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href="https://hsph-harvard.academia.edu/">Harvard School of Public Health</a>, <a class="u-tcGrayDarker" href="https://hsph-harvard.academia.edu/Departments/Biostatistics/Documents">Biostatistics</a>, <span class="u-tcGrayDarker">Alumnus</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Massimo" data-follow-user-id="31976424" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="31976424"><span class="material-symbols-outlined" 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id="Pill-react-component-0b3e2678-d987-4762-a5cb-a66c3df09262"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Massimo Stafoggia</h3></div><div class="js-work-strip profile--work_container" data-work-id="24437482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects"><img alt="Research paper thumbnail of Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://attachments.academia-assets.com/44766805/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ucl.academia.edu/MichalisKatsoulis">Michalis Katsoulis</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Associations between long-term exposure to ambient particulate matter (PM) and cardio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular<br />(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents<br />of PM on total CVD mortality have been explored in a single study only.<br />Aims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.<br />Methods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European<br />Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health<br />impacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average<br />exposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was<br />estimated using Land Use Regression models. Eight elements representing major sources were selected a<br />priori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were<br />conducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis<br />was used to calculate combined effect estimates.<br />Results: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically<br />significant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the<br />pooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was<br />0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5<br />(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).<br />Conclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between<br />long-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eaa0fdbcdf42020a5594a08fedf83052" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44766805,"asset_id":24437482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44766805/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="24437482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="24437482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24437482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24437482]").text(description); $(".js-view-count[data-work-id=24437482]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24437482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='24437482']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 24437482, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eaa0fdbcdf42020a5594a08fedf83052" } } $('.js-work-strip[data-work-id=24437482]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":24437482,"title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects","translated_title":"","metadata":{"abstract":"Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular\n(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents\nof PM on total CVD mortality have been explored in a single study only.\nAims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.\nMethods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European\nStudy of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health\nimpacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average\nexposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was\nestimated using Land Use Regression models. Eight elements representing major sources were selected a\npriori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were\nconducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis\nwas used to calculate combined effect estimates.\nResults: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically\nsignificant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the\npooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was\n0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5\n(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).\nConclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between\nlong-term exposure to 8 elemental constituents of particles and total cardiovascular mortality."},"translated_abstract":"Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular\n(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents\nof PM on total CVD mortality have been explored in a single study only.\nAims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.\nMethods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European\nStudy of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health\nimpacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average\nexposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was\nestimated using Land Use Regression models. Eight elements representing major sources were selected a\npriori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were\nconducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis\nwas used to calculate combined effect estimates.\nResults: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically\nsignificant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the\npooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was\n0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5\n(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).\nConclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between\nlong-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.","internal_url":"https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects","translated_internal_url":"","created_at":"2016-04-15T09:16:52.389-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":43522758,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":19030291,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":4328847,"email":"m***s@ucl.ac.uk","display_order":-3,"name":"Michail 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="24437188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project"><img alt="Research paper thumbnail of Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project" class="work-thumbnail" src="https://attachments.academia-assets.com/44766522/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project">Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ucl.academia.edu/MichalisKatsoulis">Michalis Katsoulis</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JoachimHeinrich">Joachim Heinrich</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background Few studies on long-term exposure to air pollution and mortality have been reported fr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.<br />Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the<br />association between natural-cause mortality and long-term exposure to several air pollutants.<br />Methods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.<br />All cohorts were general population samples, although some were restricted to one sex only. With a strictly<br />standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of<br />particulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and<br />2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land<br />use regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road<br />(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical<br />analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional<br />hazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.<br />Findings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk<br />(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased<br />hazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted<br />between individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when<br />we included only participants exposed to pollutant concentrations lower than the European annual mean limit value<br />of 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).<br />Interpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even<br />within concentration ranges well below the present European annual mean limit value.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f9f347671dff528bad7ad71bd7b7ea8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44766522,"asset_id":24437188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44766522/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="24437188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="24437188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24437188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24437188]").text(description); $(".js-view-count[data-work-id=24437188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24437188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='24437188']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 24437188, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3f9f347671dff528bad7ad71bd7b7ea8" } } $('.js-work-strip[data-work-id=24437188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":24437188,"title":"Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project","translated_title":"","metadata":{"abstract":"Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.\nWithin the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the\nassociation between natural-cause mortality and long-term exposure to several air pollutants.\nMethods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.\nAll cohorts were general population samples, although some were restricted to one sex only. With a strictly\nstandardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of\nparticulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and\n2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land\nuse regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road\n(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical\nanalyses using confounder models with increasing adjustment for confounder variables, and Cox proportional\nhazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.\nFindings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk\n(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased\nhazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted\nbetween individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when\nwe included only participants exposed to pollutant concentrations lower than the European annual mean limit value\nof 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).\nInterpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even\nwithin concentration ranges well below the present European annual mean limit value."},"translated_abstract":"Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.\nWithin the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the\nassociation between natural-cause mortality and long-term exposure to several air pollutants.\nMethods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.\nAll cohorts were general population samples, although some were restricted to one sex only. With a strictly\nstandardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of\nparticulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and\n2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land\nuse regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road\n(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical\nanalyses using confounder models with increasing adjustment for confounder variables, and Cox proportional\nhazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.\nFindings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk\n(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased\nhazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted\nbetween individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when\nwe included only participants exposed to pollutant concentrations lower than the European annual mean limit value\nof 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).\nInterpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even\nwithin concentration ranges well below the present European annual mean limit value.","internal_url":"https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project","translated_internal_url":"","created_at":"2016-04-15T09:05:02.339-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":43522758,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":19029887,"work_id":24437188,"tagging_user_id":43522758,"tagged_user_id":278760017,"co_author_invite_id":245442,"email":"k***y@med.uoa.gr","display_order":-1,"name":"Klea Katsouyanni","title":"Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project"},{"id":19029888,"work_id":24437188,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":4328847,"email":"m***s@ucl.ac.uk","display_order":4194303,"name":"Michail Katsoulis","title":"Effects 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dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857214"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857214/A_Case_Crossover_Analysis_of_Out_of_Hospital_Coronary_Deaths_and_Air_Pollution_in_Rome_Italy"><img alt="Research paper thumbnail of A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy" class="work-thumbnail" src="https://attachments.academia-assets.com/45887135/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857214/A_Case_Crossover_Analysis_of_Out_of_Hospital_Coronary_Deaths_and_Air_Pollution_in_Rome_Italy">A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DanielaDippoliti">Daniela Dippoliti</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the association with air pollution is not well understood. Objectives: We evaluated the association between daily ambient air pollution levels (particle number concentration (PNC)—a proxy for ultrafine particles (diameter 0.1 m), mass of particles with dia- meter less than 10 m (PM10); CO, NO2 ,a nd O3)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8515c8f5f576e4bf2ba20ad85415a7ed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887135,"asset_id":12857214,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887135/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857214"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857214"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857214; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857214]").text(description); $(".js-view-count[data-work-id=12857214]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857214; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857214']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857214, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8515c8f5f576e4bf2ba20ad85415a7ed" } } $('.js-work-strip[data-work-id=12857214]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857214,"title":"A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy","translated_title":"","metadata":{"abstract":"Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the association with air pollution is not well understood. 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857213/_Air_pollution_and_fatal_and_non_fatal_coronary_events_in_Rome_">[Air pollution and fatal and non fatal coronary events in Rome]</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DanielaDippoliti">Daniela Dippoliti</a></span></div><div class="wp-workCard_item"><span>Epidemiologia e prevenzione</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To examine the relationship between air pollution and coronary events in Rome in the period 1998-...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To examine the relationship between air pollution and coronary events in Rome in the period 1998-2000, considering both out-of-hospital deaths and hospitalisations. Time-series of daily counts of out-of-hospital deaths and hospitalised events, implementation of Generalised Additive Models. The air pollutants taken into account were PNC (Particle Number Concentration--a measure of ultrafine particles), PM10, CO, NO2, SO2 and O3. The association was studied with respect to either single days or the cumulative effect on more consecutive days; furthermore, effect modification by age was tested (for the age groups 0-64, 65-74 and 75+). People resident of Rome and died/hospitalised for coronary causes into the city in the period 1998-2000. Association between pollutants and out-of-hospital deaths/hospitalised events. Distinction between fatal events (out-of-hospital deaths + hospitalisations with death within 28 days of admission) and non fatal events (hospitalisations with survival longe...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857213"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857213"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857213; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857213]").text(description); $(".js-view-count[data-work-id=12857213]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857213; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857213']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857213, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857213]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857213,"title":"[Air pollution and fatal and non fatal coronary events in Rome]","translated_title":"","metadata":{"abstract":"To examine the relationship between air pollution and coronary events in Rome in the period 1998-2000, considering both out-of-hospital deaths and hospitalisations. 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A multi-city study was designed to assess the probability of recurrent hospitalization in a cohort of incident myocardial infarction survivors in five European cities. The objective of this paper is to discuss the methods for analyzing short-term health effects in a cohort study based on a case-series. Three methods were considered for the analyses of the cohort data: Poisson regression approach, case-crossover analyses and extended Cox regression analyses. The major challenge of these analyses is to appropriately consider changes within the cohort over time due to changes in the underlying risk following a myocardial infarction, slow time trends in risk factors within the population, dynamic cohort size and seasonal variation. Poisson regression analyses, case-crossover analyses and Extended Cox regression analyses gave similar results. Application of smoothing methods...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="20ad175be3c630984bc50d75583629bb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887108,"asset_id":12857212,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887108/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857212"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857212"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857212; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857212]").text(description); $(".js-view-count[data-work-id=12857212]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857212; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857212']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857212, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "20ad175be3c630984bc50d75583629bb" } } $('.js-work-strip[data-work-id=12857212]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857212,"title":"Comparison of different methods in analyzing short-term air pollution effects in a cohort study of susceptible individuals","translated_title":"","metadata":{"abstract":"Short-term fluctuations of ambient air pollution have been associated with exacerbation of cardiovascular disease. 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alt="Research paper thumbnail of The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857211/The_risks_of_acute_exposure_to_black_carbon_in_Southern_Europe_results_from_the_MED_PARTICLES_project">The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnunziataFaustini">Annunziata Faustini</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EnnioCadum">Ennio Cadum</a></span></div><div class="wp-workCard_item"><span>Occupational and environmental medicine</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">While several studies have reported associations of daily exposures to PM2.5 (particles less than...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">While several studies have reported associations of daily exposures to PM2.5 (particles less than 2.5 µm) with mortality, few studies have examined the impact of its constituents such as black carbon (BC), which is also a significant contributor to global climate change. We assessed the association between daily concentrations of BC and total, cardiovascular and respiratory mortality in two southern Mediterranean cities. Daily averages of BC were collected for 2 years in Barcelona, Spain and Athens, Greece. We used case-crossover analysis and examined single and cumulative lags up to 3 days. We observed associations between BC and all mortality measures. For a 3-day moving average, cardiovascular mortality increased by 4.5% (95% CI 0.7 to 8.5) and 2.0% (95% CI 0 to 4.0) for an interquartile change in BC in Athens and Barcelona, respectively. Considerably higher effects for respiratory mortality and for those above age 65 were observed. In addition, BC exhibited much greater toxicity...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857211"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857211"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857211; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857211]").text(description); $(".js-view-count[data-work-id=12857211]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857211; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857211']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857211, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857211]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857211,"title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"While several studies have reported associations of daily exposures to PM2.5 (particles less than 2.5 µm) with mortality, few studies have examined the impact of its constituents such as black carbon (BC), which is also a significant contributor to global climate change. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12838112"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12838112/Short_term_effects_of_particulate_matter_constituents_on_daily_hospitalizations_and_mortality_in_five_South_European_cities_Results_from_the_MED_PARTICLES_project"><img alt="Research paper thumbnail of Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12838112/Short_term_effects_of_particulate_matter_constituents_on_daily_hospitalizations_and_mortality_in_five_South_European_cities_Results_from_the_MED_PARTICLES_project">Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnunziataFaustini">Annunziata Faustini</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EAlessandrini">Ester Alessandrini</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Environment international</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Few recent studies examined acute effects on health of individual chemical species in the particu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. Studies in Southern Europe are scarce. The aim of this study is to examine the relationship between particulate matter constituents and daily hospital admissions and mortality in five cities in Southern Europe. The study included five cities in Southern Europe, three cities in Spain: Barcelona (2003-2010), Madrid (2007-2008) and Huelva (2003-2010); and two cities in Italy: Rome (2005-2007) and Bologna (2011-2013). A case-crossover design was used to link cardiovascular and respiratory hospital admissions and total, cardiovascular and respiratory mortality with a pre-defined list of 16 PM10 and PM2.5 constituents. Lags 0 to 2 were examined. City-specific results were combined by random-effects meta-analysis. Most of the elements studied, namely EC, SO4(2-), SiO2, Ca, Fe, Zn, Cu, Ti, Mn, V and Ni, showed incre...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12838112"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12838112"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12838112; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12838112]").text(description); $(".js-view-count[data-work-id=12838112]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12838112; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12838112']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12838112, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12838112]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12838112,"title":"Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. 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Most of the elements studied, namely EC, SO4(2-), SiO2, Ca, Fe, Zn, Cu, Ti, Mn, V and Ni, showed incre...","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Environment international"},"translated_abstract":"Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. Studies in Southern Europe are scarce. The aim of this study is to examine the relationship between particulate matter constituents and daily hospital admissions and mortality in five cities in Southern Europe. The study included five cities in Southern Europe, three cities in Spain: Barcelona (2003-2010), Madrid (2007-2008) and Huelva (2003-2010); and two cities in Italy: Rome (2005-2007) and Bologna (2011-2013). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857210"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project"><img alt="Research paper thumbnail of Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project">Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a></span></div><div class="wp-workCard_item"><span>Science of The Total Environment</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Previous studies reported significant variability of air pollutants across Europe with the lowest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857210"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857210"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857210; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857210]").text(description); $(".js-view-count[data-work-id=12857210]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857210; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857210']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857210, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857210]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857210,"title":"Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Science of The Total Environment"},"translated_abstract":"Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.","internal_url":"https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project","translated_internal_url":"","created_at":"2015-06-08T00:33:55.603-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796446,"work_id":12857210,"tagging_user_id":31976424,"tagged_user_id":65210792,"co_author_invite_id":283638,"email":"e***i@med.uoa.gr","display_order":0,"name":"Evangelia Samoli","title":"Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China"><img alt="Research paper thumbnail of Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China" class="work-thumbnail" src="https://attachments.academia-assets.com/45887150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China">Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NancyPedersen">Nancy Pedersen</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>PLoS ONE</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e01eea93bf28cc493f57993fb477e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887150,"asset_id":12857209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887150/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857209; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857209]").text(description); $(".js-view-count[data-work-id=12857209]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857209; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857209']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857209, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28e01eea93bf28cc493f57993fb477e6" } } $('.js-work-strip[data-work-id=12857209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857209,"title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China","translated_title":"","metadata":{"grobid_abstract":"Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardiovascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and MetS may be hypothesized. We explored this association, (taking into account, pathwayspecific MetS definitions), and its potential modifiers in Swiss adults. We studied 3769 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, reporting at least four-hour fasting time before venepuncture. AP exposures were 10-year mean residential PM 10 (particulate matter \u003c10μm in diameter) and NO 2 (nitrogen dioxide). Outcomes included MetS defined by World Health Organization (MetS-W), International Diabetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four-and eighthour fasting time limits. We also explored associations with individual components of MetS. We applied mixed logistic regression models to explore these associations. The prevalence of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W, MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per 10μg/m 3 increase in 10-year mean PM 10 . We observed weaker associations with NO 2 . Associations were stronger among physically-active, ever-smokers and non-diabetic participants especially with PM 10 (p\u003c0.05). Associations remained robust across various sensitivity analyses including ten imputations of missing observations and exclusion of diabetes cases. The observed associations between AP exposure and MetS were sensitive to MetS definitions. Regarding the MetS components, we observed strongest associations with impaired fasting glycemia, and positive but weaker associations with hypertension and waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. Welldesigned prospective studies are needed to confirm these findings.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"PLoS ONE","grobid_abstract_attachment_id":45887150},"translated_abstract":null,"internal_url":"https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China","translated_internal_url":"","created_at":"2015-06-08T00:33:55.428-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796471,"work_id":12857209,"tagging_user_id":31976424,"tagged_user_id":32035136,"co_author_invite_id":286927,"email":"c***i@deplazio.it","display_order":-7589692,"name":"Chiara Badaloni","title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857208/Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities">Particulate matter and out-of-hospital coronary deaths in eight Italian cities</a></div><div class="wp-workCard_item"><span>Occupational and Environmental Medicine</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857208"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857208"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857208; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857208]").text(description); $(".js-view-count[data-work-id=12857208]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857208; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857208']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857208, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857208]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857208,"title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities","translated_title":"","metadata":{"abstract":"We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Occupational and Environmental Medicine"},"translated_abstract":"We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.","internal_url":"https://www.academia.edu/12857208/Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities","translated_internal_url":"","created_at":"2015-06-08T00:33:55.260-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796350,"work_id":12857208,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities"},{"id":796371,"work_id":12857208,"tagging_user_id":31976424,"tagged_user_id":12088562,"co_author_invite_id":null,"email":"f***e@deplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities"}],"downloadable_attachments":[],"slug":"Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31976424,"first_name":"Massimo","middle_initials":null,"last_name":"Stafoggia","page_name":"MassimoStafoggia","domain_name":"hsph-harvard","created_at":"2015-06-08T00:33:15.635-07:00","display_name":"Massimo Stafoggia","url":"https://hsph-harvard.academia.edu/MassimoStafoggia"},"attachments":[],"research_interests":[{"id":11976,"name":"Air pollution","url":"https://www.academia.edu/Documents/in/Air_pollution"},{"id":22772,"name":"Urban Health","url":"https://www.academia.edu/Documents/in/Urban_Health"},{"id":45213,"name":"Italy","url":"https://www.academia.edu/Documents/in/Italy"},{"id":54259,"name":"Cities","url":"https://www.academia.edu/Documents/in/Cities"},{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality"},{"id":125564,"name":"Statistical Significance","url":"https://www.academia.edu/Documents/in/Statistical_Significance"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":312164,"name":"Occupational","url":"https://www.academia.edu/Documents/in/Occupational"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"},{"id":518609,"name":"Particulate Matter","url":"https://www.academia.edu/Documents/in/Particulate_Matter"},{"id":559242,"name":"Cardiovascular Diseases","url":"https://www.academia.edu/Documents/in/Cardiovascular_Diseases"},{"id":1146830,"name":"Cross-Over Studies","url":"https://www.academia.edu/Documents/in/Cross-Over_Studies"},{"id":1834376,"name":"Occupational and environmental medicine","url":"https://www.academia.edu/Documents/in/Occupational_and_environmental_medicine"},{"id":2345130,"name":"Socioeconomic Factors","url":"https://www.academia.edu/Documents/in/Socioeconomic_Factors"}],"urls":[]}, dispatcherData: dispatcherData }); 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Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"The Lancet","grobid_abstract_attachment_id":45898827},"translated_abstract":null,"internal_url":"https://www.academia.edu/12840189/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project","translated_internal_url":"","created_at":"2015-06-07T04:01:56.965-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31949546,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":785150,"work_id":12840189,"tagging_user_id":31949546,"tagged_user_id":38513023,"co_author_invite_id":284115,"email":"m***i@unibas.ch","affiliation":"Swiss Tropical and Public 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src="https://attachments.academia-assets.com/45898844/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840187/Comparison_of_regression_models_with_land_use_and_emission_data_to_predict_the_spatial_distribution_of_traffic_related_air_pollution_in_Rome">Comparison of regression models with land-use and emission data to predict the spatial distribution of traffic-related air pollution in Rome</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>Journal of Exposure Science and Environmental Epidemiology</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="403fb9054191c208049995496a142960" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45898844,"asset_id":12840187,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45898844/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12840187"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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The aim of this study was to develop a land-use regression model to predict nitrogen dioxide (NO 2 ) concentrations and compare its performance with a model including emissions data. The association between each land-use variable and NO 2 concentrations at 68 locations in Rome in 1995 and 1996 was assessed by univariate linear regression and a multiple linear regression model that was constructed based on the importance of each variable. Traffic emissions (particulate matter, carbon monoxide, nitrogen oxides, and benzene) were estimated for 164 areas of the city based on vehicle type, traffic counts and driving patterns. Mean NO 2 concentration across the 68 sites was 46.8 mg/m 3 (SD 9.8 mg/m 3 ; inter-quartile range 11.5 mg/m 3 ; min 24 mg/m 3 ; max 73 mg/m 3 ). The most important predicting variables were the circular traffic zones (main ring road, green strip, inner ring road, traffic-limited zone), distance from busy streets, size of the census block, the inverse population density, and altitude. A multiple regression model including these variables resulted in an R 2 of 0.686. The best-fitting model adding an emission term of benzene resulted in an R 2 of 0.690, but was not significantly different from the model without emissions (P ¼ 0.147). In conclusion, these results suggest that a land-use regression model explains the traffic-related air pollution levels with reasonable accuracy and that emissions data do not significantly improve the model.","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Journal of Exposure Science and Environmental 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<div class="js-work-strip profile--work_container" data-work-id="12857207"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis"><img alt="Research paper thumbnail of Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/45887132/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis">Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis</a></div><div class="wp-workCard_item"><span>Journal of Epidemiology & Community Health</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aae43477d78baa4c7b5109933fa2a67e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887132,"asset_id":12857207,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887132/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857207"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857207"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857207; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857207]").text(description); $(".js-view-count[data-work-id=12857207]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857207; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857207']"); 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$('.js-work-strip[data-work-id=12857207]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857207,"title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis","translated_title":"","metadata":{"ai_title_tag":"Heat-Related In-Hospital Mortality: A Multi-City Study","grobid_abstract":"A table on the distribution of acute medical conditions of the patients by age groups and hospital wards is published online only at ABSTRACT Background: Several studies have identified strong effects of high temperatures on mortality at population level; however, individual vulnerability factors associated with heat-related in-hospital mortality are largely unknown. The objective of the study was to evaluate heat-related in-hospital mortality using a multi-city casecrossover analysis. Methods: We studied residents of four Italian cities, aged 65+ years, who died during 1997-2004. For 94 944 individuals who died in hospital and were hospitalised two or more days before death, demographics, chronic conditions, primary diagnoses of last event and hospital wards were considered. A city-specific case-crossover analysis was performed to evaluate the association between apparent temperature and mortality. Pooled odds ratios (OR) of dying on a day with a temperature of 30uC compared to a day with a temperature of 20uC were estimated with a random-effects meta-analysis. Results: We estimated an overall OR of 1.32 (95% confidence interval: 1.25, 1.39). Age, marital status and hospital ward were important risk indicators. Patients in general medicine were at higher risk than those in high and intensive care units. A history of psychiatric disorders and cerebrovascular diseases gave a higher vulnerability. Mortality was greater among patients hospitalised for heart failure, stroke and chronic pulmonary diseases. Conclusions: In-hospital mortality is strongly associated with high temperatures. A comfortable temperature in hospitals and increased attention to vulnerable patients during heatwaves, especially in general medicine, are necessary preventive measures.","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Journal of Epidemiology \u0026 Community Health","grobid_abstract_attachment_id":45887132},"translated_abstract":null,"internal_url":"https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis","translated_internal_url":"","created_at":"2015-06-08T00:33:54.092-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796370,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":12088562,"co_author_invite_id":null,"email":"f***e@deplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796457,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":286923,"email":"m***e@unicampus.it","display_order":null,"name":"M. Maria","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796349,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796450,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":62879380,"co_author_invite_id":286922,"email":"t***a@gmail.com","display_order":null,"name":"Martín Demaría","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796460,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":36749867,"co_author_invite_id":286924,"email":"p***i@deplazio.it","display_order":null,"name":"Paola Michelozzi","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"}],"downloadable_attachments":[{"id":45887132,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45887132/thumbnails/1.jpg","file_name":"Factors_affecting_in-hospital_heat-relat20160523-758-a77qfk.pdf","download_url":"https://www.academia.edu/attachments/45887132/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Factors_affecting_in_hospital_heat_relat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45887132/Factors_affecting_in-hospital_heat-relat20160523-758-a77qfk-libre.pdf?1464028988=\u0026response-content-disposition=attachment%3B+filename%3DFactors_affecting_in_hospital_heat_relat.pdf\u0026Expires=1732972029\u0026Signature=IH6PHvQDDez9nJbS3fUT40GbI~kBY~bJnvISsQxlSrtObBjW2cLe0P35AWGt7X8AIK22~-XWXqjRHIU87fltecVFFgq5mixedRIF0F~ntvGjhNQ7vP3mNeTUtJVOOjsHOBDwOGttOLht2HQ4huCOkxsVvoyeRyO6rKr9MNkwljB6TqPmr84L4BPmg4KO3uHghsbloHY4S4Bji7Do9y7MDKoog2ikTOK1nkGV9Oyu~Oo9CieBj8davIsSCfABxynORtJJ-FovEVBUkwUobvotjkJk5Di2PoUvkj4GBZcLH-5dovdNqXdJNcgLVtl90Gep8lTPiJbfuVQQxPiqrlG1yw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":31976424,"first_name":"Massimo","middle_initials":null,"last_name":"Stafoggia","page_name":"MassimoStafoggia","domain_name":"hsph-harvard","created_at":"2015-06-08T00:33:15.635-07:00","display_name":"Massimo 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Unit","url":"https://www.academia.edu/Documents/in/Intensive_Care_Unit"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"},{"id":486757,"name":"Random-Effects Models","url":"https://www.academia.edu/Documents/in/Random-Effects_Models"},{"id":901284,"name":"Hospital Mortality","url":"https://www.academia.edu/Documents/in/Hospital_Mortality"},{"id":1266529,"name":"Pulmonary disease","url":"https://www.academia.edu/Documents/in/Pulmonary_disease"},{"id":1491514,"name":"*Hot Temperature","url":"https://www.academia.edu/Documents/in/_Hot_Temperature"},{"id":1587858,"name":"Confidence Interval","url":"https://www.academia.edu/Documents/in/Confidence_Interval"},{"id":1981316,"name":"Marital Status","url":"https://www.academia.edu/Documents/in/Marital_Status"},{"id":2037489,"name":"Preventive measures","url":"https://www.academia.edu/Documents/in/Preventive_measures"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857206"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857206/Spie_charts_target_plots_and_radar_plots_for_displaying_comparative_outcomes_of_health_care"><img alt="Research paper thumbnail of Spie charts, target plots, and radar plots for displaying comparative outcomes of health care" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857206/Spie_charts_target_plots_and_radar_plots_for_displaying_comparative_outcomes_of_health_care">Spie charts, target plots, and radar plots for displaying comparative outcomes of health care</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MariangelaDOvidio">Mariangela D'Ovidio</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AdeleLallo">Adele Lallo</a></span></div><div class="wp-workCard_item"><span>Journal of Clinical Epidemiology</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. Furthermore, they are well suited to summarize the overall performance of a health care provider with a single score. &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts represented the best graphical tool for displaying multivariate health care data in comparative evaluations of clinical outcomes and processes of care among health care providers.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857206"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857206"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857206; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857206]").text(description); $(".js-view-count[data-work-id=12857206]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857206; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857206']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857206, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857206]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857206,"title":"Spie charts, target plots, and radar plots for displaying comparative outcomes of health care","translated_title":"","metadata":{"abstract":"Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. Furthermore, they are well suited to summarize the overall performance of a health care provider with a single score. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts represented the best graphical tool for displaying multivariate health care data in comparative evaluations of clinical outcomes and processes of care among health care providers.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Journal of Clinical Epidemiology"},"translated_abstract":"Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857204"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857204/Identification_of_Population_Subgroups_Susceptible_to_Heat_in_Italy"><img alt="Research paper thumbnail of Identification of Population Subgroups Susceptible to Heat in Italy" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857204/Identification_of_Population_Subgroups_Susceptible_to_Heat_in_Italy">Identification of Population Subgroups Susceptible to Heat in Italy</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MariangelaDOvidio">Mariangela D'Ovidio</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/UKirchmayer">U. 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Demaria","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763202,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277885,"email":"l***i@asl.milano.it","display_order":null,"name":"Luigi Bisanti","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763108,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277880,"email":"r***a@aslmb.it","display_order":null,"name":"Magda Rognoni","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763049,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277868,"email":"b***i@stat.ds.unifi.it","display_order":null,"name":"Michela Baccini","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763156,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277884,"email":"s***a@asplazio.it","display_order":null,"name":"Massimo Stafoggia","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763132,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277882,"email":"p***i@asplazio.it","display_order":null,"name":"Carlo Perucci","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763169,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763114,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":null,"co_author_invite_id":277881,"email":"r***o@sancarlo.mi.it","display_order":null,"name":"Antonio Russo","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"},{"id":763144,"work_id":12809829,"tagging_user_id":31895672,"tagged_user_id":31976424,"co_author_invite_id":277883,"email":"m***a@deplazio.it","affiliation":"Harvard School of Public Health","display_order":null,"name":"Massimo Stafoggia","title":"Heat-Related Mortality: a Case-Only Approach to Evaluate Effect Modification"}],"downloadable_attachments":[],"slug":"Heat_Related_Mortality_a_Case_Only_Approach_to_Evaluate_Effect_Modification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31895672,"first_name":"Ennio","middle_initials":null,"last_name":"Cadum","page_name":"EnnioCadum","domain_name":"independent","created_at":"2015-06-05T02:34:51.110-07:00","display_name":"Ennio Cadum","url":"https://independent.academia.edu/EnnioCadum"},"attachments":[],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics"},{"id":1085,"name":"Epidemiology","url":"https://www.academia.edu/Documents/in/Epidemiology"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3014374" id="papers"><div class="js-work-strip profile--work_container" data-work-id="24437482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects"><img alt="Research paper thumbnail of Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects" class="work-thumbnail" src="https://attachments.academia-assets.com/44766805/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects">Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ucl.academia.edu/MichalisKatsoulis">Michalis Katsoulis</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: Associations between long-term exposure to ambient particulate matter (PM) and cardio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular<br />(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents<br />of PM on total CVD mortality have been explored in a single study only.<br />Aims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.<br />Methods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European<br />Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health<br />impacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average<br />exposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was<br />estimated using Land Use Regression models. Eight elements representing major sources were selected a<br />priori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were<br />conducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis<br />was used to calculate combined effect estimates.<br />Results: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically<br />significant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the<br />pooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was<br />0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5<br />(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).<br />Conclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between<br />long-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eaa0fdbcdf42020a5594a08fedf83052" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44766805,"asset_id":24437482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44766805/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="24437482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="24437482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24437482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24437482]").text(description); $(".js-view-count[data-work-id=24437482]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24437482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='24437482']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 24437482, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eaa0fdbcdf42020a5594a08fedf83052" } } $('.js-work-strip[data-work-id=24437482]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":24437482,"title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects","translated_title":"","metadata":{"abstract":"Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular\n(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents\nof PM on total CVD mortality have been explored in a single study only.\nAims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.\nMethods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European\nStudy of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health\nimpacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average\nexposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was\nestimated using Land Use Regression models. Eight elements representing major sources were selected a\npriori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were\nconducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis\nwas used to calculate combined effect estimates.\nResults: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically\nsignificant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the\npooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was\n0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5\n(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).\nConclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between\nlong-term exposure to 8 elemental constituents of particles and total cardiovascular mortality."},"translated_abstract":"Background: Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular\n(CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents\nof PM on total CVD mortality have been explored in a single study only.\nAims: The aim of this study was to examine the association of PM composition with cardiovascular mortality.\nMethods: We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European\nStudy of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health\nimpacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average\nexposure to elemental constituentswithin particlematter smaller than 2.5 and 10 μm(PM2.5 and PM10)was\nestimated using Land Use Regression models. Eight elements representing major sources were selected a\npriori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were\nconducted using Cox proportional hazards models with a standardized protocol. Random-effects metaanalysis\nwas used to calculate combined effect estimates.\nResults: The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically\nsignificant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the\npooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was\n0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5\n(1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).\nConclusion: In a joint analysis of 19 European cohorts, we found no statistically significant association between\nlong-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.","internal_url":"https://www.academia.edu/24437482/Long_term_exposure_to_elemental_constituents_of_particulate_matter_and_cardiovascular_mortality_in_19_European_cohorts_Results_from_the_ESCAPE_and_TRANSPHORM_projects","translated_internal_url":"","created_at":"2016-04-15T09:16:52.389-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":43522758,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":19030291,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":4328847,"email":"m***s@ucl.ac.uk","display_order":-3,"name":"Michail Katsoulis","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030292,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":365088,"email":"a***a@nut.uoa.gr","display_order":4194301,"name":"Antonia Trichopoulou","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030293,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":278760017,"co_author_invite_id":245442,"email":"k***y@med.uoa.gr","display_order":6291453,"name":"Klea Katsouyanni","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030294,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":7340029,"name":"Francesco Forastiere","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030295,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":31949546,"co_author_invite_id":null,"email":"a***i@arpa.emr.it","display_order":7864317,"name":"Andrea Ranzi","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030296,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":31976424,"co_author_invite_id":null,"email":"m***a@deplazio.it","affiliation":"Harvard School of Public Health","display_order":8126461,"name":"Massimo Stafoggia","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030297,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":32000612,"co_author_invite_id":null,"email":"f***i@gmail.com","display_order":8257533,"name":"Fulvio Ricceri","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030298,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":32826726,"co_author_invite_id":null,"email":"m***k@ki.se","affiliation":"Karolinska Institutet","display_order":8323069,"name":"Michal Korek","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030299,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":38513023,"co_author_invite_id":null,"email":"m***i@unibas.ch","affiliation":"Swiss Tropical and Public Health Institute (Swiss TPH)","display_order":8355837,"name":"Ming-Yi Tsai","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030300,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":115595,"email":"p***s@helmholtz-muenchen.de","display_order":8372221,"name":"Annette Peters","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030301,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":284095,"email":"a***n@univr.it","display_order":8380413,"name":"Alessandro Marcon","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030302,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":32728784,"co_author_invite_id":null,"email":"g***i@deplazio.it","display_order":8384509,"name":"Giulia Cesaroni","title":"Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects"},{"id":19030303,"work_id":24437482,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":4329396,"email":"c***i@asplazio.it","display_order":8386557,"name":"G. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="24437188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project"><img alt="Research paper thumbnail of Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project" class="work-thumbnail" src="https://attachments.academia-assets.com/44766522/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project">Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ucl.academia.edu/MichalisKatsoulis">Michalis Katsoulis</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JoachimHeinrich">Joachim Heinrich</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background Few studies on long-term exposure to air pollution and mortality have been reported fr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.<br />Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the<br />association between natural-cause mortality and long-term exposure to several air pollutants.<br />Methods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.<br />All cohorts were general population samples, although some were restricted to one sex only. With a strictly<br />standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of<br />particulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and<br />2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land<br />use regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road<br />(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical<br />analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional<br />hazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.<br />Findings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk<br />(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased<br />hazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted<br />between individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when<br />we included only participants exposed to pollutant concentrations lower than the European annual mean limit value<br />of 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).<br />Interpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even<br />within concentration ranges well below the present European annual mean limit value.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f9f347671dff528bad7ad71bd7b7ea8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44766522,"asset_id":24437188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44766522/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="24437188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="24437188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24437188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24437188]").text(description); $(".js-view-count[data-work-id=24437188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24437188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='24437188']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 24437188, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3f9f347671dff528bad7ad71bd7b7ea8" } } $('.js-work-strip[data-work-id=24437188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":24437188,"title":"Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project","translated_title":"","metadata":{"abstract":"Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.\nWithin the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the\nassociation between natural-cause mortality and long-term exposure to several air pollutants.\nMethods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.\nAll cohorts were general population samples, although some were restricted to one sex only. With a strictly\nstandardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of\nparticulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and\n2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land\nuse regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road\n(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical\nanalyses using confounder models with increasing adjustment for confounder variables, and Cox proportional\nhazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.\nFindings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk\n(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased\nhazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted\nbetween individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when\nwe included only participants exposed to pollutant concentrations lower than the European annual mean limit value\nof 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).\nInterpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even\nwithin concentration ranges well below the present European annual mean limit value."},"translated_abstract":"Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe.\nWithin the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the\nassociation between natural-cause mortality and long-term exposure to several air pollutants.\nMethods We used data from 22 European cohort studies, which created a total study population of 367 251 participants.\nAll cohorts were general population samples, although some were restricted to one sex only. With a strictly\nstandardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of\nparticulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and\n2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land\nuse regression models. We also investigated two traffic intensity variables—traffic intensity on the nearest road\n(vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical\nanalyses using confounder models with increasing adjustment for confounder variables, and Cox proportional\nhazards models with a common protocol. We obtained pooled effect estimates through a random-effects metaanalysis.\nFindings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk\n(average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased\nhazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02–1·13) per 5 μg/m³ was recorded. No heterogeneity was noted\nbetween individual cohort effect estimates (I² p value=0·95). HRs for PM2·5 remained significantly raised even when\nwe included only participants exposed to pollutant concentrations lower than the European annual mean limit value\nof 25 μg/m³ (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m³ (1·07, 1·01–1·13).\nInterpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even\nwithin concentration ranges well below the present European annual mean limit value.","internal_url":"https://www.academia.edu/24437188/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project","translated_internal_url":"","created_at":"2016-04-15T09:05:02.339-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":43522758,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":19029887,"work_id":24437188,"tagging_user_id":43522758,"tagged_user_id":278760017,"co_author_invite_id":245442,"email":"k***y@med.uoa.gr","display_order":-1,"name":"Klea Katsouyanni","title":"Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project"},{"id":19029888,"work_id":24437188,"tagging_user_id":43522758,"tagged_user_id":null,"co_author_invite_id":4328847,"email":"m***s@ucl.ac.uk","display_order":4194303,"name":"Michail Katsoulis","title":"Effects 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dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857214"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857214/A_Case_Crossover_Analysis_of_Out_of_Hospital_Coronary_Deaths_and_Air_Pollution_in_Rome_Italy"><img alt="Research paper thumbnail of A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy" class="work-thumbnail" src="https://attachments.academia-assets.com/45887135/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857214/A_Case_Crossover_Analysis_of_Out_of_Hospital_Coronary_Deaths_and_Air_Pollution_in_Rome_Italy">A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DanielaDippoliti">Daniela Dippoliti</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the association with air pollution is not well understood. Objectives: We evaluated the association between daily ambient air pollution levels (particle number concentration (PNC)—a proxy for ultrafine particles (diameter 0.1 m), mass of particles with dia- meter less than 10 m (PM10); CO, NO2 ,a nd O3)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8515c8f5f576e4bf2ba20ad85415a7ed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887135,"asset_id":12857214,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887135/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857214"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857214"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857214; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857214]").text(description); $(".js-view-count[data-work-id=12857214]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857214; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857214']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857214, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8515c8f5f576e4bf2ba20ad85415a7ed" } } $('.js-work-strip[data-work-id=12857214]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857214,"title":"A Case-Crossover Analysis of Out-of-Hospital Coronary Deaths and Air Pollution in Rome, Italy","translated_title":"","metadata":{"abstract":"Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the association with air pollution is not well understood. 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857213/_Air_pollution_and_fatal_and_non_fatal_coronary_events_in_Rome_">[Air pollution and fatal and non fatal coronary events in Rome]</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/DanielaDippoliti">Daniela Dippoliti</a></span></div><div class="wp-workCard_item"><span>Epidemiologia e prevenzione</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To examine the relationship between air pollution and coronary events in Rome in the period 1998-...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To examine the relationship between air pollution and coronary events in Rome in the period 1998-2000, considering both out-of-hospital deaths and hospitalisations. Time-series of daily counts of out-of-hospital deaths and hospitalised events, implementation of Generalised Additive Models. The air pollutants taken into account were PNC (Particle Number Concentration--a measure of ultrafine particles), PM10, CO, NO2, SO2 and O3. The association was studied with respect to either single days or the cumulative effect on more consecutive days; furthermore, effect modification by age was tested (for the age groups 0-64, 65-74 and 75+). People resident of Rome and died/hospitalised for coronary causes into the city in the period 1998-2000. Association between pollutants and out-of-hospital deaths/hospitalised events. Distinction between fatal events (out-of-hospital deaths + hospitalisations with death within 28 days of admission) and non fatal events (hospitalisations with survival longe...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857213"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857213"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857213; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857213]").text(description); $(".js-view-count[data-work-id=12857213]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857213; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857213']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857213, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857213]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857213,"title":"[Air pollution and fatal and non fatal coronary events in Rome]","translated_title":"","metadata":{"abstract":"To examine the relationship between air pollution and coronary events in Rome in the period 1998-2000, considering both out-of-hospital deaths and hospitalisations. 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A multi-city study was designed to assess the probability of recurrent hospitalization in a cohort of incident myocardial infarction survivors in five European cities. The objective of this paper is to discuss the methods for analyzing short-term health effects in a cohort study based on a case-series. Three methods were considered for the analyses of the cohort data: Poisson regression approach, case-crossover analyses and extended Cox regression analyses. The major challenge of these analyses is to appropriately consider changes within the cohort over time due to changes in the underlying risk following a myocardial infarction, slow time trends in risk factors within the population, dynamic cohort size and seasonal variation. Poisson regression analyses, case-crossover analyses and Extended Cox regression analyses gave similar results. Application of smoothing methods...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="20ad175be3c630984bc50d75583629bb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887108,"asset_id":12857212,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887108/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857212"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857212"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857212; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857212]").text(description); $(".js-view-count[data-work-id=12857212]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857212; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857212']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857212, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "20ad175be3c630984bc50d75583629bb" } } $('.js-work-strip[data-work-id=12857212]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857212,"title":"Comparison of different methods in analyzing short-term air pollution effects in a cohort study of susceptible individuals","translated_title":"","metadata":{"abstract":"Short-term fluctuations of ambient air pollution have been associated with exacerbation of cardiovascular disease. 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alt="Research paper thumbnail of The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857211/The_risks_of_acute_exposure_to_black_carbon_in_Southern_Europe_results_from_the_MED_PARTICLES_project">The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnunziataFaustini">Annunziata Faustini</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EnnioCadum">Ennio Cadum</a></span></div><div class="wp-workCard_item"><span>Occupational and environmental medicine</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">While several studies have reported associations of daily exposures to PM2.5 (particles less than...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">While several studies have reported associations of daily exposures to PM2.5 (particles less than 2.5 µm) with mortality, few studies have examined the impact of its constituents such as black carbon (BC), which is also a significant contributor to global climate change. We assessed the association between daily concentrations of BC and total, cardiovascular and respiratory mortality in two southern Mediterranean cities. Daily averages of BC were collected for 2 years in Barcelona, Spain and Athens, Greece. We used case-crossover analysis and examined single and cumulative lags up to 3 days. We observed associations between BC and all mortality measures. For a 3-day moving average, cardiovascular mortality increased by 4.5% (95% CI 0.7 to 8.5) and 2.0% (95% CI 0 to 4.0) for an interquartile change in BC in Athens and Barcelona, respectively. Considerably higher effects for respiratory mortality and for those above age 65 were observed. In addition, BC exhibited much greater toxicity...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857211"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857211"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857211; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857211]").text(description); $(".js-view-count[data-work-id=12857211]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857211; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857211']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857211, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857211]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857211,"title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"While several studies have reported associations of daily exposures to PM2.5 (particles less than 2.5 µm) with mortality, few studies have examined the impact of its constituents such as black carbon (BC), which is also a significant contributor to global climate change. We assessed the association between daily concentrations of BC and total, cardiovascular and respiratory mortality in two southern Mediterranean cities. Daily averages of BC were collected for 2 years in Barcelona, Spain and Athens, Greece. We used case-crossover analysis and examined single and cumulative lags up to 3 days. We observed associations between BC and all mortality measures. For a 3-day moving average, cardiovascular mortality increased by 4.5% (95% CI 0.7 to 8.5) and 2.0% (95% CI 0 to 4.0) for an interquartile change in BC in Athens and Barcelona, respectively. Considerably higher effects for respiratory mortality and for those above age 65 were observed. In addition, BC exhibited much greater toxicity...","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Occupational and environmental medicine"},"translated_abstract":"While several studies have reported associations of daily exposures to PM2.5 (particles less than 2.5 µm) with mortality, few studies have examined the impact of its constituents such as black carbon (BC), which is also a significant contributor to global climate change. We assessed the association between daily concentrations of BC and total, cardiovascular and respiratory mortality in two southern Mediterranean cities. Daily averages of BC were collected for 2 years in Barcelona, Spain and Athens, Greece. We used case-crossover analysis and examined single and cumulative lags up to 3 days. We observed associations between BC and all mortality measures. For a 3-day moving average, cardiovascular mortality increased by 4.5% (95% CI 0.7 to 8.5) and 2.0% (95% CI 0 to 4.0) for an interquartile change in BC in Athens and Barcelona, respectively. Considerably higher effects for respiratory mortality and for those above age 65 were observed. In addition, BC exhibited much greater toxicity...","internal_url":"https://www.academia.edu/12857211/The_risks_of_acute_exposure_to_black_carbon_in_Southern_Europe_results_from_the_MED_PARTICLES_project","translated_internal_url":"","created_at":"2015-06-08T00:33:55.876-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796447,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":65210792,"co_author_invite_id":283638,"email":"e***i@med.uoa.gr","display_order":0,"name":"Evangelia Samoli","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796440,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":283621,"email":"b***n@inserm.fr","display_order":null,"name":"Bénédicte Jacquemin","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796438,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":135504,"email":"x***a@creal.cat","display_order":null,"name":"Xavier Basagaña","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796384,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":12088562,"co_author_invite_id":null,"email":"f***e@deplazio.it","display_order":null,"name":"Francesco Forastiere","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796478,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":11215340,"co_author_invite_id":null,"email":"k***e@gmail.com","display_order":null,"name":"Kostas Eleftheriadis","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796339,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":138867,"email":"j***r@creal.cat","display_order":null,"name":"Jordi Sunyer","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796343,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":31946160,"co_author_invite_id":null,"email":"a***i@deplazio.it","display_order":null,"name":"Annunziata Faustini","title":"The risks of acute exposure to black carbon in Southern Europe: results from the MED-PARTICLES project"},{"id":796456,"work_id":12857211,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":283624,"email":"g***i@guest.unimi.it","display_order":null,"name":"G. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12838112"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12838112/Short_term_effects_of_particulate_matter_constituents_on_daily_hospitalizations_and_mortality_in_five_South_European_cities_Results_from_the_MED_PARTICLES_project"><img alt="Research paper thumbnail of Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12838112/Short_term_effects_of_particulate_matter_constituents_on_daily_hospitalizations_and_mortality_in_five_South_European_cities_Results_from_the_MED_PARTICLES_project">Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AnnunziataFaustini">Annunziata Faustini</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EAlessandrini">Ester Alessandrini</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a></span></div><div class="wp-workCard_item"><span>Environment international</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Few recent studies examined acute effects on health of individual chemical species in the particu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. Studies in Southern Europe are scarce. The aim of this study is to examine the relationship between particulate matter constituents and daily hospital admissions and mortality in five cities in Southern Europe. The study included five cities in Southern Europe, three cities in Spain: Barcelona (2003-2010), Madrid (2007-2008) and Huelva (2003-2010); and two cities in Italy: Rome (2005-2007) and Bologna (2011-2013). A case-crossover design was used to link cardiovascular and respiratory hospital admissions and total, cardiovascular and respiratory mortality with a pre-defined list of 16 PM10 and PM2.5 constituents. Lags 0 to 2 were examined. City-specific results were combined by random-effects meta-analysis. Most of the elements studied, namely EC, SO4(2-), SiO2, Ca, Fe, Zn, Cu, Ti, Mn, V and Ni, showed incre...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12838112"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12838112"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12838112; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12838112]").text(description); $(".js-view-count[data-work-id=12838112]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12838112; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12838112']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12838112, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12838112]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12838112,"title":"Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. 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Most of the elements studied, namely EC, SO4(2-), SiO2, Ca, Fe, Zn, Cu, Ti, Mn, V and Ni, showed incre...","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Environment international"},"translated_abstract":"Few recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. Studies in Southern Europe are scarce. The aim of this study is to examine the relationship between particulate matter constituents and daily hospital admissions and mortality in five cities in Southern Europe. The study included five cities in Southern Europe, three cities in Spain: Barcelona (2003-2010), Madrid (2007-2008) and Huelva (2003-2010); and two cities in Italy: Rome (2005-2007) and Bologna (2011-2013). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857210"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project"><img alt="Research paper thumbnail of Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project">Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/EvangeliaSamoli">Evangelia Samoli</a></span></div><div class="wp-workCard_item"><span>Science of The Total Environment</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Previous studies reported significant variability of air pollutants across Europe with the lowest...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857210"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857210"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857210; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857210]").text(description); $(".js-view-count[data-work-id=12857210]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857210; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857210']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857210, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857210]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857210,"title":"Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project","translated_title":"","metadata":{"abstract":"Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Science of The Total Environment"},"translated_abstract":"Previous studies reported significant variability of air pollutants across Europe with the lowest concentrations generally found in Northern Europe and the highest in Southern European countries. Within the MED-PARTICLES project the spatial and temporal variations of long-term PM and gaseous pollutants data were investigated in traffic and urban background sites across Southern Europe. The highest PM levels were observed in Greece and Italy (Athens, Thessaloniki, Turin and Rome) while all traffic sites showed high NO2 levels, frequently exceeding the established limit value. High PM2.5/PM10 ratios were calculated indicating that fine particles comprise a large fraction of PM10, with the highest values found in the urban background sites. It seems that although in traffic sites the concentrations of both PM2.5 and PM10 are significantly higher than those registered in urban background sites, the coarse fraction PM2.5-10 is more important at the traffic sites. This fact is probably due to the high levels of resuspended road dust in sites highly affected by traffic, a phenomenon particularly relevant for Mediterranean countries. The long-term trends of air pollutants revealed a significant decrease of the concentration levels for PM, SO2 and CO while for NO2 no clear trend or slightly increasing trends were observed. This reduction could be attributed to the effectiveness of abatement measures and strategies and also to meteorological conditions and to the economic crisis that affected Southern Europe.","internal_url":"https://www.academia.edu/12857210/Particulate_matter_and_gaseous_pollutants_in_the_Mediterranean_Basin_Results_from_the_MED_PARTICLES_project","translated_internal_url":"","created_at":"2015-06-08T00:33:55.603-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796446,"work_id":12857210,"tagging_user_id":31976424,"tagged_user_id":65210792,"co_author_invite_id":283638,"email":"e***i@med.uoa.gr","display_order":0,"name":"Evangelia Samoli","title":"Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China"><img alt="Research paper thumbnail of Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China" class="work-thumbnail" src="https://attachments.academia-assets.com/45887150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China">Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://ki.academia.edu/MichalKorek">Michal Korek</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JosefCyrys">Josef Cyrys</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/NancyPedersen">Nancy Pedersen</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>PLoS ONE</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e01eea93bf28cc493f57993fb477e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887150,"asset_id":12857209,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887150/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857209"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857209"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857209; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857209]").text(description); $(".js-view-count[data-work-id=12857209]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857209; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857209']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857209, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28e01eea93bf28cc493f57993fb477e6" } } $('.js-work-strip[data-work-id=12857209]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857209,"title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China","translated_title":"","metadata":{"grobid_abstract":"Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardiovascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and MetS may be hypothesized. We explored this association, (taking into account, pathwayspecific MetS definitions), and its potential modifiers in Swiss adults. We studied 3769 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, reporting at least four-hour fasting time before venepuncture. AP exposures were 10-year mean residential PM 10 (particulate matter \u003c10μm in diameter) and NO 2 (nitrogen dioxide). Outcomes included MetS defined by World Health Organization (MetS-W), International Diabetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four-and eighthour fasting time limits. We also explored associations with individual components of MetS. We applied mixed logistic regression models to explore these associations. The prevalence of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W, MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per 10μg/m 3 increase in 10-year mean PM 10 . We observed weaker associations with NO 2 . Associations were stronger among physically-active, ever-smokers and non-diabetic participants especially with PM 10 (p\u003c0.05). Associations remained robust across various sensitivity analyses including ten imputations of missing observations and exclusion of diabetes cases. The observed associations between AP exposure and MetS were sensitive to MetS definitions. Regarding the MetS components, we observed strongest associations with impaired fasting glycemia, and positive but weaker associations with hypertension and waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. Welldesigned prospective studies are needed to confirm these findings.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"PLoS ONE","grobid_abstract_attachment_id":45887150},"translated_abstract":null,"internal_url":"https://www.academia.edu/12857209/Long_Term_Exposure_to_Ambient_Air_Pollution_and_Mortality_Due_to_Cardiovascular_Disease_and_Cerebrovascular_Disease_in_Shenyang_China","translated_internal_url":"","created_at":"2015-06-08T00:33:55.428-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796471,"work_id":12857209,"tagging_user_id":31976424,"tagged_user_id":32035136,"co_author_invite_id":286927,"email":"c***i@deplazio.it","display_order":-7589692,"name":"Chiara Badaloni","title":"Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857208/Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities">Particulate matter and out-of-hospital coronary deaths in eight Italian cities</a></div><div class="wp-workCard_item"><span>Occupational and Environmental Medicine</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857208"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857208"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857208; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857208]").text(description); $(".js-view-count[data-work-id=12857208]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857208; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857208']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857208, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857208]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857208,"title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities","translated_title":"","metadata":{"abstract":"We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Occupational and Environmental Medicine"},"translated_abstract":"We evaluated the association between PM(10) concentration and out-of-hospital coronary deaths in eight Italian cities during 1997-2004. 16 989 subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;35 years who died out-of-hospital from coronary causes were studied and hospital admissions in the previous 2 years identified. We studied the effect of the mean of current and previous day PM(10) values (lag 0-1). A city-specific case-crossover analysis was applied using a time-stratified approach considering as confounders weather, holidays, influenza epidemics, and summer decrease in population. The pooled percentage increase (95% CI) in mortality per 10 microg/m(3) increase in PM(10) was estimated. A statistically significant increase in out-of-hospital coronary deaths was related to a 10 microg/m(3) increase in PM(10): 1.46% (95% CI 0.50 to 2.43). Although no statistically significant effect modification by age was found, the effect was stronger among subjects aged \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;65 years (1.60%, 0.59 to 2.63), particularly those aged 65-74 (3.01%, 0.74 to 5.34). People in the lowest socio-economic category (3.34%, 1.28 to 5.45) had a stronger effect than those in the highest category. No clear effect modification was seen for gender, season or any specific comorbidity. An indication of negative effect modification was seen for previous admission for cardiac dysrhythmias. Subjects without hospital admissions in the previous 2 years were slightly more affected by PM(10) effects (1.91%, 0.28 to 3.47) than those with at least one previous hospital admission (1.44%, 0.09 to 2.82). Our results show that short term exposure to PM(10) is associated with coronary mortality especially among the elderly and socio-economically disadvantaged. No clear effect modification by previous hospitalisations was detected except for cardiac dysrhythmias, possibly due to protective treatment.","internal_url":"https://www.academia.edu/12857208/Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities","translated_internal_url":"","created_at":"2015-06-08T00:33:55.260-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796350,"work_id":12857208,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities"},{"id":796371,"work_id":12857208,"tagging_user_id":31976424,"tagged_user_id":12088562,"co_author_invite_id":null,"email":"f***e@deplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Particulate matter and out-of-hospital coronary deaths in eight Italian cities"}],"downloadable_attachments":[],"slug":"Particulate_matter_and_out_of_hospital_coronary_deaths_in_eight_Italian_cities","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31976424,"first_name":"Massimo","middle_initials":null,"last_name":"Stafoggia","page_name":"MassimoStafoggia","domain_name":"hsph-harvard","created_at":"2015-06-08T00:33:15.635-07:00","display_name":"Massimo Stafoggia","url":"https://hsph-harvard.academia.edu/MassimoStafoggia"},"attachments":[],"research_interests":[{"id":11976,"name":"Air pollution","url":"https://www.academia.edu/Documents/in/Air_pollution"},{"id":22772,"name":"Urban Health","url":"https://www.academia.edu/Documents/in/Urban_Health"},{"id":45213,"name":"Italy","url":"https://www.academia.edu/Documents/in/Italy"},{"id":54259,"name":"Cities","url":"https://www.academia.edu/Documents/in/Cities"},{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality"},{"id":125564,"name":"Statistical Significance","url":"https://www.academia.edu/Documents/in/Statistical_Significance"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":312164,"name":"Occupational","url":"https://www.academia.edu/Documents/in/Occupational"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"},{"id":518609,"name":"Particulate Matter","url":"https://www.academia.edu/Documents/in/Particulate_Matter"},{"id":559242,"name":"Cardiovascular Diseases","url":"https://www.academia.edu/Documents/in/Cardiovascular_Diseases"},{"id":1146830,"name":"Cross-Over Studies","url":"https://www.academia.edu/Documents/in/Cross-Over_Studies"},{"id":1834376,"name":"Occupational and environmental medicine","url":"https://www.academia.edu/Documents/in/Occupational_and_environmental_medicine"},{"id":2345130,"name":"Socioeconomic Factors","url":"https://www.academia.edu/Documents/in/Socioeconomic_Factors"}],"urls":[]}, dispatcherData: dispatcherData }); 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Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"The Lancet","grobid_abstract_attachment_id":45898827},"translated_abstract":null,"internal_url":"https://www.academia.edu/12840189/Effects_of_long_term_exposure_to_air_pollution_on_natural_cause_mortality_an_analysis_of_22_European_cohorts_within_the_multicentre_ESCAPE_project","translated_internal_url":"","created_at":"2015-06-07T04:01:56.965-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31949546,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":785150,"work_id":12840189,"tagging_user_id":31949546,"tagged_user_id":38513023,"co_author_invite_id":284115,"email":"m***i@unibas.ch","affiliation":"Swiss Tropical and Public 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src="https://attachments.academia-assets.com/45898844/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12840187/Comparison_of_regression_models_with_land_use_and_emission_data_to_predict_the_spatial_distribution_of_traffic_related_air_pollution_in_Rome">Comparison of regression models with land-use and emission data to predict the spatial distribution of traffic-related air pollution in Rome</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AndreaRanzi">Andrea Ranzi</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a></span></div><div class="wp-workCard_item"><span>Journal of Exposure Science and Environmental Epidemiology</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="403fb9054191c208049995496a142960" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45898844,"asset_id":12840187,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45898844/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12840187"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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The aim of this study was to develop a land-use regression model to predict nitrogen dioxide (NO 2 ) concentrations and compare its performance with a model including emissions data. The association between each land-use variable and NO 2 concentrations at 68 locations in Rome in 1995 and 1996 was assessed by univariate linear regression and a multiple linear regression model that was constructed based on the importance of each variable. Traffic emissions (particulate matter, carbon monoxide, nitrogen oxides, and benzene) were estimated for 164 areas of the city based on vehicle type, traffic counts and driving patterns. Mean NO 2 concentration across the 68 sites was 46.8 mg/m 3 (SD 9.8 mg/m 3 ; inter-quartile range 11.5 mg/m 3 ; min 24 mg/m 3 ; max 73 mg/m 3 ). The most important predicting variables were the circular traffic zones (main ring road, green strip, inner ring road, traffic-limited zone), distance from busy streets, size of the census block, the inverse population density, and altitude. A multiple regression model including these variables resulted in an R 2 of 0.686. The best-fitting model adding an emission term of benzene resulted in an R 2 of 0.690, but was not significantly different from the model without emissions (P ¼ 0.147). In conclusion, these results suggest that a land-use regression model explains the traffic-related air pollution levels with reasonable accuracy and that emissions data do not significantly improve the model.","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Journal of Exposure Science and Environmental 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<div class="js-work-strip profile--work_container" data-work-id="12857207"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis"><img alt="Research paper thumbnail of Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/45887132/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis">Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis</a></div><div class="wp-workCard_item"><span>Journal of Epidemiology & Community Health</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aae43477d78baa4c7b5109933fa2a67e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45887132,"asset_id":12857207,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45887132/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857207"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857207"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857207; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857207]").text(description); $(".js-view-count[data-work-id=12857207]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857207; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857207']"); 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$('.js-work-strip[data-work-id=12857207]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857207,"title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis","translated_title":"","metadata":{"ai_title_tag":"Heat-Related In-Hospital Mortality: A Multi-City Study","grobid_abstract":"A table on the distribution of acute medical conditions of the patients by age groups and hospital wards is published online only at ABSTRACT Background: Several studies have identified strong effects of high temperatures on mortality at population level; however, individual vulnerability factors associated with heat-related in-hospital mortality are largely unknown. The objective of the study was to evaluate heat-related in-hospital mortality using a multi-city casecrossover analysis. Methods: We studied residents of four Italian cities, aged 65+ years, who died during 1997-2004. For 94 944 individuals who died in hospital and were hospitalised two or more days before death, demographics, chronic conditions, primary diagnoses of last event and hospital wards were considered. A city-specific case-crossover analysis was performed to evaluate the association between apparent temperature and mortality. Pooled odds ratios (OR) of dying on a day with a temperature of 30uC compared to a day with a temperature of 20uC were estimated with a random-effects meta-analysis. Results: We estimated an overall OR of 1.32 (95% confidence interval: 1.25, 1.39). Age, marital status and hospital ward were important risk indicators. Patients in general medicine were at higher risk than those in high and intensive care units. A history of psychiatric disorders and cerebrovascular diseases gave a higher vulnerability. Mortality was greater among patients hospitalised for heart failure, stroke and chronic pulmonary diseases. Conclusions: In-hospital mortality is strongly associated with high temperatures. A comfortable temperature in hospitals and increased attention to vulnerable patients during heatwaves, especially in general medicine, are necessary preventive measures.","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Journal of Epidemiology \u0026 Community Health","grobid_abstract_attachment_id":45887132},"translated_abstract":null,"internal_url":"https://www.academia.edu/12857207/Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis","translated_internal_url":"","created_at":"2015-06-08T00:33:54.092-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31976424,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":796370,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":12088562,"co_author_invite_id":null,"email":"f***e@deplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796457,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":286923,"email":"m***e@unicampus.it","display_order":null,"name":"M. Maria","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796349,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":null,"co_author_invite_id":184902,"email":"f***e@asplazio.it","display_order":null,"name":"Francesco Forastiere","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796450,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":62879380,"co_author_invite_id":286922,"email":"t***a@gmail.com","display_order":null,"name":"Martín Demaría","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"},{"id":796460,"work_id":12857207,"tagging_user_id":31976424,"tagged_user_id":36749867,"co_author_invite_id":286924,"email":"p***i@deplazio.it","display_order":null,"name":"Paola Michelozzi","title":"Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis"}],"downloadable_attachments":[{"id":45887132,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/45887132/thumbnails/1.jpg","file_name":"Factors_affecting_in-hospital_heat-relat20160523-758-a77qfk.pdf","download_url":"https://www.academia.edu/attachments/45887132/download_file?st=MTczMzAyMDYxNCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Factors_affecting_in_hospital_heat_relat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/45887132/Factors_affecting_in-hospital_heat-relat20160523-758-a77qfk-libre.pdf?1464028988=\u0026response-content-disposition=attachment%3B+filename%3DFactors_affecting_in_hospital_heat_relat.pdf\u0026Expires=1732972029\u0026Signature=IH6PHvQDDez9nJbS3fUT40GbI~kBY~bJnvISsQxlSrtObBjW2cLe0P35AWGt7X8AIK22~-XWXqjRHIU87fltecVFFgq5mixedRIF0F~ntvGjhNQ7vP3mNeTUtJVOOjsHOBDwOGttOLht2HQ4huCOkxsVvoyeRyO6rKr9MNkwljB6TqPmr84L4BPmg4KO3uHghsbloHY4S4Bji7Do9y7MDKoog2ikTOK1nkGV9Oyu~Oo9CieBj8davIsSCfABxynORtJJ-FovEVBUkwUobvotjkJk5Di2PoUvkj4GBZcLH-5dovdNqXdJNcgLVtl90Gep8lTPiJbfuVQQxPiqrlG1yw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Factors_affecting_in_hospital_heat_related_mortality_a_multi_city_case_crossover_analysis","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":31976424,"first_name":"Massimo","middle_initials":null,"last_name":"Stafoggia","page_name":"MassimoStafoggia","domain_name":"hsph-harvard","created_at":"2015-06-08T00:33:15.635-07:00","display_name":"Massimo 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Unit","url":"https://www.academia.edu/Documents/in/Intensive_Care_Unit"},{"id":289271,"name":"Aged","url":"https://www.academia.edu/Documents/in/Aged"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"},{"id":486757,"name":"Random-Effects Models","url":"https://www.academia.edu/Documents/in/Random-Effects_Models"},{"id":901284,"name":"Hospital Mortality","url":"https://www.academia.edu/Documents/in/Hospital_Mortality"},{"id":1266529,"name":"Pulmonary disease","url":"https://www.academia.edu/Documents/in/Pulmonary_disease"},{"id":1491514,"name":"*Hot Temperature","url":"https://www.academia.edu/Documents/in/_Hot_Temperature"},{"id":1587858,"name":"Confidence Interval","url":"https://www.academia.edu/Documents/in/Confidence_Interval"},{"id":1981316,"name":"Marital Status","url":"https://www.academia.edu/Documents/in/Marital_Status"},{"id":2037489,"name":"Preventive measures","url":"https://www.academia.edu/Documents/in/Preventive_measures"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="12857206"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/12857206/Spie_charts_target_plots_and_radar_plots_for_displaying_comparative_outcomes_of_health_care"><img alt="Research paper thumbnail of Spie charts, target plots, and radar plots for displaying comparative outcomes of health care" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/12857206/Spie_charts_target_plots_and_radar_plots_for_displaying_comparative_outcomes_of_health_care">Spie charts, target plots, and radar plots for displaying comparative outcomes of health care</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://hsph-harvard.academia.edu/MassimoStafoggia">Massimo Stafoggia</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MariangelaDOvidio">Mariangela D'Ovidio</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AdeleLallo">Adele Lallo</a></span></div><div class="wp-workCard_item"><span>Journal of Clinical Epidemiology</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. Furthermore, they are well suited to summarize the overall performance of a health care provider with a single score. &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts represented the best graphical tool for displaying multivariate health care data in comparative evaluations of clinical outcomes and processes of care among health care providers.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="12857206"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12857206"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12857206; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12857206]").text(description); $(".js-view-count[data-work-id=12857206]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12857206; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='12857206']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 12857206, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=12857206]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":12857206,"title":"Spie charts, target plots, and radar plots for displaying comparative outcomes of health care","translated_title":"","metadata":{"abstract":"Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. Furthermore, they are well suited to summarize the overall performance of a health care provider with a single score. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts represented the best graphical tool for displaying multivariate health care data in comparative evaluations of clinical outcomes and processes of care among health care providers.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Journal of Clinical Epidemiology"},"translated_abstract":"Comparative evaluations of clinical outcomes (e.g., in-hospital mortality, complications after a surgical procedure) or health care processes involve the definition of several indicators for each study unit. Graphical displays are best suited for highlighting the main patterns in the data. The aim of this study was to compare different graphical techniques, including target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts, for comparing the performances of different health care providers. Thirteen indicators were calculated and combined in eight composite indices for eight clinical categories of interest. The indices were displayed with target plots, radar plots, and \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts. All the three techniques had an immediate interpretation and were easy to implement. However, target plots failed to highlight small differences between indicators, whereas radar plots were strongly influenced by the order in which the indicators were displayed. Both target and radar plots assumed equal weights for the indicators, and did not allow predetermined judgments on the relative importance of the indicators. \u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Spie\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; charts overcame the primary limitations of the other two techniques. 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