In a recent paper by Melis and colleagues [1], exposure to certain built environment characteristics-urban density and accessibility to public transit-is found to be related to mental health, even more so among women, the elderly, and the residentially stable (interactions between built environment and individual characteristics in relation to mental health have unfortunately not been tested statistically, which could have strengthened their demonstration).[...]

Chaix, Basile

Kestens, Yan

Shareck, Martine

Vallée, Julie

International Journal of Environmental Research and Public Health

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Comments on Melis et al. The Effects of the Urban Built Environment on Mental Health: A Cohort Study in a Large Northern Italian City. Int. J. Environ. Res. Public Health, 2015, 12, 14898–14915

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Yan Kestens

Basile Chaix

Martine Shareck

Julie Vallée

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In a recent paper by Melis and colleagues [1], exposure to certain built environment characteristics—urban density and accessibility to public transit—is found to be related to mental health, even more so among women, the elderly, and the residentially stable (interactions between built environment and individual characteristics in relation to mental health have unfortunately not been tested statistically, which could have strengthened their demonstration).[...

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Kestens, Y; Chaix, B; Shareck, M; Valle, J (2016) Comments on Meliset al. The Effects of the Urban Built Environment on Mental Health:A Cohort Study in a Large Northern Italian City. Int. J. Environ.Res. Public Health, 2015, 12, 14898-14915. International journal ofenvironmental research and public health, 13 (3). ISSN 1661-7827DOI: 10.3390/ijerph13030250Downloaded from: http://researchonline.lshtm.ac.uk/2534143/DOI: 10.3390/ijerph13030250Usage GuidelinesPlease refer to usage guidelines at http://researchonline.lshtm.ac.uk/policies.html or alterna-tively contact researchonline@lshtm.ac.uk.Available under license: http://creativecommons.org/licenses/by/2.5/International  Journal  ofEnvironmental Researchand Public HealthCommentComments on Melis et al. The Effects of the UrbanBuilt Environment on Mental Health: A Cohort Studyin a Large Northern Italian City. Int. J. Environ. Res.Public Health, 2015, 12, 14898–14915Yan Kestens 1,2,*, Basile Chaix 3, Martine Shareck 4 and Julie Vallée 51 Research Theme of Risks, Prevention and Health Promotion, Montreal University Research Center (CRCHUM),850, Rue St.-Denis, Montréal, QC H2X 0A9, Canada2 Department of Social and Preventive Medicine, École de Santé Publique de l’Université de Montréal (ESPUM),7101, Rue du Parc, Montréal, QC H3N 1X9, Canada3 Inserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis research team,27 Rue Chaligny, 75571 Paris, France; basile.chaix@upmc.fr4 Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine,15-17 Tavistock Place, London WC1H 9SH, UK; martine.shareck@lshtm.ac.uk5 Centre National de la Recherche Scientifique (CNRS), UMR Géographie-Cités, 13 Rue du Four,75006 Paris, France; julie.vallee@parisgeo.cnrs.fr* Correspondence: yan.kestens@umontreal.ca; Tel.: +1-514-890-8000Academic Editors: Harry Timmermans, Astrid Kemperman, Pauline van den Berg and Paul B. TchounwouReceived: 8 January 2016; Accepted: 17 February 2016; Published: 24 February 2016In a recent paper by Melis and colleagues [1], exposure to certain built environmentcharacteristics—urban density and accessibility to public transit—is found to be related to mentalhealth, even more so among women, the elderly, and the residentially stable (interactions betweenbuilt environment and individual characteristics in relation to mental health have unfortunately notbeen tested statistically, which could have strengthened their demonstration). The authors argue thatthis may be because these groups spend more time in their neighbourhoods [1]. Other studies haveassociated the length of time spent in home neighborhoods with stronger links to health outcomes, e.g.,Chum, A., et al. [2], or mentioned this hypothesis [3,4]. One interpretation of such findings may be, asthe authors suggest, that some sort of dose-response relationship is at play—the longer the exposure,the stronger the influence. However, this interpretation also reveals limitations in how environmentalexposure is being measured, and, more globally, in how little actual processes linking exposure tohealth are being documented and understood, i.e., “how environments actually get under the skin”.Regarding measures of environmental exposure, a large body of research is still focusing onresidential environments only. However, following early calls to avoid local [5] or residential traps [6],and acknowledging studies that showed differences in exposures between home and non-residentialdestinations [7–10], activity space approaches attempting to account for daily mobility and multipleexposures are increasingly being used. Such approaches try to account for non-residential exposures,by using mobility data from surveys of regular destinations [11,12] or from GPS tracking [13], byintegrating information on time spent within and outside the residential neighbourhood, or by usingmeasures of concentration of activities within/outside the residential neighbourhood [3,4]. As peoplemove around to conduct their daily activities, they may visit a number of non-residential places.Consequently, they are exposed to sometimes contrasting environments while spending time at theselocations or during trips along routes. Ignoring such non-residential exposures is problematic bothbecause there are reasons to believe that they may also play a role (unless there is a specific a priorihypothesis that only residential exposure has an influence) and because doing so may translate intosystematic bias, or misclassification.Int. J. Environ. Res. Public Health 2016, 13, 250; doi:10.3390/ijerph13030250 www.mdpi.com/journal/ijerphInt. J. Environ. Res. Public Health 2016, 13, 250 2 of 4Our first argument is to reiterate previous statements that ignoring non-residential environmentsprovides a partial picture of actual exposure. This means that providing comprehensive measures ofexposure implies accounting for the various locations and corresponding contrasting exposuresthat people experience. Doing so does not mean, however, that all locations are of the sameimportance. Because precise processes linking environments to health remain largely unknown,more comprehensive data collection on daily mobilities, activities and conditions of environmentalexposure is needed. Beyond the multiple exposures themselves, other dimensions, yet undocumented,may indeed play a role in the dose-response relationship linking exposure to health. Exposure in somedestinations may play a larger role than in others because of the nature of activities that are conductedat these locations—e.g., workplace vs. leisure place—because of the influence of time of day or day ofthe week, because social dimensions, such as co-presence or absence of social network members modifyhow environments are perceived, or because other psychosocial dimensions render ‘that place at thatmoment’ specifically important. In other words, not only should multiple destinations be consideredto improve specificity in exposure measures, but there should also be a much richer documentation ofthe conditions under which these exposures are being experienced.Our second argument presents the possibility of systematic bias, as it appears that misclassificationin exposure, or the difference between actual exposures experienced throughout daily mobilities andexposure measured in the residential environments only, should be smaller among those who spendmore time in their neighbourhood, and consequently larger if they spend more time at other locations.The difference will further increase along with increasingly contrasting characteristics of these otherlocations compared to the home environment. It is important to state here that both dimensions—i.e.,the time spent within or outside the residential neighborhood, itself linked to daily mobility patterns,and the range of difference between residential and non-residential environments may vary accordingto individual profiles. This means that magnitudes of misclassification in measures of exposurebased on home environments only may be related to other traits such as age, socio-economic status,occupation, or gender [14,15], resulting in biased effect estimates.These two arguments call for a change in how research on place effects is conducted. We providea number of recommendations to incorporate such change. First, spatial data on daily mobility shouldbe collected more often and more systematically to allow specific estimates of exposures in multipledestinations and along routes. Solutions to collect such data include map-based questionnaires, GPStracking, or other surveys about where people spend time. Second, complementary information about‘what happens where, when, with whom and why’ is needed to improve our understanding of the veryprocesses linking environments to health. This is especially true when conscious or even unconsciouscognitive processes such as chronic stress and allostatic load, feelings of safety, or perceptions ofenvironmental cues that shape behaviour are involved. In short, exposure to environments needs to befurther contextualised and individualized. Accounting for complementary conditions such as activitiesbeing conducted, time of day, presence and relation with peers, or variations in individuals’ feelingsand transient mind states will contribute to an improved understanding of how environments getunder the skin. Various tools, including ecological momentary assessment, socio-spatial questionnaires,or individualised assessments through go-along methods or ethnographic geographies, can be usedto collect such information. Of course, with an increasing number of variables to be considered, andpossible explorations of various moderation hypotheses, issues of power may arise.Regarding this point, we further believe that a stronger consideration of not only environmentalexposure conditions, but rather of changes or variations thereof will help increase power and identifymeaningful associations. If environmental conditions do in fact play a role, tracking their changesand linking those changes to variations in behaviour or health will help reveal significant pathways.Relating changes in exposure to changes in health not only increases the strength of causal proof, italso increases the power for detection. Consequently, we further argue that more efforts should beinvested to assess change in environmental conditions in relation to change in health states. Suchchanges may be observed within days, through micro-level and continuous monitoring of variations inInt. J. Environ. Res. Public Health 2016, 13, 250 3 of 4environmental exposure and health across space and time within individuals, or within longer periods,with changes in environments possibly documented through space-time geographic informationsystems, and changes in health monitored through systematic longitudinal designs.More should be done to document environmental change, including the development of dataplatforms with systematic and continuous monitoring of environmental status, and further integrationof algorithms to produce indicators of environmental change. Furthermore, because changes in theenvironment often result from planned decisions, an improved monitoring of urban interventions andmodifications of built environments is warranted. With cities increasingly producing and relying onsensor networks, and people increasingly generating individualised spatio-temporal informationon mobilities and health behaviour, new data and computing capabilities can help our field ofresearch understand how people and places interact. With new possibilities also come new challenges,and increasingly multidisciplinary perspectives will be required to fully address such complexities.The development of larger cohorts with detailed and continuous monitoring of behaviour and health,including daily mobility and social contacts, should be promoted. Novel tools, such as web-basedquestionnaires, or wearable sensors, including mobile phone sensing, can help to achieve this goal.While we realise this commentary’s recommendations go well beyond the paper by Melis et al., tacklingbuilt environment effects on mental health, we acknowledge the authors’ important research questionand hope this commentary can help our field move forward.Acknowledgments: Yan Kestens holds a CIHR Applied Public Health Chair in Urban Interventions andPopulation Health.Conflicts of Interest: The authors declare no conflict of interest.References1. Melis, G.; Gelormino, E.; Marra, G.; Ferracin, E.; Costa, G. The effects of the urban built environment onmental health: A cohort study in a large northern Italian city. Int. J. Environ. Res. Public Health 2015, 12,14898–14915. [CrossRef] [PubMed]2. Chum, A.; Farrell, E.; Vaivada, T.; Labetski, A.; Bohnert, A.; Selvaratnam, I.; Larsen, K.; Pinter, T.; O’Campo, P.The effect of food environments on fruit and vegetable intake as modified by time spent at home: Across-sectional study. BMJ Open 2015, 5. [CrossRef] [PubMed]3. Vallée, J.; Cadot, E.; Roustit, C.; Parizot, I.; Chauvin, P. The role of daily mobility in mental health inequalities:The interactive influence of activity space and neighbourhood of residence on depression. Soc. Sci. Med.2011, 73, 1133–1144. [CrossRef] [PubMed]4. Vallée, J.; Chauvin, P. Investigating the effects of medical density on health-seeking behaviours using amultiscale approach to residential and activity spaces: Results from a prospective cohort study in the Parismetropolitan area, France. Int. J. Health Geogr. 2012, 11, 54. [CrossRef] [PubMed]5. Cummins, S. Commentary: Investigating neighbourhood effects on health—Avoiding the “Local Trap”.Int. J. Epidemiol. 2007, 36, 355–357. [CrossRef] [PubMed]6. Chaix, B.; Merlo, J.; Evans, D.; Leal, C.; Havard, S. Neighbourhoods in eco-epidemiologic research: Delimitingpersonal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Soc. Sci. Med. 2009, 69,1306–1310. [CrossRef] [PubMed]7. Inagami, S.; Cohen, D.A.; Finch, B.K. Non-residential neighborhood exposures suppress neighborhoodeffects on self-rated health. Soc. Sci. Med. 2007, 65, 1779–7991. [CrossRef] [PubMed]8. Shareck, M.; Kestens, Y.; Vallée, J.; Datta, G.; Frohlich, K.L. The added value of accounting for activity spacewhen examining the association between tobacco retailer availability and smoking among young adults.Tob. Control 2015. [CrossRef] [PubMed]9. Kestens, Y.; Lebel, A.; Daniel, M.; Thériault, M.; Pampalon, R. Using experienced activity spaces to measurefoodscape exposure. Health Place 2010, 16, 1094–1103. [CrossRef] [PubMed]10. Setton, E.; Marshall, J.D.; Brauer, M.; Lundquist, K.R.; Hystad, P.; Keller, P.; Cloutier-Fisher, D. The impactof daily mobility on exposure to traffic-related air pollution and health effect estimates. J. Expo. Sci.Environ. Epidemiol. 2010, 21, 42–48. [CrossRef] [PubMed]Int. J. Environ. Res. Public Health 2016, 13, 250 4 of 411. Chaix, B.; Kestens, Y.; Perchoux, C.; Karusisi, N.; Merlo, J.; Labadi, K. An interactive mapping tool to assessindividual mobility patterns in neighborhood studies. Am. J. Prev. Med. 2012, 43, 440–450. [CrossRef][PubMed]12. Stewart, T.; Duncan, S.; Chaix, B.; Kestens, Y.; Schipperijn, J.; Schofield, G. A novel assessment of adolescentmobility: A pilot study. Int. J. Behav. Nutr. Phys. Act. 2015, 12, 18. [CrossRef] [PubMed]13. Kerr, J.; Duncan, S.; Schipperijn, J. Using global positioning systems in health research: A practical approachto data collection and processing. Am. J. Prev. Med. 2011, 41, 532–540. [CrossRef] [PubMed]14. Perchoux, C.; Kestens, Y.; Thomas, F.; van Hulst, A.; Thierry, B.; Chaix, B. Assessing patterns of spatialbehavior in health studies: Their socio-demographic determinants and associations with transportationmodes (the RECORD Cohort Study). Soc. Sci. Med. 2014, 119, 64–73. [CrossRef] [PubMed]15. Shareck, M.; Frohlich, K.L.; Kestens, Y. Considering daily mobility for a more comprehensive understandingof contextual effects on social inequalities in health: A conceptual proposal. Health Place 2014, 29, 154–160.[CrossRef] [PubMed]© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons by Attribution(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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