A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking

International audienceEnvironmental health studies have examined associations between context and health with individuals as statistical units. However, investigators have been unable to investigate momentary exposures, and such studies are often vulnerable to confounding from, for example, individual-level preferences. We present a Global Positioning System (GPS)-based methodology for segmenting individuals' observation periods into visits to places and trips, enabling novel life-segment investigations and case-crossover analysis for improved inferences. We analyzed relationships between built environments and walking in trips. Participants were tracked for 7 days with GPS receivers and accelerometers and surveyed with a Web-based mapping application about their transport modes during each trip (Residential Environment and Coronary Heart Disease (RECORD) GPS Study, France, 2012–2013; 6,313 trips made by 227 participants). Contextual factors were assessed around residences and the trips’ origins and destinations. Conditional logistic regression modeling was used to estimate associations between environmental factors and walking or accelerometry-assessed steps taken in trips. In case-crossover analysis, the probability of walking during a trip was 1.37 (95% confidence interval: 1.23, 1.61) times higher when trip origin was in the fourth (vs. first) quartile of service density and 1.47 (95% confidence interval: 1.23, 1.68) times higher when trip destination was in the fourth (vs. first) quartile of service density. Green spaces at the origin and destination of trips were also associated with within-individual, trip-to-trip variations in walking. Our proposed approach using GPS and Web-based surveys enables novel life-segment epidemiologic investigations

COVID-19 vaccine hesitancy among persons living in homeless shelters in France

International audienceCOVID-19 vaccine hesitancy is frequent and can constitute a barrier to the dissemination of vaccines once they are available. Unequal access to vaccines may also contribute to socioeconomic inequalities with regard to COVID-19. We studied vaccine hesitancy among persons living in homeless shelters in France between May and June 2020 (n = 235). Overall, 40.9% of study participants reported vaccine hesitancy, which is comparable to general population trends in France. In multivariate regression models, factors associated with vaccine hesitancy are: being a woman (OR = 2.55; 95% CI 1.40–4.74), living with a partner (OR = 2.48, 95% CI 1.17–5.41), no legal residence in France (OR = 0.51, 95% CI 0.27–0.92), and health literacy (OR = 0.38, 95% CI 0.21, 0.68). Our results suggest that trends in vaccine hesitancy and associated factors are similar among homeless persons as in the general population. Dissemination of information on vaccine risks and benefits needs to be adapted to persons who experience severe disadvantage

Does the body adiposity index (BAI) apply to paediatric populations?

Objective: Validation of body adiposity index (BAI) in a paediatrics sample; and to develop, if necessary, a valid BAI for paediatrics (i.e. BAIp). Methods: A total of 1615 children (52% boys) aged 5-12 years underwent anthropometry. Their body composition was assessed using a foot-to-foot bioimpedance. The validity of BAI=(Hip circumference/Height1.5)-18 was tested by combining correlation and agreement statistics. Then, the sample was split into two sub-samples for the construction of BAIp. A regression was used to compute the prediction equation for BAIp-based percentage of body fat (%BF). Results: The initial BAI over-estimated the %BF of children by 49% (29.6±4.2% versus 19.8±6.8%; p<0.0001). The original methodology led to a BAIp=(Hip circumference/Height0.8) - 38 in children. When compared to BAI, BAIp showed both better correlation (r=0.57; p<0.01 versus r=0.74; p<0.0001) and agreement (ICC=0.34; [95% CI=-0.19-0.65] versus ICC=0.83; [95% CI=0.81-0.84]). However, there were some systematic biases between the two values of %BF as exemplified by the large 95% limit of agreement [-9.1%; 8.8%] obtained. Conclusion: BAI over-estimates the %BF in children. In contrast, BAIp appears as a new index for children's body fatness, with acceptable accuracy. In its current form, this index is valid only for large-scale studies. © 2013 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted