful in shaping public policy relative to injury prevention. Using this data, educational programs can be developed for specific population subgroups in order to decrease the rate and severity of crashes.
By this analysis, motor vehicle crashes involving elderly drivers are largely the result of driver illness or perceptual stimulus overload.
An essential next step is to test the validity of the triage criteria and the accuracy of the data generated. These elements are critical to validating information that will be used to generate public policy and safety recommendations.
The Bivariate Association between Driver’s Age Group and Predictors
Table 11. Bivariate Analysis of Characteristics with Driver's Age Group
(N=126375, driver only and without age missing)
|
Characteristic |
Total |
Age |
|||||
|
< 25 |
25 to 44 |
45 to 64 |
65 to 74 |
> 75 |
P value |
||
|
N |
N=27317 |
N=61009 |
N=27434 |
N=6664 |
N=3951 |
||
|
% |
% |
% |
% |
% |
|||
|
Mon. |
17379 |
21.12 |
48.87 |
21.7 |
5.2 |
3.1 |
0.52 |
|
Tues. |
17120 |
22.08 |
48.42 |
21.31 |
5.12 |
3.07 |
0.03 |
|
Thurs. |
18431 |
20.94 |
47.91 |
22.59 |
5.49 |
3.08 |
<0.001 |
|
Fri. |
19746 |
21.69 |
48.2 |
21.81 |
5.22 |
3.08 |
0.82 |
|
Wed |
17507 |
21.81 |
48.11 |
21.53 |
5.42 |
3.12 |
0.76 |
|
Weekend |
36192 |
21.84 |
48.23 |
21.48 |
5.23 |
3.22 |
0.27 |
|
No indication drinking |
124338 |
21.71 |
48.02 |
21.77 |
5.33 |
3.17 |
<0.001 |
|
At-fault driver |
66958 |
22.51 |
48.36 |
21.04 |
5.05 |
3.04 |
<0.001 |
|
Female |
48915 |
21.7 |
48.31 |
21.42 |
5.27 |
3.3 |
0.71 |
|
At-fault traffic unit #1 |
74734 |
22.84 |
47.53 |
21.03 |
5.32 |
3.28 |
<0.001 |
|
At-fault traffic unit #2 |
45271 |
20.3 |
48.87 |
22.52 |
5.25 |
3.06 |
<0.001 |
|
At-fault traffic unit #3 |
5004 |
17.17 |
52.2 |
23.62 |
5.1 |
1.92 |
<0.001 |
|
Collision type: pedestrian |
1122 |
20.23 |
44.21 |
25.4 |
6.33 |
3.83 |
<0.001 |
|
Involved more than 3 vehicles |
15273 |
17.93 |
51.04 |
23.64 |
5.09 |
2.3 |
<0.001 |
|
Involved 1 vehicle |
16257 |
29.05 |
48.05 |
17.3 |
3.68 |
1.91 |
<0.001 |
|
Involved 2 vehicles |
94845 |
20.93 |
47.87 |
22.15 |
5.58 |
3.47 |
<0.001 |
|
Involved more than 1 pedestrians |
1236 |
20.06 |
45.23 |
24.51 |
6.31 |
3.88 |
<0.001 |
|
Collision type: angle |
8420 |
23.1 |
43.46 |
22.05 |
6.45 |
4.94 |
<0.001 |
|
Collision type: backing |
2047 |
16.85 |
49.34 |
24.57 |
6.25 |
2.98 |
<0.001 |
|
Collision type: jackknife |
108 |
8.33 |
56.48 |
28.7 |
4.63 |
1.85 |
0.02 |
|
Collision type: head-on |
1272 |
21.86 |
50.08 |
23.11 |
3.14 |
1.81 |
0.08 |
|
Collision type: overturn |
753 |
34 |
47.81 |
15.54 |
1.86 |
0.8 |
<0.001 |
|
Collision type: parking |
758 |
12.93 |
50 |
26.25 |
6.07 |
4.75 |
<0.001 |
|
Collision type: rear-end |
47718 |
19.88 |
50.65 |
22.4 |
4.89 |
2.19 |
0.09 |
|
Collision type: sideswipe-same direction |
12157 |
17.69 |
50.79 |
23.57 |
4.89 |
3.05 |
<0.001 |
|
Collision type: turning-same direction |
5322 |
21.44 |
48.21 |
22.27 |
5.15 |
2.93 |
0.8 |
|
Median barrier: no median barrier |
116431 |
21.57 |
48.03 |
21.77 |
5.4 |
3.23 |
<0.001 |
|
Median barrier: no penetration |
8886 |
21.7 |
51.2 |
21.46 |
3.7 |
1.94 |
<0.001 |
|
Collision type: fixed object |
14630 |
30.09 |
48.05 |
16.34 |
3.66 |
1.87 |
<0.001 |
|
Construction |
2464 |
17.45 |
49.88 |
24.51 |
5.32 |
2.84 |
<0.001 |
|
Contributing factor: driving/entered on wrong side of road |
1700 |
24.53 |
48.06 |
19.41 |
5.35 |
2.65 |
<0.001 |
|
Contributing factor: driver illness |
441 |
10.2 |
39.23 |
27.44 |
14.29 |
8.84 |
<0.001 |
|
Contributing factor: speed too fast |
11732 |
28.2 |
48.88 |
18.25 |
3.44 |
1.23 |
<0.001 |
|
Contributing factor: violated traffic control |
8328 |
21.64 |
43.48 |
22.2 |
7.14 |
5.54 |
<0.001 |
|
Contributing factor: failed to grant right of way |
23814 |
22.56 |
43.39 |
21.77 |
6.87 |
5.42 |
<0.001 |
|
Contributing factor: following too closely |
40404 |
20.1 |
50.39 |
22.49 |
4.83 |
2.19 |
0.39 |
|
Collision type: turning-intersecting paths |
15688 |
22.64 |
44.22 |
21.41 |
6.74 |
4.99 |
<0.001 |
|
At intersection |
62336 |
21.26 |
47.64 |
21.86 |
5.6 |
3.65 |
<0.001 |
|
Light condition: dark - lighted |
23948 |
27.16 |
51.01 |
17.88 |
2.77 |
1.19 |
<0.001 |
|
Light condition: dark-not lighted |
6600 |
28.88 |
49.62 |
17.56 |
2.88 |
1.06 |
<0.001 |
|
Light condition: dawn |
999 |
17.42 |
52.55 |
26.33 |
3 |
0.7 |
0.41 |
|
Light condition: daylight |
91474 |
19.59 |
47.44 |
23 |
6.16 |
3.81 |
<0.001 |
|
Light condition: dusk |
2785 |
24.7 |
47 |
20.79 |
0 |
3.02 |
<0.001 |
|
Lig_Utd |
569 |
21.97 |
51.67 |
19.86 |
3.51 |
2.99 |
0.13 |
|
Collision type: moving object |
2189 |
13.16 |
54.41 |
27.18 |
3.97 |
1.28 |
<0.001 |
|
Non collision |
109 |
31.19 |
50.46 |
16.51 |
1.83 |
0 |
<0.001 |
|
Object location: on shoulder |
993 |
27.39 |
49.35 |
18.73 |
2.92 |
1.61 |
<0.001 |
|
Object location: off road and shoulder |
12139 |
31.82 |
46.78 |
15.33 |
3.96 |
2.1 |
<0.001 |
|
Object location: in roadway |
2673 |
14.55 |
53.54 |
25.89 |
4.23 |
1.8 |
<0.001 |
|
Object location: on median divider |
2795 |
27.48 |
52.77 |
15.21 |
3.11 |
1.43 |
<0.001 |
|
Collision type: sideswipe-opposite direction |
2700 |
20.59 |
49.89 |
22.11 |
5.26 |
2.15 |
0.96 |
|
Collision type: turning-opposite direction |
11240 |
22.4 |
43.39 |
21.47 |
6.98 |
5.76 |
<0.001 |
|
OthFat_0 |
43657 |
21.9 |
50.49 |
21.26 |
4.3 |
2.05 |
<0.001 |
|
Other roadway feature: intersection with public roadway |
52910 |
21.38 |
47.49 |
21.76 |
5.62 |
3.75 |
<0.001 |
|
Other roadway feature: intersection with private roadway |
29808 |
21.62 |
46.43 |
22.27 |
6.08 |
3.6 |
<0.001 |
|
1st object struck: animal other than deer |
1133 |
36.01 |
43.78 |
14.74 |
3.88 |
1.59 |
<0.001 |
|
1st object struck: curbing |
1644 |
32.73 |
45.92 |
14.42 |
4.14 |
2.8 |
<0.001 |
|
1st object struck: deer |
924 |
14.39 |
47.84 |
31.6 |
4.44 |
1.73 |
<0.001 |
|
1st object struck: highway sign/post/delineator |
647 |
27.98 |
48.53 |
17.16 |
3.71 |
2.63 |
<0.001 |
|
1st object struck: Jersey barrier |
1361 |
26.23 |
55.18 |
14.47 |
3.31 |
0.81 |
<0.001 |
|
1st object struck: metal beam guide rail |
2930 |
28.5 |
50.85 |
15.7 |
3.48 |
1.47 |
<0.001 |
|
1st object struck: tree |
1410 |
37.8 |
41.28 |
15.18 |
3.83 |
1.91 |
<0.001 |
|
1st object struck: utility pole |
1615 |
31.52 |
46.44 |
15.48 |
4.46 |
2.11 |
<0.001 |
|
1st object struck: wire rope guide rail |
2085 |
28.2 |
48.3 |
18.03 |
3.5 |
1.97 |
<0.001 |
|
2nd object struck |
4435 |
32.97 |
45.95 |
14.7 |
3.92 |
2.46 |
<0.001 |
|
Road surface: other |
197 |
24.37 |
44.67 |
24.87 |
4.06 |
2.03 |
0.59 |
|
Road surface: sand/mud/dirt or oil |
1079 |
24.1 |
49.12 |
18.44 |
5.47 |
2.87 |
0.01 |
|
Road surface: snow/slush |
6082 |
19.86 |
52.93 |
22.74 |
3.24 |
1.23 |
<0.001 |
|
SurfUtd |
536 |
20.15 |
51.68 |
20.15 |
5.22 |
2.8 |
0.84 |
|
Road surface: dry |
88782 |
21.37 |
47.61 |
21.89 |
5.63 |
3.5 |
<0.001 |
|
Road surface: ice |
2796 |
20.99 |
51.93 |
22.35 |
3.51 |
1.22 |
<0.001 |
|
Road surface: wet |
26903 |
22.8 |
48.97 |
20.96 |
4.73 |
2.54 |
<0.001 |
|
Weather: sleet/ hail |
711 |
25.6 |
50.35 |
19.97 |
3.09 |
0.98 |
<0.001 |
|
Weather: blowing sand/soil/ dirt or snow |
424 |
21.46 |
52.36 |
21.46 |
4.25 |
0.47 |
0.14 |
|
Weather: fog |
909 |
25.41 |
46.97 |
21.56 |
4.4 |
1.65 |
<0.001 |
|
Weather: other |
749 |
24.3 |
47.13 |
19.89 |
4.94 |
3.74 |
0.15 |
|
Weather: rain |
19442 |
23.17 |
49.07 |
20.94 |
4.45 |
2.37 |
<0.001 |
|
Weather: snow |
5203 |
19.53 |
53.32 |
22.66 |
3.4 |
1.1 |
0.01 |
|
WeatUtd |
647 |
21.02 |
48.22 |
22.41 |
5.72 |
2.63 |
0.7 |
|
Weather: severe cross winds |
139 |
26.62 |
50.36 |
17.27 |
2.88 |
2.88 |
0.04 |
|
Weather: no adverse condition |
98151 |
21.33 |
47.84 |
21.84 |
5.57 |
3.42 |
<0.001 |
|
Vehicle type: automobile |
104618 |
22.76 |
46.57 |
21.25 |
5.78 |
3.63 |
0.18 |
|
Vehicle type: motorcycle |
938 |
27.51 |
57.89 |
14.07 |
0.43 |
0.11 |
<0.001 |
|
Vehicle type: truck |
11578 |
16.18 |
57.26 |
22.43 |
3.16 |
0.97 |
0.97 |
|
Vehicle type: passenger van |
3849 |
11.41 |
58.79 |
26.14 |
3.04 |
0.62 |
<0.001 |
|
Airbag deployed |
3929 |
22.58 |
47.87 |
20.9 |
5.88 |
2.77 |
0.23 |
|
Injury type: incapacitating injury |
3727 |
26.51 |
45.69 |
18.51 |
5.5 |
3.78 |
<0.001 |
|
Injury type: non-incapacitating injury |
8555 |
29.53 |
44.99 |
17.51 |
4.48 |
3.5 |
<0.001 |
|
Injury type: possible injury |
19943 |
21.1 |
48.92 |
22.13 |
5.22 |
2.63 |
0.8 |
|
Injury type: fatal injury |
203 |
22.17 |
45.32 |
23.15 |
5.91 |
3.45 |
0.67 |
|
Property damage only |
68180 |
20.35 |
49.07 |
22.25 |
5.22 |
3.11 |
<0.001 |
|
Past MVC with 1 year |
1208 |
33.61 |
51.74 |
10.93 |
2.07 |
1.66 |
<0.001 |
|
Past MVC with 6 months |
449 |
35.63 |
49.89 |
11.8 |
1.34 |
1.34 |
<0.001 |
Bivariate Analysis of Characteristics by DOT Injury Classification
Table 12. Bivariate Analysis of Characteristics with DOT Injury Classification
(N=132918, Driver only)
|
Characteristic |
Total |
Incapacitating Injury |
Non-Incapacitating Injury |
Possible Injury |
Fatal Injury |
No Injury |
P value |
||||||||||||
|
N |
N=3801 |
N=8741 |
N=20381 |
N=206 |
N=99789 |
||||||||||||||
|
% |
% |
% |
% |
% |
|||||||||||||||
|
Mon. |
18280 |
2.84 |
6.73 |
15.18 |
0.19 |
75.05 |
0.564 |
||||||||||||
|
Tues. |
18009 |
2.98 |
6.84 |
15.2 |
0.21 |
74.78 |
0.135 |
||||||||||||
|
Thurs. |
19343 |
2.8 |
6.12 |
15.39 |
0.16 |
75.54 |
0.08 |
||||||||||||
|
Fri. |
20775 |
2.7 |
6.34 |
15.19 |
0.09 |
75.68 |
0.016 |
||||||||||||
|
Wed |
18450 |
2.79 |
6.92 |
15.56 |
0.17 |
74.57 |
0.195 |
||||||||||||
|
Weekend |
38061 |
2.97 |
6.57 |
15.41 |
0.14 |
74.91 |
0.45 |
||||||||||||
|
No indication drinking |
130853 |
2.77 |
6.34 |
15.37 |
0.09 |
75.43 |
<0.001 |
||||||||||||
|
At-fault driver |
70332 |
2.96 |
7.66 |
14.95 |
0.23 |
74.21 |
<0.001 |
||||||||||||
|
Female |
49672 |
3.13 |
6.53 |
20.33 |
0.1 |
69.89 |
<0.001 |
||||||||||||
|
Age > 64 years |
11212 |
3.24 |
6.46 |
14.81 |
0.19 |
75.3 |
<0.001 |
||||||||||||
|
Age missing |
5946 |
0.96 |
2.42 |
5.77 |
0.02 |
98.8 |
<0.001 |
||||||||||||
|
At-fault traffic unit #1 |
77924 |
3.7 |
8.07 |
15.92 |
0.23 |
72.08 |
<0.001 |
||||||||||||
|
At-fault traffic unit #2 |
48310 |
1.74 |
4.63 |
14.22 |
0.05 |
79.36 |
<0.001 |
||||||||||||
|
At-fault traffic unit #3 |
5268 |
1.16 |
3.4 |
16.12 |
0.02 |
79.31 |
<0.001 |
||||||||||||
|
Collision type: pedestrian |
1385 |
0.07 |
0.94 |
0.65 |
0 |
98.34 |
<0.001 |
||||||||||||
|
Involved more than 3 vehicles |
16026 |
2.98 |
5.35 |
18.34 |
0.11 |
73.22 |
<0.001 |
||||||||||||
|
Involved more than 1 pedestrians |
1513 |
0.13 |
1.39 |
1.39 |
0 |
97.09 |
<0.001 |
||||||||||||
|
Collision type: angle |
8842 |
6.19 |
10.4 |
22.03 |
0.1 |
61.28 |
<0.001 |
||||||||||||
|
Collision type: backing |
2195 |
0.91 |
2.23 |
10.52 |
0 |
86.33 |
<0.001 |
||||||||||||
|
Collision type: jackknife |
113 |
2.65 |
9.73 |
7.96 |
0 |
79.65 |
0.183 |
||||||||||||
|
Collision type: head-on |
1329 |
18.13 |
21.07 |
22.12 |
2.18 |
36.49 |
<0.001 |
||||||||||||
|
Collision type: overturn |
791 |
9.23 |
27.69 |
19.72 |
6.32 |
37.04 |
<0.001 |
||||||||||||
|
Collision type: parking |
827 |
1.45 |
2.42 |
9.31 |
0 |
86.82 |
<0.001 |
||||||||||||
|
Collision type: rear-end |
49600 |
1.44 |
3.47 |
17.21 |
0.04 |
77.83 |
<0.001 |
||||||||||||
|
Collision type: sideswipe-same direction |
13376 |
0.76 |
2.51 |
7.41 |
0.04 |
89.27 |
<0.001 |
||||||||||||
|
Collision type: turning-same direction |
5551 |
2.05 |
4.34 |
11.51 |
0.11 |
81.99 |
<0.001 |
||||||||||||
|
Median barrier: no median barrier |
122315 |
3 |
6.63 |
15.53 |
0.16 |
74.68 |
<0.001 |
||||||||||||
|
Median barrier: no penetration |
9487 |
1.3 |
5.42 |
12.94 |
0.01 |
80.33 |
<0.001 |
||||||||||||
|
Collision type: fixed object |
15443 |
4.66 |
15.68 |
15.19 |
0.4 |
64.07 |
<0.001 |
||||||||||||
|
Construction |
2584 |
1.24 |
3.95 |
11.34 |
0.04 |
83.44 |
<0.001 |
||||||||||||
|
Contributing factor: driving/entered on wrong side of road |
1921 |
10.57 |
15.36 |
19.21 |
0.16 |
54.71 |
<0.001 |
||||||||||||
|
Contributing factor: driver illness |
449 |
16.26 |
21.16 |
29.62 |
1.11 |
31.85 |
<0.001 |
||||||||||||
|
Contributing factor: speed too fast |
12242 |
3.15 |
10 |
16.79 |
0.19 |
69.87 |
<0.001 |
||||||||||||
|
Contributing factor: violated traffic control |
8775 |
5.64 |
9.14 |
19.37 |
0.08 |
65.77 |
<0.001 |
||||||||||||
|
Contributing factor: failed to grant right of way |
24746 |
3.84 |
7.39 |
16.47 |
0.04 |
72.26 |
<0.001 |
||||||||||||
|
Contributing factor: following too closely |
41907 |
1.21 |
2.8 |
16.85 |
0.01 |
79.13 |
<0.001 |
||||||||||||
|
Collision type: turning-intersecting paths |
16370 |
3.19 |
6.16 |
15.37 |
0.04 |
75.24 |
<0.001 |
||||||||||||
|
At intersection |
65651 |
3.06 |
6.1 |
16.39 |
0.06 |
74.38 |
<0.001 |
||||||||||||
|
Light condition: dark - lighted |
25956 |
3.66 |
8.47 |
15.91 |
0.23 |
71.73 |
<0.001 |
||||||||||||
|
Light condition: dark-not lighted |
6980 |
2.79 |
10.85 |
14.53 |
0.59 |
71.25 |
<0.001 |
||||||||||||
|
Light condition: dawn |
1045 |
2.58 |
9.95 |
15.22 |
0.77 |
71.48 |
<0.001 |
||||||||||||
|
Light condition: daylight |
95335 |
2.64 |
5.73 |
15.21 |
0.09 |
76.32 |
<0.001 |
||||||||||||
|
Light condition: dusk |
2919 |
2.81 |
5.93 |
15.93 |
0.27 |
75.06 |
0.252 |
||||||||||||
|
Collision type: moving object |
2290 |
0.44 |
2.71 |
3.01 |
0 |
93.84 |
<0.001 |
||||||||||||
|
Non collision |
117 |
1.71 |
9.4 |
3.42 |
0 |
85.47 |
0.005 |
||||||||||||
|
Object location: on shoulder |
1010 |
3.37 |
13.47 |
14.85 |
0.1 |
68.22 |
<0.001 |
||||||||||||
|
Object location: off road and shoulder |
12555 |
6.73 |
18.7 |
18.12 |
0.8 |
55.64 |
<0.001 |
||||||||||||
|
Object location: in roadway |
2737 |
1.35 |
4.42 |
4.86 |
0.04 |
89.33 |
<0.001 |
||||||||||||
|
Object location: on median divider |
2856 |
1.47 |
9.35 |
15.97 |
0.67 |
72.55 |
<0.001 |
||||||||||||
|
Collision type: sideswipe-opposite direction |
2918 |
5.59 |
11.86 |
17.72 |
0.48 |
64.36 |
<0.001 |
||||||||||||
|
Collision type: turning-opposite direction |
11566 |
4.72 |
9.27 |
17.49 |
0.04 |
68.48 |
<0.001 |
||||||||||||
|
Other roadway feature: intersection with public roadway |
55771 |
3.27 |
6.38 |
16.99 |
0.06 |
73.3 |
<0.001 |
||||||||||||
|
Other roadway feature: intersection with private roadway |
30911 |
2.36 |
5.36 |
14.77 |
0.04 |
77.46 |
<0.001 |
||||||||||||
|
1st object struck: animal other than deer |
1159 |
5.44 |
20.88 |
22.43 |
1.21 |
50.04 |
<0.001 |
||||||||||||
|
1st object struck: curbing |
1700 |
11.53 |
19.76 |
19.41 |
1 |
48.29 |
<0.001 |
||||||||||||
|
1st object struck: deer |
942 |
0.85 |
4.35 |
3.18 |
0 |
91.61 |
<0.001 |
||||||||||||
|
1st object struck: highway sign post, delineator |
684 |
3.95 |
14.62 |
11.55 |
0.58 |
69.3 |
<0.001 |
||||||||||||
|
1st object struck: Jersey barrier |
1391 |
1.01 |
11.29 |
19.77 |
0.14 |
67.79 |
<0.001 |
||||||||||||
|
1st object struck: metal beam guide rail |
2991 |
1.27 |
7.36 |
13.54 |
0.7 |
77.13 |
<0.001 |
||||||||||||
|
1st object struck: tree |
1443 |
9.98 |
28.27 |
22.18 |
1.52 |
38.05 |
<0.001 |
||||||||||||
|
1st object struck: utility pole |
1658 |
10.86 |
30.7 |
22.62 |
0.78 |
35.04 |
<0.001 |
||||||||||||
|
1st object struck: wire rope guide rail |
2144 |
1.77 |
8.54 |
9.84 |
0.37 |
79.48 |
<0.001 |
||||||||||||
|
2nd object struck |
4588 |
8.91 |
23.19 |
19.66 |
1.44 |
46.8 |
<0.001 |
||||||||||||
|
Road surface: other |
199 |
6.03 |
9.55 |
21.61 |
0.5 |
62.31 |
<0.001 |
||||||||||||
|
Road surface: sand, mud, dirt or oil |
1129 |
3.72 |
11.25 |
20.19 |
0.18 |
64.66 |
<0.001 |
||||||||||||
|
Road surface: snow/slush |
6361 |
1.75 |
4.76 |
13.93 |
0.09 |
79.47 |
<0.001 |
||||||||||||
|
Road surface: dry |
93540 |
2.98 |
6.72 |
15.04 |
0.16 |
75.1 |
<0.001 |
||||||||||||
|
Road surface: ice |
2921 |
2.23 |
7.57 |
16.47 |
0.21 |
73.54 |
0.013 |
||||||||||||
|
Road surface: wet |
28134 |
2.7 |
6.23 |
16.25 |
0.14 |
74.67 |
<0.001 |
||||||||||||
|
Weather: sleet, hail |
737 |
1.76 |
6.11 |
14.93 |
0.27 |
76.93 |
0.351 |
||||||||||||
|
Weather: blowing sand, soil |
454 |
3.08 |
3.3 |
19.82 |
0 |
73.79 |
0.006 |
||||||||||||
|
Weather: fog |
955 |
2.3 |
11.1 |
14.76 |
0.52 |
71.31 |
<0.001 |
||||||||||||
|
Weather: other |
785 |
2.55 |
8.66 |
17.71 |
0 |
71.08 |
0.023 |
||||||||||||
|
Weather: rain |
20349 |
2.41 |
6.06 |
16.57 |
0.09 |
74.87 |
<0.001 |
||||||||||||
|
Weather: snow |
5423 |
1.38 |
4.59 |
12.3 |
0.15 |
81.58 |
<0.001 |
||||||||||||
|
Weather: severe cross winds |
141 |
2.84 |
5.67 |
13.48 |
0 |
78.01 |
0.928 |
||||||||||||
|
Weather: no adverse condition |
103326 |
3.04 |
6.75 |
15.21 |
0.17 |
74.83 |
<0.001 |
||||||||||||
|
Vehicle type: automobile |
109031 |
2.76 |
6.4 |
16.33 |
0.13 |
74.39 |
<0.001 |
||||||||||||
|
Vehicle type: motorcycle |
975 |
23.9 |
40.31 |
17.85 |
3.18 |
14.77 |
<0.001 |
||||||||||||
|
Vehicle type: truck |
12092 |
1.72 |
4.81 |
10.25 |
0.15 |
83.07 |
<0.001 |
||||||||||||
|
Vehicle type: passenger van |
4012 |
2.27 |
3.91 |
13.11 |
0.15 |
80.56 |
<0.001 |
||||||||||||
|
Airbag deployed |
3995 |
8.14 |
21.78 |
27.46 |
0.5 |
42.13 |
<0.001 |
||||||||||||
|
MVC within past 1 year |
1214 |
10.54 |
20.51 |
32.37 |
0.33 |
36.24 |
<0.001 |
||||||||||||
|
MVC within past 6 months |
451 |
13.08 |
20.4 |
31.71 |
0.44 |
34.37 |
<0.001 |
||||||||||||
The Crash Outcome Data Evaluation System (CODES), U.S. Department of Transportation, National Highway Traffic Safety Administration, January 1996.
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