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      Short term association between ozone and mortality: global two stage time series study in 406 locations in 20 countries

      research-article
      1 , 2 , 3 , , 1 , 4 , 1 , 1 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 11 , 13 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 18 , 20 , 20 , 21 , 22 ,   23 , 24 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 36 , 37 , 38 , 37 , 38 ,   39 , 39 , 40 , 40 , 41 , 42 , 1 , 43 , 44
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          Abstract

          Objective

          To assess short term mortality risks and excess mortality associated with exposure to ozone in several cities worldwide.

          Design

          Two stage time series analysis.

          Setting

          406 cities in 20 countries, with overlapping periods between 1985 and 2015, collected from the database of Multi-City Multi-Country Collaborative Research Network.

          Population

          Deaths for all causes or for external causes only registered in each city within the study period .

          Main outcome measures

          Daily total mortality (all or non-external causes only).

          Results

          A total of 45 165 171 deaths were analysed in the 406 cities. On average, a 10 µg/m 3 increase in ozone during the current and previous day was associated with an overall relative risk of mortality of 1.0018 (95% confidence interval 1.0012 to 1.0024). Some heterogeneity was found across countries, with estimates ranging from greater than 1.0020 in the United Kingdom, South Africa, Estonia, and Canada to less than 1.0008 in Mexico and Spain. Short term excess mortality in association with exposure to ozone higher than maximum background levels (70 µg/m 3) was 0.26% (95% confidence interval 0.24% to 0.28%), corresponding to 8203 annual excess deaths (95% confidence interval 3525 to 12 840) across the 406 cities studied. The excess remained at 0.20% (0.18% to 0.22%) when restricting to days above the WHO guideline (100 µg/m 3), corresponding to 6262 annual excess deaths (1413 to 11 065). Above more lenient thresholds for air quality standards in Europe, America, and China, excess mortality was 0.14%, 0.09%, and 0.05%, respectively.

          Conclusions

          Results suggest that ozone related mortality could be potentially reduced under stricter air quality standards. These findings have relevance for the implementation of efficient clean air interventions and mitigation strategies designed within national and international climate policies.

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          Most cited references23

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          Ozone and short-term mortality in 95 US urban communities, 1987-2000.

          Ozone has been associated with various adverse health effects, including increased rates of hospital admissions and exacerbation of respiratory illnesses. Although numerous time-series studies have estimated associations between day-to-day variation in ozone levels and mortality counts, results have been inconclusive. To investigate whether short-term (daily and weekly) exposure to ambient ozone is associated with mortality in the United States. Using analytical methods and databases developed for the National Morbidity, Mortality, and Air Pollution Study, we estimated a national average relative rate of mortality associated with short-term exposure to ambient ozone for 95 large US urban communities from 1987-2000. We used distributed-lag models for estimating community-specific relative rates of mortality adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term trends) and hierarchical models for combining relative rates across communities to estimate a national average relative rate, taking into account spatial heterogeneity. Daily counts of total non-injury-related mortality and cardiovascular and respiratory mortality in 95 large US communities during a 14-year period. A 10-ppb increase in the previous week's ozone was associated with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality (95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were larger than for models considering only a single day's exposure. Results were robust to adjustment for particulate matter, weather, seasonality, and long-term trends. These results indicate a statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population. The findings indicate that this widespread pollutant adversely affects public health.
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            Attributable risk from distributed lag models

            Background Measures of attributable risk are an integral part of epidemiological analyses, particularly when aimed at the planning and evaluation of public health interventions. However, the current definition of such measures does not consider any temporal relationships between exposure and risk. In this contribution, we propose extended definitions of attributable risk within the framework of distributed lag non-linear models, an approach recently proposed for modelling delayed associations in either linear or non-linear exposure-response associations. Methods We classify versions of attributable number and fraction expressed using either a forward or backward perspective. The former specifies the future burden due to a given exposure event, while the latter summarizes the current burden due to the set of exposure events experienced in the past. In addition, we illustrate how the components related to sub-ranges of the exposure can be separated. Results We apply these methods for estimating the mortality risk attributable to outdoor temperature in two cities, London and Rome, using time series data for the periods 1993–2006 and 1992–2010, respectively. The analysis provides estimates of the overall mortality burden attributable to temperature, and then computes the components attributable to cold and heat and then mild and extreme temperatures. Conclusions These extended definitions of attributable risk account for the additional temporal dimension which characterizes exposure-response associations, providing more appropriate attributable measures in the presence of dependencies characterized by potentially complex temporal patterns.
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              The global burden of disease due to outdoor air pollution.

              As part of the World Health Organization (WHO) Global Burden of Disease Comparative Risk Assessment, the burden of disease attributable to urban ambient air pollution was estimated in terms of deaths and disability-adjusted life years (DALYs). Air pollution is associated with a broad spectrum of acute and chronic health effects, the nature of which may vary with the pollutant constituents. Particulate air pollution is consistently and independently related to the most serious effects, including lung cancer and other cardiopulmonary mortality. The analyses on which this report is based estimate that ambient air pollution, in terms of fine particulate air pollution (PM(2.5)), causes about 3% of mortality from cardiopulmonary disease, about 5% of mortality from cancer of the trachea, bronchus, and lung, and about 1% of mortality from acute respiratory infections in children under 5 yr, worldwide. This amounts to about 0.8 million (1.2%) premature deaths and 6.4 million (0.5%) years of life lost (YLL). This burden occurs predominantly in developing countries; 65% in Asia alone. These estimates consider only the impact of air pollution on mortality (i.e., years of life lost) and not morbidity (i.e., years lived with disability), due to limitations in the epidemiologic database. If air pollution multiplies both incidence and mortality to the same extent (i.e., the same relative risk), then the DALYs for cardiopulmonary disease increase by 20% worldwide.
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                Author and article information

                Contributors
                Role: assistant professor
                Role: research fellow
                Role: PhD student
                Role: professor
                Role: assistant professor
                Role: professor
                Role: professor
                Role: adjunct professor
                Role: associate professor
                Role: postdoctoral researcher
                Role: associate professor
                Role: lecturer
                Role: project manager
                Role: research fellow
                Role: senior scientist
                Role: professor
                Role: associate professor
                Role: senior researcher
                Role: researcher
                Role: professor
                Role: professor
                Role: lecturer
                Role: researcher
                Role: researcher
                Role: researcher
                Role: postdoctoral research fellow
                Role: assistant professor
                Role: principal researcher
                Role: professor
                Role: senior researcher
                Role: senior researcher
                Role: professor
                Role: senior research assistant
                Role: senior scientific collaborator
                Role: professor
                Role: professor
                Role: professor
                Role: principal research scientist
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                10 February 2020
                : 368
                : m108
                Affiliations
                [1 ]Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
                [2 ]Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
                [3 ]Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
                [4 ]School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China
                [5 ]Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
                [6 ]Shanghai Children’s Medical Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
                [7 ]School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China
                [8 ]School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
                [9 ]Air Health Science Division, Health Canada, Ottawa, Canada
                [10 ]School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
                [11 ]Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic
                [12 ]Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
                [13 ]Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
                [14 ]Santé Publique France, French National Public Health Agency, Saint Maurice, France
                [15 ]Department of Physical, Chemical and Natural Systems, Universidad Pablo de Olavide, Sevilla, Spain
                [16 ]Potsdam Institute for Climate Impact Research, Potsdam, Germany
                [17 ]Institute of Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
                [18 ]Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Greece
                [19 ]School of Population Health and Environmental Sciences, King’s College London, London, UK
                [20 ]Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome, Italy
                [21 ]Department of Global Health Policy, School of International Health, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
                [22 ]Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
                [23 ]School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
                [24 ]Department of Environmental Health, National Institute of Public Health, Cuernavaca Morelos, Mexico
                [25 ]Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Lisbon, Portugal
                [26 ]EPIUnit–Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
                [27 ]Department of Environmental Health, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal
                [28 ]Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA
                [29 ]Natural Resources and the Environment Unit, Council for Scientific and Industrial Research, Pretoria 0001, South Africa
                [30 ]Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa
                [31 ]Department of Geography, Geo-informatics and Meteorology, University of Pretoria, Pretoria, South Africa
                [32 ]Graduate School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
                [33 ]Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain
                [34 ]Department of Statistics and Computational Research, University of Valencia, Valencia, Spain
                [35 ]Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
                [36 ]Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
                [37 ]Swiss Tropical and Public Health Institute, Basel, Switzerland
                [38 ]University of Basel, Basel, Switzerland
                [39 ]Environmental and Occupational Medicine, National Taiwan University and NTU Hospital, Taiwan
                [40 ]Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [41 ]School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
                [42 ]Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, China
                [43 ]Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, UK
                [44 ]Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
                Author notes
                Correspondence to: A M Vicedo-Cabrera ana.vicedo-cabrera@ 123456lshtm.ac.uk
                Author information
                https://orcid.org/0000-0001-6982-8867
                Article
                vica050789
                10.1136/bmj.m108
                7190035
                32041707
                fb1126c6-cc91-4ead-8051-4c1be4d375fc
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 December 2019
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