Evaluating the causal effects of cellphone distraction on crash risk using propensity score methods

被引:28
作者
Lu, Danni [1 ]
Guo, Feng [1 ,2 ]
Li, Fan [3 ]
机构
[1] Virginia Tech, Dept Stat, 406A Drillfield Dr, Blacksburg, VA 24061 USA
[2] Virginia Tech, Transportat Inst, 3500 Transportat Res Driver, Blacksburg, VA 24061 USA
[3] Duke Univ, Dept Stat Sci, 122 Old Chem Bldg, Durham, NC 27708 USA
关键词
Causal inference; Covariate balance; Propensity score; Cellphone distraction; Naturalistic driving study; Crash risk; MOBILE PHONE USE; POTENTIAL OUTCOMES; CELL PHONE; SAFETY; INFERENCE;
D O I
10.1016/j.aap.2020.105579
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Introduction/objective: This paper evaluates the causal effects of cellphone distraction on traffic crashes using propensity score weighting approaches and naturalistic driving study (NDS) data. Methods: We adopt three propensity score weighting approaches to estimate the causal odds ratio (OR) of cellphone use on three different event-populations, including average treatment effect (ATE) on the whole population, average treatment effect on the treated population (ATT), and average treatment effect on the overlapping population (ATO). Three types of cellphone distractions are evaluated: overall cellphone use, talking, and visual-manual tasks. The propensity scores are estimated based on driver, roadway, and environmental characteristics. The Second Strategic Highway Research Program NDS data used in this study include 3400 participant drivers with 1047 severe crashes and 19,798 random case-cohort control driving segments. Results: The study reveals several highly imbalanced potential confounding factors among cellphone use groups, e.g., income, age, and time of day, which could lead to biased risk estimation. All three propensity score approaches improve the balance of the baseline characteristics. The propensity score adjusted ORs differ from unweighted ORs substantially, ranging from -44.25% to 54.88%. Specifically, the adjusted ORs for young drivers are higher than unweighted ORs and these for middle-age drivers are lower. Among different cellphone related distractions, the ORs associated with visual-manual tasks (OR range: 3.47-6.63) are uniformly higher than overall cellphone distraction and cellphone talking (OR range: 0.63-4.15). Cellphone talking increases the risk for young drivers but has no significant impact on middle-age drivers. Conclusion: Propensity score approaches effectively mitigate potential confounding effect caused by imbalanced driver environmental characteristics in the observational NDS data. The ATT and ATO estimands are recommended for NDS case-cohort studies. ATT reflects the effect among exposed events, i.e. crashes or controls with cellphone exposure and ATO reflects the effect among events with similar characteristics. The study confirms the significant causal effect of overall cellphone distraction on crash risk and the heterogeneity in safety impact by age group.
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页数:9
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共 47 条
  • [1] Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods
    Ahmed, Ali
    Husain, Ahsan
    Love, Thomas E.
    Gambassi, Giovanni
    Dell'Italia, Louis J.
    Francis, Gary S.
    Gheorghiade, Mihai
    Allman, Richard M.
    Meleth, Sreelatha
    Bourge, Robert C.
    [J]. EUROPEAN HEART JOURNAL, 2006, 27 (12) : 1431 - 1439
  • [2] [Anonymous], TRANSPORTATION RES R
  • [3] [Anonymous], 2013, IMPACT HAND HELD HAN
  • [4] [Anonymous], STATUS REP
  • [5] [Anonymous], 2014, J EMERGENCY MED
  • [6] [Anonymous], 811059 NHTSA DOT HS
  • [7] The driver-level crash risk associated with daily cellphone use and cellphone use while driving
    Atwood, Jon
    Guo, Feng
    Fitch, Greg
    Dingus, Thomas A.
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2018, 119 : 149 - 154
  • [8] Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples
    Austin, Peter C.
    [J]. STATISTICS IN MEDICINE, 2009, 28 (25) : 3083 - 3107
  • [9] Variable selection for propensity score models
    Brookhart, M. Alan
    Schneeweiss, Sebastian
    Rothman, Kenneth J.
    Glynn, Robert J.
    Avorn, Jerry
    Sturmer, Til
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2006, 163 (12) : 1149 - 1156
  • [10] Cramer H., 1999, Mathematical methods of statistics