Publications

Export 3 results:
Sort by: [ Author  (Asc)] Title Type Year
A B [C] D E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
C
Cartier, Y, Benmarhnia T, Brousselle A.  2015.  Tool for assessing health and equity impacts of interventions modifying air quality in urban environments. Evaluation and Program Planning. 53:1-9.   10.1016/j.evalprogplan.2015.07.004   AbstractWebsite

Background: Urban outdoor air pollution (AP) is a major public health concern but the mechanisms by which interventions impact health and social inequities are rarely assessed. Health and equity impacts of policies and interventions are questioned, but managers and policy agents in various institutional contexts have very few practical tools to help them better orient interventions in sectors other than the health sector. Our objective was to create such a tool to facilitate the assessment of health impacts of urban outdoor AP interventions by non-public health experts.Methods: An iterative process of reviewing the academic literature, brainstorming, and consultation with experts was used to identify the chain of effects of urban outdoor AP and the major modifying factors. To test its applicability, the tool was applied to two interventions, the London Low Emission Zone and the Montreal BIXI public bicycle-sharing program.Results: We identify the chain of effects, six categories of modifying factors: those controlling the source of emissions, the quantity of emissions, concentrations of emitted pollutants, their spatial distribution, personal exposure, and individual vulnerability. Modifiable and non-modifiable factors are also identified. Results are presented in the text but also graphically, as we wanted it to be a practical tool, from pollution sources to emission, exposure, and finally, health effects.Conclusion: The tool represents a practical first step to assessing AP-related interventions for health and equity impacts. Understanding how different factors affect health and equity through air pollution can provide insight to city policymakers pursuing Health in All Policies. (C) 2015 The Authors. Published by Elsevier Ltd.

Chaix, B, Duncan D, Vallee J, Vernez-Moudon A, Benmarhnia T, Kestens Y.  2017.  The "Residential" Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction. Epidemiology. 28:789-797.   10.1097/ede.0000000000000726   AbstractWebsite

Background: Because of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this "residential" effect fallacy bias applicable to a large fraction of neighborhood and health studies. Methods: Our empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naive intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions). Results: There was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naive model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naive estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8. Conclusions: Commonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.

Chyderiotis, S, Beck F, Andler R, Hitchman SC, Benmarhnia T.  2019.  How to reduce biases coming from a before and after design: the impact of the 2007-08 French smoking ban policy. European Journal of Public Health. 29:372-377.   10.1093/eurpub/cky160   AbstractWebsite

Background: Smoke-free laws aim at protecting against second-hand smoke and at contributing to change smoking behaviors. Impact evaluation studies can help understand to what extent they reach their goals. Simple before and after designs are often used but cannot isolate the effect of the policy of interest. Methods: The short-term impact of the French smoking ban (2007-08) on smoking behavior outcomes was evaluated among smokers with data from the ITC project. We first conducted a before and after design on the French sample. Second, we added the UK (excluding Scotland) as a control group and finally used external pre-policy data from national surveys to control for bias arising from pre-policy trends. Results: After one year post-implementation, the smoking ban led to a decrease in seeing people smoking in bars, restaurants and workplaces [estimated risk ratios (RR) of 8.81 IC95% (5.34-14.71), 2.02 (1.79-2.31) and 1.24 (1.16-1.33), respectively], as well as an increase in support for the smoke-free policy, but only in bars and restaurants [RR of 1.35 (1.15-1.61) and 1.25 (1.16-1.35)], respectively. No impact was found on smoking behaviors and on having a strict no smoking policy at home. The simple before and after design systematically overestimated the smoking ban's impact [e.g. RR of 29.9 (20.06-44.56) for observed smoking in bar, compared to 13.21 (7.78-22.42) with the control group, and 8.81 (5.34-14.71) with the correction from external data]. Conclusion: When data are lacking to conduct quasi-experimental designs for impact evaluation, the use of external data could help understand and correct pre-policy trends.