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      Road safety implications of the partial legalisation of cannabis in Germany: protocol for a quasi-experimental study

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          Abstract

          Introduction

          Germany is reforming its legal approach to cannabis, allowing the possession and cultivation of cannabis for recreational purposes. The objective of this study is to investigate the impact of the policy reform on (1) The prevalence of cannabis use in the general population and (2) Driving under the influence of cannabis (DUIC) among regular users.

          Methods and analysis

          A quasi-experimental research design will be employed, with repeated cross-sectional surveys on self-reported DUIC and cannabis use conducted at three measurement points in Germany (intervention group) and Austria (control group) over a 2-year observation period (2023–2025). Data will be collected from approximately 50 000 individuals aged between 18 years and 64 years. To minimise reporting biases in the measurement of DUIC, we will use direct and indirect assessments via crosswise model and motor vehicle accident data from official statistics. In a difference-in-difference framework, regression analyses and interrupted time series analysis will be carried out for hypothesis testing.

          Ethics and dissemination

          Participants will be informed about voluntary participation, data protection laws and the option to delete data on request. Ethical approval was obtained from the Local Psychological Ethics Committee of the Centre for Psychosocial Medicine in Hamburg, Germany (reference number: 0686). Findings will be disseminated through scientific networks and will be key for a comprehensive evaluation of the cannabis law reform. The findings will facilitate the design and implementation of road safety measures.

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          Most cited references36

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)

            COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries' subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.
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              Interrupted time series regression for the evaluation of public health interventions: a tutorial

              Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2024
                13 June 2024
                : 14
                : 6
                : e084611
                Affiliations
                [1 ] departmentCentre of Interdisciplinary Addiction Research of Hamburg University, Department of Psychiatry and Psychotherapy , Ringgold_37734University Medical Center Hamburg-Eppendorf , Hamburg, Germany
                [2 ] departmentDepartment of Psychiatry, Medical Faculty , Ringgold_37734University of Leipzig , Leipzig, Germany
                Author notes
                [Correspondence to ] Anna Schranz; a.schranz@ 123456isd-hamburg.de
                Author information
                http://orcid.org/0009-0007-9125-7360
                http://orcid.org/0000-0003-1231-3760
                Article
                bmjopen-2024-084611
                10.1136/bmjopen-2024-084611
                11177663
                38871660
                ff9dffb3-dd12-4835-b272-58239b822b58
                © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 23 January 2024
                : 03 June 2024
                Funding
                Funded by: Highway Research Institute;
                Award ID: 82.0816/2023
                Categories
                Health Policy
                1506
                1703
                Protocol
                Custom metadata
                unlocked

                Medicine
                driving under the influence,public health,legislation,observational study,prevalence
                Medicine
                driving under the influence, public health, legislation, observational study, prevalence

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