1
|
Doiron D, Setton EM, Syer J, Redivo A, McKee A, Noaeen M, Patel P, Booth GL, Brauer M, Fuller D, Kestens Y, Rosella LC, Stieb D, Villeneuve PJ, Brook JR. HealthyPlan.City: A Web Tool to Support Urban Environmental Equity and Public Health in Canadian Communities. J Urban Health 2024; 101:497-507. [PMID: 38587782 PMCID: PMC11190122 DOI: 10.1007/s11524-024-00855-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/08/2024] [Indexed: 04/09/2024]
Abstract
Urban environmental factors such as air quality, heat islands, and access to greenspaces and community amenities impact public health. Some vulnerable populations such as low-income groups, children, older adults, new immigrants, and visible minorities live in areas with fewer beneficial conditions, and therefore, face greater health risks. Planning and advocating for equitable healthy urban environments requires systematic analysis of reliable spatial data to identify where vulnerable populations intersect with positive or negative urban/environmental characteristics. To facilitate this effort in Canada, we developed HealthyPlan.City ( https://healthyplan.city/ ), a freely available web mapping platform for users to visualize the spatial patterns of built environment indicators, vulnerable populations, and environmental inequity within over 125 Canadian cities. This tool helps users identify areas within Canadian cities where relatively higher proportions of vulnerable populations experience lower than average levels of beneficial environmental conditions, which we refer to as Equity priority areas. Using nationally standardized environmental data from satellite imagery and other large geospatial databases and demographic data from the Canadian Census, HealthyPlan.City provides a block-by-block snapshot of environmental inequities in Canadian cities. The tool aims to support urban planners, public health professionals, policy makers, and community organizers to identify neighborhoods where targeted investments and improvements to the local environment would simultaneously help communities address environmental inequities, promote public health, and adapt to climate change. In this paper, we report on the key considerations that informed our approach to developing this tool and describe the current web-based application.
Collapse
Affiliation(s)
- Dany Doiron
- Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.
| | - Eleanor M Setton
- Geography Department, University of Victoria, Victoria, BC, Canada
| | - Joey Syer
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Andre Redivo
- Geography Department, University of Victoria, Victoria, BC, Canada
| | - Allan McKee
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Mohammad Noaeen
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Priya Patel
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Gillian L Booth
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
| | - Daniel Fuller
- Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Yan Kestens
- École de Santé Publique de L'Université de Montréal, Montréal, QC, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Dave Stieb
- Environmental Health Science and Research Bureau, Health Canada, Vancouver, BC, Canada
| | | | - Jeffrey R Brook
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
2
|
Gasparrini A. A tutorial on the case time series design for small-area analysis. BMC Med Res Methodol 2022; 22:129. [PMID: 35501713 PMCID: PMC9063281 DOI: 10.1186/s12874-022-01612-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels. Methods This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level. Results The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions. Conclusions The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.
Collapse
Affiliation(s)
- Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, London, WC1H 9SH, UK. .,Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, UK.
| |
Collapse
|
3
|
Piel FB, Cockings S. Using large and complex datasets for small-area environment-health studies: from theory to practice. Int J Epidemiol 2020; 49 Suppl 1:i1-i3. [PMID: 32293010 PMCID: PMC7158051 DOI: 10.1093/ije/dyaa018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Revised: 01/14/2020] [Accepted: 02/11/2020] [Indexed: 12/04/2022] Open
Affiliation(s)
- Frédéric B Piel
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Imperial College London, UK
| | - Samantha Cockings
- Department of Geography and Environmental Science, University of Southampton, Southampton, UK
| |
Collapse
|
4
|
Fecht D, Cockings S, Hodgson S, Piel FB, Martin D, Waller LA. Advances in mapping population and demographic characteristics at small-area levels. Int J Epidemiol 2020; 49 Suppl 1:i15-i25. [PMID: 32293009 PMCID: PMC7158058 DOI: 10.1093/ije/dyz179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 08/09/2019] [Indexed: 11/30/2022] Open
Abstract
Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
Collapse
Affiliation(s)
- Daniela Fecht
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - Samantha Cockings
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Susan Hodgson
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - Frédéric B Piel
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - David Martin
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Lance A Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| |
Collapse
|
5
|
Piel FB, Fecht D, Hodgson S, Blangiardo M, Toledano M, Hansell AL, Elliott P. Small-area methods for investigation of environment and health. Int J Epidemiol 2020; 49:686-699. [PMID: 32182344 PMCID: PMC7266556 DOI: 10.1093/ije/dyaa006] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 01/10/2020] [Indexed: 11/30/2022] Open
Abstract
Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.
Collapse
Affiliation(s)
- Frédéric B Piel
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Imperial College London, UK
| | - Daniela Fecht
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Susan Hodgson
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Marta Blangiardo
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M Toledano
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - A L Hansell
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Environmental Health and Sustainability, Medical School, University of Leicester, Leicester, UK
| | - Paul Elliott
- UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Imperial College London, UK
| |
Collapse
|