Christelle Clary

Association between Activity Space Exposure to Food Establishments and Individual Risk of Overweight

Objective Environmental exposure to food sources may underpin area level differences in individual risk for overweight. Place of residence is generally used to assess neighbourhood exposure. Yet, because people are mobile, multiple exposures should be accounted for to assess the relation between food environments and overweight. Unfortunately, mobility data is often missing from health surveys. We hereby test the feasibility of linking travel survey data with food listings to derive food store exposure predictors of overweight among health survey participants. Methods Food environment exposure measures accounting for non-residential activity places (activity spaces) were computed and modelled in Montreal and Quebec City, Canada, using travel surveys and food store listings. Models were then used to predict activity space food exposures for 5,578 participants of the Canadian Community Health Survey. These food exposure estimates, accounting for daily mobility, were used to model self-reported overweight in a multilevel framework. Median Odd Ratios were used to assess the proportion of between-neighborhood variance explained by such food exposure predictors. Results Estimates of food environment exposure accounting for both residential and non-residential destinations were significantly and more strongly associated with overweight than residential-only measures of exposure for men. For women, residential exposures were more strongly associated with overweight than non-residential exposures. In Montreal, adjusted models showed men in the highest quartile of exposure to food stores were at lesser risk of being overweight considering exposure to restaurants (OR = 0.36 [0.21–0.62]), fast food outlets (0.48 [0.30–0.79]), or corner stores (0.52 [0.35–0.78]). Conversely, men experiencing the highest proportion of restaurants being fast-food outlets were at higher risk of being overweight (2.07 [1.25–3.42]). Women experiencing higher residential exposures were at lower risk of overweight. Conclusion Using residential neighbourhood food exposure measures may underestimate true exposure and observed associations. Using mobility data offers potential for deriving activity space exposure estimates in epidemiological models.

Addressing equity in interventions to reduce air pollution in urban areas: a systematic review

ObjectivesWe did a systematic review to assess quantitative studies investigating the association between interventions aiming to reduce air pollution, health benefits and equity effects.MethodsThree databases were searched for studies investigating the association between evaluated interventions aiming to reduce air pollution and heath-related benefits. We designed a two-stage selection process to judge how equity was assessed and we systematically determined if there was a heterogeneous effect of the intervention between subgroups or subareas.ResultsOf 145 identified articles, 54 were reviewed in-depth with eight satisfying the inclusion criteria. This systematic review showed that interventions aiming to reduce air pollution in urban areas have a positive impact on air quality and on mortality rates, but the documented effect on equity is less straightforward.ConclusionsIntegration of equity in evidence-based public health is a great challenge nowadays. In this review we draw attention to the importance of considering equity in air pollution interventions. We also propose further methodological and theoretical challenges when assessing equity in interventions to reduce air pollution and we present opportunities to develop this research area.

Between exposure, access and use: Reconsidering foodscape influences on dietary behaviours.

Abstract Good accessibility to both healthy and unhealthy food outlets is a greater reality than food deserts. Yet, there is a lack of conceptual insights on the contextual factors that push individuals to opt for healthy or unhealthy food outlets when both options are accessible. Our comprehension of foodscape influences on dietary behaviours would benefit from a better understanding of the decision‐making process for food outlet choices. In this paper, we build on the fundamental position that outlet choices are conditioned by how much outlets’ attributes accommodate individuals’ constraints and preferences. We further argue that food outlets continuously experienced within individuals’ daily‐path help people re‐evaluate food acquisition possibilities, push them to form intentions, and shape their preferences for the choices they will subsequently make. Doing so, we suggest differentiating access, defined as the potential for the foodscape to be used at the time when individuals decide to do so, from exposure, which acts as a constant catalyst for knowledge, intention, preferences and routine tendency. We conclude with implications for future research, and discuss consequences for public policy. HighlightsExposure and access to the foodscape are distinctly defined.The intertwined links between exposure to, access to, and use of, the foodscape are conceptualised.The way the foodscape is used is conditioned by how much outlets’ attributes accommodate individuals’ constraints and preferences.Foodscapes exposure helps re‐evaluate acquisitions possibilities and shapes intentions and preferences.More studies using activity‐space, life‐course and relational approaches are needed.

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data

BackgroundIncreases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data.MethodsThe Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability.ResultsApplying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals’ travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%).ConclusionWe present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.

Housing, neighbourhood and sociodemographic associations with adult levels of physical activity and adiposity: baseline findings from the ENABLE London study

Objectives The neighbourhood environment is increasingly shown to be an important correlate of health. We assessed associations between housing tenure, neighbourhood perceptions, sociodemographic factors and levels of physical activity (PA) and adiposity among adults seeking housing in East Village (formerly London 2012 Olympic/Paralympic Games Athletes’ Village). Setting Cross-sectional analysis of adults seeking social, intermediate and market-rent housing in East Village. Participants 1278 participants took part in the study (58% female). Complete data on adiposity (body mass index (BMI) and fat mass %) were available for 1240 participants (97%); of these, a subset of 1107 participants (89%) met the inclusion criteria for analyses of accelerometer-based measurements of PA. We examined associations between housing sector sought, neighbourhood perceptions (covariates) and PA and adiposity (dependent variables) adjusted for household clustering, sex, age group, ethnic group and limiting long-standing illness. Results Participants seeking social housing had the fewest daily steps (8304, 95% CI 7959 to 8648) and highest BMI (26.0 kg/m2, 95% CI 25.5kg/m2 to 26.5 kg/m2) compared with those seeking intermediate (daily steps 9417, 95% CI 9106 to 9731; BMI 24.8 kg/m2, 95% CI 24.4 kg/m2 to 25.2 kg/m2) or market-rent housing (daily steps 9313, 95% CI 8858 to 9768; BMI 24.6 kg/m2, 95% CI 24.0 kg/m2 to 25.2 kg/m2). Those seeking social housing had lower levels of PA (by 19%–42%) at weekends versus weekdays, compared with other housing groups. Positive perceptions of neighbourhood quality were associated with higher steps and lower BMI, with differences between social and intermediate groups reduced by ~10% following adjustment, equivalent to a reduction of 111 for steps and 0.5 kg/m2 for BMI. Conclusions The social housing group undertook less PA than other housing sectors, with weekend PA offering the greatest scope for increasing PA and tackling adiposity in this group. Perceptions of neighbourhood quality were associated with PA and adiposity and reduced differences in steps and BMI between housing sectors. Interventions to encourage PA at weekends and improve neighbourhood quality, especially among the most disadvantaged, may provide scope to reduce inequalities in health behaviour.