Search

Objectives Comprehensive smokefree laws, as recommended by the WHO Framework Convention of Tobacco Control (WHO FCTC), are the most effective tool to protect the population from secondhand smoke (SHS) and to ensure healthy environments. Studies evaluating how laws govern SHS protection are scarce. This study assessed the level of protection from SHS of laws from countries belonging to the WHO European Region. Methods A new methodology system was developed to evaluate the smokefree legislation according to the principles provided by the WHO guidelines for the correct implementation of Article 8 of the FCTC. For each law, six main sectors and 28 facilities were evaluated. Results Overall 68 laws from 48 countries from the WHO European Region were reviewed. 'Education' and 'Public transport' were the most protected sectors from SHS. Many WHO European laws do not provide protection from SHS across all public sectors. For example, 48.5% of general health facilities and 71.2% of restaurants are unprotected from SHS. The level of protection provided in the 28 facilities studied was low; many WHO European laws still allow smoking under certain conditions, permitting smoking in designated and/or ventilated areas. Conclusions Nine years after the adoption of the WHO FCTC there are still legal formulas in which smoking is allowed in several facilities, through the inclusion of separated areas, ventilated areas and other conditions. Tobacco control efforts still face the challenge of eradicating the legal clauses that prevent 100% smokefree environments. [ABSTRACT FROM AUTHOR]

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manua coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. Conclusions The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. [ABSTRACT FROM AUTHOR]