Nazif-Muñoz · Journal of epidemiology and community health 2020 · interrupted time-series analysis · n=?

State or market? How to effectively decrease alcohol-related crash fatalities and injuries.

Cited 23 times in the scientific literature.

Level 3 - non-randomized controlled study

Quasi-experimental interrupted time-series evaluated by non-clinical study design analogy.

PubMed 32238476 · doi:10.1136/jech-2019-213191 · record verified 2026-08-31

What was done

Interrupted time-series analyses were conducted using weekly alcohol-related traffic fatalities and injuries per 1,000,000 population across three Chilean urban conglomerates (Santiago, Valparaíso, and Concepción) from 2010 to 2017. The study evaluated the effects of two state legislative interventions—the Zero Tolerance Law (ZTL, reducing legal blood alcohol concentration) and the Emilia Law (EL, increasing penalties for drunk drivers)—and the market entry of Uber ridesharing.

What was found

In Santiago, ZTL was associated with a 29.1% decrease (95% CI 1.2 to 70.2), EL with a 41.0% decrease (95% CI 5.5 to 93.2), and Uber entry with a non-significant 28.0% decrease (95% CI -6.4 to 78.5) in weekly alcohol-related traffic casualties. In Concepción, EL was associated with a 28.9% decrease (95% CI 4.3 to 62.7). In Valparaíso, ZTL was associated with a -0.01 decrease (95% CI -0.02 to -0.00) in the casualty trend. State interventions consistently coincided with reductions in alcohol-related casualties, while ridesharing entry did not reach statistical significance.

Why it matters

This study indicates that statutory penalties and lowered legal blood alcohol thresholds reduce traffic fatalities and injuries, whereas private ridesharing availability alone did not produce statistically detectable declines in alcohol-related crashes.

Limits

The total count of participants or events is not reported in the abstract. The ecological design precludes individual-level verification of ridesharing use or alcohol consumption prior to driving. Effect estimates had wide confidence intervals, and findings varied by municipality, suggesting unmeasured local confounders such as differing traffic enforcement intensity.

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