Journal of Urban Design and Mental Health 2018;4:2
EDITORIAL
Towards quantifying the role of urban place factors in the production and socio-spatial distribution of mental health in city dwellers
Chinmoy Sarkar
Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong
Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong
The World Health Organization defines mental health as “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community” (World Health Organization). Mental health disorders constitute a substantial proportion of global burden of disease in high income as well as low and middle income countries (Prince et al. 2007; Whiteford et al. 2013). It has been recently suggested that the true burden of mental illness has thus far been underestimated, accounting globally for 32·4% of the years people live with disability and 13·0% of disability-adjusted life-years (Vigo et al. 2016) as well as being a significant risk factor for mortality (Walker et al. 2015).
In recent years, the magnitude of the public health challenge posed by mental illness has been exacerbated by several ongoing demographic transitions at a global scale. First, rural to urban migration and the exponential pace of urbanization means the world's urban population is expected to rise to 60% by 2030 with one in three people living in cities of at least half a million inhabitants. Secondly, accompanying economic development and globalization are exacerbating socioeconomic disparities. Thirdly, there has been a marked increase in average life expectancy and rapid shift towards an ageing population in recent years (Bloom 2011; WHO 2011).
Given that mental health disorders have an inherently complex aetiology, being characterized by long latency between exposures and subsequent incidence and progression, a more holistic life course-based approach has been proposed for enhancing the mental capital of cities and wellbeing of its populations (Beddington et al. 2008). The importance of a such a holistic approach has been further emphasized by the recent inclusion of mental health within the sustainable development goals (WHO 2017).
The state of human health in general, its production and socio-spatial distribution across populations, as well as contextual urban scales, originates as a result of complex interplay between multiple multi-level causative factors (Sarkar and Webster 2017a). In addition to genetic, physiological and lifestyle-related factors, certain attributes of the built environment in our cities have also been implicated in prevalence and progression of mental health disorders. Specific factors such as housing, built density, design, land use heterogeneity, configuration, accessibility to key service destinations and public spaces have been evidenced to affect mental health (Halpern 1995; Evans 2003).
In a seminal study in environmental cognition, Kevin Lynch articulated that individual’s perception of urban spaces and maneuverability within them originates from a set of cognitive maps coded from discrete interconnected urban sub-spaces comprising paths (roads, walkways, transit lines), path intersections or nodes, landmarks, districts and boundaries or edges (Lynch 1960). Well-designed built environment are thus associated with a better perception of place, degree of attachment to that place, enhanced sense of community, and social capital (Burton and Mitchell 2006). Neighbourhoods with greater social capital have generally been found to produce beneficial effects upon the mental health of its residents (Almedom 2005). The built environment also influences an individual’s route choice and travel mode and thus walkability and physical activity. The protective effects of walkability and physical activity upon mental health have been evidenced (Strawbridge et al. 2002; Berke et al. 2007). The protective effects of salutogenic urban environments, especially the accessibility to, quality and design of greenspaces and blue spaces upon mental health and wellbeing have also been well established (Gascon et al. 2015). These effects originate from the inherent stress-relieving and restorative potential of these spaces as well as their capacity to support physical activity and promote social interactions.
The built and social environment configure one another and this may produce islands of stressful exposures producing negative effects upon mental health. Neighbourhood-level deprivation and socio-economic disparity have been evidenced to have a negative impacts on mental wellbeing (Kim 2008). Similar are the effects of community severance (and the loss of defensible space) originating from risk of traffic and fear of crime (Stafford et al. 2007; Mindell and Karlsen 2012). Environmental stressors evidenced to be detrimental to mental health include air (Power et al. 2015) and noise (Orban et al. 2016) pollution. Climate change, although occurring at a global scale, is experienced locally, and potentially has considerable negative impacts upon mental health (Doherty and Clayton 2011).
Although the evidence of links between built environment and mental health is increasing, the use of built environment as an intervention towards reducing the burden of mental disorders is still in its infancy and substantial challenges remain to be overcome. Almost a decade and a half after Sally Macintyre eloquently articulated the necessity to unravel the black box (unspecified urban miasma) of contextual determinants of health (Macintyre et al. 2002), we are nowadays, with the increasing advances in geospatial technologies, urban analytics, modelling algorithms, data linkage and anonymisation techniques, able to develop highly characterised measures of health-influencing environmental exposures for large population cohorts (Sarkar and Webster 2017b). Emerging data platforms such as the UK Biobank Urban Morphometrics Platform (Sarkar et al. 2015) are beginning to make very large scale individual-level built environment – health modelling a possibility. From an epidemiological and public health perspective, we are yet to gain in-depth insights into the underlying causality in the aetiology of mental illness. There is a need to progress from the current series of association studies to causal inference and prediction modeling. For the current evidence base to be reliable, we need prospective studies linking changes in urban exposures over a period of time to corresponding changes in mental health, adjusting for all other related covariates.
We are also far from unraveling the exact proportion of the effects upon mental health exerted by who we are (individual genetics and physiology), where we are (place-effects) and how we choose to live (lifestyle-effects). With the recent availability of large scale prospective cohorts with linked genetic, health and lifestyle, and exposure data (such as UK Biobank) and all-encompassing data infrastructures facilitating very large cross-cohort studies (like the UK Dementias Platform), in the pipeline, are a new breed of gene-lifestyle-environment models aiming to provide answers to these questions and generate reliable evidence base. Such large scale prospective evidence of the detrimental/beneficial effects of various urban exposures is likely to be more generalisable and help guide policies.
Urban planning finds itself at the cusp of an important transformation, re-linking with public health by orienting its focus towards a more holistic and evidence-based approach, especially in emphasizing the need to identify, anticipate and measure the social and health externalities associated with development. From an urban design perspective, the challenge is to find effective ways to retrofit and optimize designs to create multi-functional places that encourage sense of community and social capital, promote active lifestyles and minimize environmental stressors and social disparities detrimental to mental health. Such designs will of course have to be based on reliable evidence generated on the links between place, lifestyle and mental health. Hence the renewed impetus has been to build, plan and design using our cities with its resident populations as urban epidemiological laboratories (testing multiple experimental designs including observational, case-controlled, randomized controlled studies, natural experiments and quasi experiments to gather evidence). Given our urgency to minimize burden of mental disorders in our cities, it will rather be of foresight to soon envision a ubiquitously collaborative work model wherein planners/designers/architects are re-wired to work with epidemiologists, psychologists, public health and social service professionals, selling packaged residential spaces with individualised design customized to particular families and populations with specific physical and mental health outcomes and needs. These specially packaged spaces with individualized design features can be tailored for micro-environments (residences, offices, schools, hostels, care homes) as well as neighbourhood environments (streets, neighbourhoods and public spaces) and will be a step-change from the universal design guidelines that exist in a few cities of the world. They will, in essence, constitute targeted preventive interventions and help reduce the long-term direct and indirect economic burdens associated with mental health disorders.
Economics does matter. Given the laws of behavioural economics and change management, health economists can help monetize the value of healthy lifestyles and good urban designs that enhance mental capital of cities in addition to pricing negative externalities. By doing so, positive and negative health externalities of urban living can be essentially imprinted within the decision making calculus and fiscal planning and public health budgeting calculations. Such a mechanism can disincentivise lifestyles and behaviours detrimental to mental health and vice versa.
Overcoming the challenge posed by mental illnesses will also entail devising actionable policies derived from gathered evidences at multiple levels. Such policies must be systems-based, multidisciplinary and have a predictive element. Given the societal changes we are currently encountering and the associated stochasticity, probabilistic Bayesian modelling and related policy analytics may be employed as exploratory decision making tools.
The World health Organization’s slogan, “no health without mental health”, has become all the more relevant in the current global context than ever before. The way we build, plan and design our cities today; its dwellings, neighbourhoods, streets and public spaces and transport infrastructures are likely to affect the mental health and wellbeing of millions of its residents, not only today, but for generations to come. Being enduring and pervasive, planning and designing healthy cities have the potential to be at the forefront of our preventive strategies aimed at enhancing its mental capital as well as managing and minimizing the burden of mental illnesses of its residents.
In recent years, the magnitude of the public health challenge posed by mental illness has been exacerbated by several ongoing demographic transitions at a global scale. First, rural to urban migration and the exponential pace of urbanization means the world's urban population is expected to rise to 60% by 2030 with one in three people living in cities of at least half a million inhabitants. Secondly, accompanying economic development and globalization are exacerbating socioeconomic disparities. Thirdly, there has been a marked increase in average life expectancy and rapid shift towards an ageing population in recent years (Bloom 2011; WHO 2011).
Given that mental health disorders have an inherently complex aetiology, being characterized by long latency between exposures and subsequent incidence and progression, a more holistic life course-based approach has been proposed for enhancing the mental capital of cities and wellbeing of its populations (Beddington et al. 2008). The importance of a such a holistic approach has been further emphasized by the recent inclusion of mental health within the sustainable development goals (WHO 2017).
The state of human health in general, its production and socio-spatial distribution across populations, as well as contextual urban scales, originates as a result of complex interplay between multiple multi-level causative factors (Sarkar and Webster 2017a). In addition to genetic, physiological and lifestyle-related factors, certain attributes of the built environment in our cities have also been implicated in prevalence and progression of mental health disorders. Specific factors such as housing, built density, design, land use heterogeneity, configuration, accessibility to key service destinations and public spaces have been evidenced to affect mental health (Halpern 1995; Evans 2003).
In a seminal study in environmental cognition, Kevin Lynch articulated that individual’s perception of urban spaces and maneuverability within them originates from a set of cognitive maps coded from discrete interconnected urban sub-spaces comprising paths (roads, walkways, transit lines), path intersections or nodes, landmarks, districts and boundaries or edges (Lynch 1960). Well-designed built environment are thus associated with a better perception of place, degree of attachment to that place, enhanced sense of community, and social capital (Burton and Mitchell 2006). Neighbourhoods with greater social capital have generally been found to produce beneficial effects upon the mental health of its residents (Almedom 2005). The built environment also influences an individual’s route choice and travel mode and thus walkability and physical activity. The protective effects of walkability and physical activity upon mental health have been evidenced (Strawbridge et al. 2002; Berke et al. 2007). The protective effects of salutogenic urban environments, especially the accessibility to, quality and design of greenspaces and blue spaces upon mental health and wellbeing have also been well established (Gascon et al. 2015). These effects originate from the inherent stress-relieving and restorative potential of these spaces as well as their capacity to support physical activity and promote social interactions.
The built and social environment configure one another and this may produce islands of stressful exposures producing negative effects upon mental health. Neighbourhood-level deprivation and socio-economic disparity have been evidenced to have a negative impacts on mental wellbeing (Kim 2008). Similar are the effects of community severance (and the loss of defensible space) originating from risk of traffic and fear of crime (Stafford et al. 2007; Mindell and Karlsen 2012). Environmental stressors evidenced to be detrimental to mental health include air (Power et al. 2015) and noise (Orban et al. 2016) pollution. Climate change, although occurring at a global scale, is experienced locally, and potentially has considerable negative impacts upon mental health (Doherty and Clayton 2011).
Although the evidence of links between built environment and mental health is increasing, the use of built environment as an intervention towards reducing the burden of mental disorders is still in its infancy and substantial challenges remain to be overcome. Almost a decade and a half after Sally Macintyre eloquently articulated the necessity to unravel the black box (unspecified urban miasma) of contextual determinants of health (Macintyre et al. 2002), we are nowadays, with the increasing advances in geospatial technologies, urban analytics, modelling algorithms, data linkage and anonymisation techniques, able to develop highly characterised measures of health-influencing environmental exposures for large population cohorts (Sarkar and Webster 2017b). Emerging data platforms such as the UK Biobank Urban Morphometrics Platform (Sarkar et al. 2015) are beginning to make very large scale individual-level built environment – health modelling a possibility. From an epidemiological and public health perspective, we are yet to gain in-depth insights into the underlying causality in the aetiology of mental illness. There is a need to progress from the current series of association studies to causal inference and prediction modeling. For the current evidence base to be reliable, we need prospective studies linking changes in urban exposures over a period of time to corresponding changes in mental health, adjusting for all other related covariates.
We are also far from unraveling the exact proportion of the effects upon mental health exerted by who we are (individual genetics and physiology), where we are (place-effects) and how we choose to live (lifestyle-effects). With the recent availability of large scale prospective cohorts with linked genetic, health and lifestyle, and exposure data (such as UK Biobank) and all-encompassing data infrastructures facilitating very large cross-cohort studies (like the UK Dementias Platform), in the pipeline, are a new breed of gene-lifestyle-environment models aiming to provide answers to these questions and generate reliable evidence base. Such large scale prospective evidence of the detrimental/beneficial effects of various urban exposures is likely to be more generalisable and help guide policies.
Urban planning finds itself at the cusp of an important transformation, re-linking with public health by orienting its focus towards a more holistic and evidence-based approach, especially in emphasizing the need to identify, anticipate and measure the social and health externalities associated with development. From an urban design perspective, the challenge is to find effective ways to retrofit and optimize designs to create multi-functional places that encourage sense of community and social capital, promote active lifestyles and minimize environmental stressors and social disparities detrimental to mental health. Such designs will of course have to be based on reliable evidence generated on the links between place, lifestyle and mental health. Hence the renewed impetus has been to build, plan and design using our cities with its resident populations as urban epidemiological laboratories (testing multiple experimental designs including observational, case-controlled, randomized controlled studies, natural experiments and quasi experiments to gather evidence). Given our urgency to minimize burden of mental disorders in our cities, it will rather be of foresight to soon envision a ubiquitously collaborative work model wherein planners/designers/architects are re-wired to work with epidemiologists, psychologists, public health and social service professionals, selling packaged residential spaces with individualised design customized to particular families and populations with specific physical and mental health outcomes and needs. These specially packaged spaces with individualized design features can be tailored for micro-environments (residences, offices, schools, hostels, care homes) as well as neighbourhood environments (streets, neighbourhoods and public spaces) and will be a step-change from the universal design guidelines that exist in a few cities of the world. They will, in essence, constitute targeted preventive interventions and help reduce the long-term direct and indirect economic burdens associated with mental health disorders.
Economics does matter. Given the laws of behavioural economics and change management, health economists can help monetize the value of healthy lifestyles and good urban designs that enhance mental capital of cities in addition to pricing negative externalities. By doing so, positive and negative health externalities of urban living can be essentially imprinted within the decision making calculus and fiscal planning and public health budgeting calculations. Such a mechanism can disincentivise lifestyles and behaviours detrimental to mental health and vice versa.
Overcoming the challenge posed by mental illnesses will also entail devising actionable policies derived from gathered evidences at multiple levels. Such policies must be systems-based, multidisciplinary and have a predictive element. Given the societal changes we are currently encountering and the associated stochasticity, probabilistic Bayesian modelling and related policy analytics may be employed as exploratory decision making tools.
The World health Organization’s slogan, “no health without mental health”, has become all the more relevant in the current global context than ever before. The way we build, plan and design our cities today; its dwellings, neighbourhoods, streets and public spaces and transport infrastructures are likely to affect the mental health and wellbeing of millions of its residents, not only today, but for generations to come. Being enduring and pervasive, planning and designing healthy cities have the potential to be at the forefront of our preventive strategies aimed at enhancing its mental capital as well as managing and minimizing the burden of mental illnesses of its residents.
REFERENCES
Almedom, A.M. Social capital and mental health: An interdisciplinary review of primary evidence. Social Science & Medicine 2005;61:943-964
Beddington, J.; Cooper, C.L.; Field, J.; Goswami, U.; Huppert, F.A.; Jenkins, R., et al. The mental wealth of nations. Nature 2008;455:1057
Berke, E.M.; Gottlieb, L.M.; Moudon, A.V.; Larson, E.B. Protective association between neighborhood walkability and depression in older men. Journal of the American Geriatrics Society 2007;55:526-533
Bloom, D.E. 7 Billion and Counting. Science 2011;333:562-569
Burton, E.; Mitchell, L. Inclusive urban design: Streets for life ed^eds. New York: Routledge; 2006
Doherty, T.J.; Clayton, S. The psychological impacts of global climate change. American Psychologist 2011;66:265
Evans, G.W. The built environment and mental health. Journal of Urban Health 2003;80:536-555
Gascon, M.; Triguero-Mas, M.; Martínez, D.; Dadvand, P.; Forns, J.; Plasència, A., et al. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. International Journal of Environmental Research and Public Health 2015;12:4354-4379
Halpern, D. Mental health and the built environment: more than bricks and mortar? ed^eds. London: Taylor and Francis; 1995
Kim, D. Blues from the neighborhood? Neighborhood characteristics and depression. Epidemiologic Reviews 2008;30:101-117
Lynch, K. The image of the city ed^eds. Cambridge, MA: MIT press; 1960
Macintyre, S.; Ellaway, A.; Cummins, S. Place effects on health: how can we conceptualise, operationalise and measure them? Social Science & Medicine 2002;55:125-139
Mindell, J.S.; Karlsen, S. Community severance and health: what do we actually know? Journal of Urban Health 2012;89:232-246
Orban, E.; McDonald, K.; Sutcliffe, R.; Hoffmann, B.; Fuks, K.B.; Dragano, N., et al. Residential road traffic noise and high depressive symptoms after five years of follow-up: results from the Heinz Nixdorf recall study. Environmental Health Perspectives 2016;124:578
Power, M.C.; Kioumourtzoglou, M.-A.; Hart, J.E.; Okereke, O.I.; Laden, F.; Weisskopf, M.G. The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study. BMJ 2015;350:h1111
Prince, M.; Patel, V.; Saxena, S.; Maj, M.; Maselko, J.; Phillips, M.R., et al. No health without mental health. The Lancet 2007;370:859-877
Sarkar, C.; Webster, C. Urban environments and human health: current trends and future directions. Current Opinion in Environmental Sustainability 2017a;25:33-44
Sarkar, C.; Webster, C. Healthy cities of tomorrow: the case for large scale built environment–health studies. Journal of Urban Health 2017b;94:4-19
Sarkar, C.; Webster, C.; Gallacher, J. UK Biobank Urban Morphometric Platform (UKBUMP)–a nationwide resource for evidence-based healthy city planning and public health interventions. Annals of GIS 2015;21:135-148
Stafford, M.; Chandola, T.; Marmot, M. Association between fear of crime and mental health and physical functioning. American Journal of Public Health 2007;97:2076-2081
Strawbridge, W.J.; Deleger, S.; Roberts, R.E.; Kaplan, G.A. Physical Activity Reduces the Risk of Subsequent Depression for Older Adults. American Journal of Epidemiology 2002;156:328-334
Vigo, D.; Thornicroft, G.; Atun, R. Estimating the true global burden of mental illness. The Lancet Psychiatry 2016;3:171-178
Walker, E.; McGee, R.E.; Druss, B.G. Mortality in mental disorders and global disease burden implications: A systematic review and meta-analysis. JAMA Psychiatry 2015;72:334-341
Whiteford, H.A.; Degenhardt, L.; Rehm, J.; Baxter, A.J.; Ferrari, A.J.; Erskine, H.E., et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet 2013;382:1575-1586
WHO. Global health and ageing. . World Health Organization 2011 http://www.who.int/ageing/publications/global_health.pdf
WHO. Mental health included in the UN Sustainable Development Goals. http://www.whi.int/mental_health/SDGs/en/. 2017
World Health Organization. Mental health: a state of well-being. http://www.who.int.eproxy2.lib.hku.hk/features/factfiles/mental_health/en/.
Beddington, J.; Cooper, C.L.; Field, J.; Goswami, U.; Huppert, F.A.; Jenkins, R., et al. The mental wealth of nations. Nature 2008;455:1057
Berke, E.M.; Gottlieb, L.M.; Moudon, A.V.; Larson, E.B. Protective association between neighborhood walkability and depression in older men. Journal of the American Geriatrics Society 2007;55:526-533
Bloom, D.E. 7 Billion and Counting. Science 2011;333:562-569
Burton, E.; Mitchell, L. Inclusive urban design: Streets for life ed^eds. New York: Routledge; 2006
Doherty, T.J.; Clayton, S. The psychological impacts of global climate change. American Psychologist 2011;66:265
Evans, G.W. The built environment and mental health. Journal of Urban Health 2003;80:536-555
Gascon, M.; Triguero-Mas, M.; Martínez, D.; Dadvand, P.; Forns, J.; Plasència, A., et al. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. International Journal of Environmental Research and Public Health 2015;12:4354-4379
Halpern, D. Mental health and the built environment: more than bricks and mortar? ed^eds. London: Taylor and Francis; 1995
Kim, D. Blues from the neighborhood? Neighborhood characteristics and depression. Epidemiologic Reviews 2008;30:101-117
Lynch, K. The image of the city ed^eds. Cambridge, MA: MIT press; 1960
Macintyre, S.; Ellaway, A.; Cummins, S. Place effects on health: how can we conceptualise, operationalise and measure them? Social Science & Medicine 2002;55:125-139
Mindell, J.S.; Karlsen, S. Community severance and health: what do we actually know? Journal of Urban Health 2012;89:232-246
Orban, E.; McDonald, K.; Sutcliffe, R.; Hoffmann, B.; Fuks, K.B.; Dragano, N., et al. Residential road traffic noise and high depressive symptoms after five years of follow-up: results from the Heinz Nixdorf recall study. Environmental Health Perspectives 2016;124:578
Power, M.C.; Kioumourtzoglou, M.-A.; Hart, J.E.; Okereke, O.I.; Laden, F.; Weisskopf, M.G. The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study. BMJ 2015;350:h1111
Prince, M.; Patel, V.; Saxena, S.; Maj, M.; Maselko, J.; Phillips, M.R., et al. No health without mental health. The Lancet 2007;370:859-877
Sarkar, C.; Webster, C. Urban environments and human health: current trends and future directions. Current Opinion in Environmental Sustainability 2017a;25:33-44
Sarkar, C.; Webster, C. Healthy cities of tomorrow: the case for large scale built environment–health studies. Journal of Urban Health 2017b;94:4-19
Sarkar, C.; Webster, C.; Gallacher, J. UK Biobank Urban Morphometric Platform (UKBUMP)–a nationwide resource for evidence-based healthy city planning and public health interventions. Annals of GIS 2015;21:135-148
Stafford, M.; Chandola, T.; Marmot, M. Association between fear of crime and mental health and physical functioning. American Journal of Public Health 2007;97:2076-2081
Strawbridge, W.J.; Deleger, S.; Roberts, R.E.; Kaplan, G.A. Physical Activity Reduces the Risk of Subsequent Depression for Older Adults. American Journal of Epidemiology 2002;156:328-334
Vigo, D.; Thornicroft, G.; Atun, R. Estimating the true global burden of mental illness. The Lancet Psychiatry 2016;3:171-178
Walker, E.; McGee, R.E.; Druss, B.G. Mortality in mental disorders and global disease burden implications: A systematic review and meta-analysis. JAMA Psychiatry 2015;72:334-341
Whiteford, H.A.; Degenhardt, L.; Rehm, J.; Baxter, A.J.; Ferrari, A.J.; Erskine, H.E., et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet 2013;382:1575-1586
WHO. Global health and ageing. . World Health Organization 2011 http://www.who.int/ageing/publications/global_health.pdf
WHO. Mental health included in the UN Sustainable Development Goals. http://www.whi.int/mental_health/SDGs/en/. 2017
World Health Organization. Mental health: a state of well-being. http://www.who.int.eproxy2.lib.hku.hk/features/factfiles/mental_health/en/.
ABOUT THE AUTHOR
|
Dr Chinmoy Sarkar is an Assistant Professor of GIS, Urban Health and Environment at The University of Hong Kong. He received a PhD from Cardiff University, UK and subsequently conducted post-doctoral studies at Cardiff University and The University of Hong Kong. His research interest lies in the interdisciplinary domains of built environment epidemiology, big data modeling in health and urban mobility, spatial design analyses for healthy cities, urban green and active travel, planning for smart cities, use of smart technologies including location-based services for health research, statistical and geostatistical modeling.
He is also the concept lead and developer of the UK Biobank Urban Morphometric Platform (UKBUMP), the largest ever individual-level built environment – health database being constructed. This has emerged from his PhD and post-doctoral research and involves big data spatial modeling and analyses towards construction of more than 750 individual-level built environment morphological metrics (morphometrics) for half-a-million participants of the UK Biobank study, UK’s flagship epidemiological cohort. He has also examined the associations between detailed neighbourhood-level built environment and individual health outcomes of respondents from the Caerphilly Prospective study. This formed the basis of the book Healthy Cities: Public Health Through Urban Planning (authored with Prof. Chris Webster and Prof John Gallacher), conceptualizing Healthy Cities in terms of empirically defined spatial health niches - a framework that has the potential to incorporate and integrate multiple multi-level spatio-temporal health determinants existing at the different hierarchies in a city system. The UKBUMP study presently forms a platform for more than half-a-dozen research collaborations with ivy-league universities. Dr Sarkar, besides developing the UKBUMP database, is also leading studies aiming to develop more robust models of associations between objectively measured built environment and individual activity behaviours, obesity, cardio-vascular risks, mental health and wellbeing. |