Evidence map›Paper›PMID 31555768›Full record

ArticleArchives of academic emergency medicine2019

Factors with the Highest Impact on Road Traffic Deaths in Iran; an Ecological Study.

Alireza Razzaghi, Hamid Soori, Amir Kavousi, Alireza Abadi, Ardeshir Khosravi

Abstract read
In one paragraph

Article in Archives of academic emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alireza RazzaghiSafety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (Email: alirezarazzaghi_21@yahoo.com, ORCID id: 0000-0003-1874-6364 ).
Hamid SooriSafety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (Email: Hsoori@yahoo.com, ORCID id: 0000-0002-3775-1831).
Amir KavousiSafety Promotion and Injury Prevention Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. (Email: kavousi@sbmu.ac.ir, ORCID id: 0000-0003-3922-0564).
Alireza AbadiDepartment of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ardeshir KhosraviDepartment of Statistics and Informatics, Iranian Ministry of Health and Medical Education, Tehran, Iran. (Email: ardeshir.khosravi@gmail.com, ORCID id: 0000-0003-2963-0674).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe largest proportion of road traffic deaths (RTDs) happen in Low and Middle Income Countries (LMICs). The efforts for decreasing RTDs can be successful if there is precise information about its related risk factors. This study aimed to determine economic, population, road, and vehicle factors with the highest impacts on RTDs in Iran.

methodsThis is an ecological study, which has been done using covariates including: the population density, economic growth, urbanization, distance traveled (km) in 100 thousand people, the length of urban roads, the length of rural roads and the Vehicle per 1000 population for each province of Iran in 2015. The covariates considered had been gathered from different sources and to determine which one of the covariates has an effect on RTDs, the Negative Binomial (NB) regression model was used.

resultsThe mean number of RTDs per 100000 population was 474 ± 70.59 in 2015. The highest and lowest rates of death belonged to Fars and Qom provinces, respectively. The results of the univariate model showed the population density as the only covariate of RTDs (p=0.001). Also, among other covariates, GDP was the only variable with a p-value equal to 0.2. In the multivariate NB model, it was seen that the population density (p=0.001), and GDP (p=0.02) significantly correlated with RTDs. For a unit (Million Rial) increase in the GDP of the province, the number of deaths decreased by as much as 0.0014. In addition, for a unit increase in population density, the number of deaths went up by as much as 30.

conclusionPopulation density and GDP had positive and negative effects on the number of fatal road traffic injuries, respectively. By considering these factors in presentational and controlling programs on road traffic injuries, it is possible to decrease the RTDs.

Indexed as

accidentsDeathmortalitymultiple traumatraffic

Identifiers

PMID31555768
PMCPMC6732207

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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.