ArticleSSM - population health2022
Identifying regions of excess injury risks associated with distracted driving: A case study in Central Ohio, USA.
Article in SSM - population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Behaviour of motorcyclists and bicyclists before and after a road safety campaign, China.Bulletin of the World Health Organization · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study examines the latent influence of spatial locations on the relative risks of crash injuries associated with distracted driving (DD) and identifies regions of excess risks for policy intervention. Using a sample of aggregated injury and fatal DD crash records for the period 2015-2019 across 1,024 census block groups in Central Ohio (i.e., the Columbus Metropolitan Area) in the United States, we investigate the role of latent effects along with several covariates such as land-use mix, sociodemographic features, and the built environment. To this end, we specifically leverage a full Bayesian hierarchical formulation with conditional autoregressive priors to account for uncertainty (i.e., spatially structured random effects) stemming from adjacent census block groups. Furthermore, we consider uncorrelated random effects from upper-level administrative units within which each block group is nested (i.e., census tracts and counties). Our analysis reveals that (1) addressing spatial correlation improves the model's performance, (2) block-group-level variability substantially explains the residual random fluctuation, and (3) intersection density appears negatively associated with the relative risks of crash injuries, while more diversified land use can increase injury risk. Based on these findings, we present spatial clusters with twice the relative risks compared to other block groups, suggesting that policies be devised to mitigate severe injuries due to DD and therefore enhance public health.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.