A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia
Abstract Introduction Road traffic noise increases the risk of mortality from ischemic heart disease (IHD). Because noise is highly localized, high resolution maps of exposures and health outcomes are key to urban planning interventions that are informed by health risks. In Australia, publicly acces...
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doaj-70444b3148854e7a96c22c13e2ed45c42020-11-25T02:30:10ZengBMCInternational Journal of Health Geographics1476-072X2019-09-0118111010.1186/s12942-019-0184-xA statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, AustraliaIvan C. Hanigan0Timothy B. Chaston1Ben Hinze2Martine Dennekamp3Bin Jalaludin4Yohannes Kinfu5Geoffrey G. Morgan6School of Public Health and University Centre for Rural Health, The University of SydneySchool of Public Health and University Centre for Rural Health, The University of SydneyAmbient MapsEnvironment Protection Authority VictoriaSchool of Public Health and Community Medicine, University of New South WalesCentre for Research and Action in Public Health and Faculty of Health, University of CanberraSchool of Public Health and University Centre for Rural Health, The University of SydneyAbstract Introduction Road traffic noise increases the risk of mortality from ischemic heart disease (IHD). Because noise is highly localized, high resolution maps of exposures and health outcomes are key to urban planning interventions that are informed by health risks. In Australia, publicly accessible IHD deaths data are only available at the coarse spatial aggregation level of local government area (LGA), in which about 130,000 people reside. Herein, we addressed this limitation of health data using statistical downscaling and generated environmental health risk maps for noise at the meshblock level (MB; ~ 90 people). Methods We estimated noise exposures at the MB level using a model of road traffic noise in Melbourne, Australia, from 2011. As recommended by the World Health Organization, a non-linear exposure–response function for traffic noise and IHD was used to calculate odds ratios for noise related IHD in all MBs. Noise attributable risks of IHD death were then estimated by statistically downscaling LGA-level IHD rates to the MB level. Results Noise levels of 80 dB were recorded in some MBs. From the given noise maps, approximately 5% of the population was exposed to traffic noise above the risk threshold of 55 dB. Maps of excess risk at the MB level identified areas in which noise levels and exposed populations are large. Attributable rates of IHD deaths due to noise were generally very low, but some were as high as 5–10 per 100,000, and in extremely noisy and populated MBs represented more than 8% excess risk of IHD death. We presented results as interactive maps of excess risk due to noise at the small neighbourhood scale. Conclusion Our method accommodates low-resolution health data and could be used to inform urban planning and public health decision making for various environmental health concerns. Estimated noise related IHD deaths were relatively few in Melbourne in 2011, likely because road traffic is one of many noise sources and the current noise model underestimates exposures. Nonetheless, this novel computational framework could be used globally to generate maps of noise related health risks using scant health outcomes data.http://link.springer.com/article/10.1186/s12942-019-0184-xEnvironmental health risk assessmentStatistical downscalingExposure–response functionNoiseMortality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ivan C. Hanigan Timothy B. Chaston Ben Hinze Martine Dennekamp Bin Jalaludin Yohannes Kinfu Geoffrey G. Morgan |
spellingShingle |
Ivan C. Hanigan Timothy B. Chaston Ben Hinze Martine Dennekamp Bin Jalaludin Yohannes Kinfu Geoffrey G. Morgan A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia International Journal of Health Geographics Environmental health risk assessment Statistical downscaling Exposure–response function Noise Mortality |
author_facet |
Ivan C. Hanigan Timothy B. Chaston Ben Hinze Martine Dennekamp Bin Jalaludin Yohannes Kinfu Geoffrey G. Morgan |
author_sort |
Ivan C. Hanigan |
title |
A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia |
title_short |
A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia |
title_full |
A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia |
title_fullStr |
A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia |
title_full_unstemmed |
A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia |
title_sort |
statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in melbourne, australia |
publisher |
BMC |
series |
International Journal of Health Geographics |
issn |
1476-072X |
publishDate |
2019-09-01 |
description |
Abstract Introduction Road traffic noise increases the risk of mortality from ischemic heart disease (IHD). Because noise is highly localized, high resolution maps of exposures and health outcomes are key to urban planning interventions that are informed by health risks. In Australia, publicly accessible IHD deaths data are only available at the coarse spatial aggregation level of local government area (LGA), in which about 130,000 people reside. Herein, we addressed this limitation of health data using statistical downscaling and generated environmental health risk maps for noise at the meshblock level (MB; ~ 90 people). Methods We estimated noise exposures at the MB level using a model of road traffic noise in Melbourne, Australia, from 2011. As recommended by the World Health Organization, a non-linear exposure–response function for traffic noise and IHD was used to calculate odds ratios for noise related IHD in all MBs. Noise attributable risks of IHD death were then estimated by statistically downscaling LGA-level IHD rates to the MB level. Results Noise levels of 80 dB were recorded in some MBs. From the given noise maps, approximately 5% of the population was exposed to traffic noise above the risk threshold of 55 dB. Maps of excess risk at the MB level identified areas in which noise levels and exposed populations are large. Attributable rates of IHD deaths due to noise were generally very low, but some were as high as 5–10 per 100,000, and in extremely noisy and populated MBs represented more than 8% excess risk of IHD death. We presented results as interactive maps of excess risk due to noise at the small neighbourhood scale. Conclusion Our method accommodates low-resolution health data and could be used to inform urban planning and public health decision making for various environmental health concerns. Estimated noise related IHD deaths were relatively few in Melbourne in 2011, likely because road traffic is one of many noise sources and the current noise model underestimates exposures. Nonetheless, this novel computational framework could be used globally to generate maps of noise related health risks using scant health outcomes data. |
topic |
Environmental health risk assessment Statistical downscaling Exposure–response function Noise Mortality |
url |
http://link.springer.com/article/10.1186/s12942-019-0184-x |
work_keys_str_mv |
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