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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Joel Schwartz" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/32440576/10788358/12041750/s200_joel.schwartz.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Joel Schwartz</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://hsph-harvard.academia.edu/">Harvard School of Public Health</a>, <a class="u-tcGrayDarker" href="https://hsph-harvard.academia.edu/Departments/Environmental_Health/Documents">Environmental Health</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" 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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 Joel Schwartz</h3></div><div class="js-work-strip profile--work_container" data-work-id="112645877"><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/112645877/Salivary_microbiome_with_gastroesophageal_reflux_disease_and_treatment"><img alt="Research paper thumbnail of Salivary microbiome with gastroesophageal reflux disease and treatment" class="work-thumbnail" src="https://attachments.academia-assets.com/109811815/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/112645877/Salivary_microbiome_with_gastroesophageal_reflux_disease_and_treatment">Salivary microbiome with gastroesophageal reflux disease and treatment</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, Jan 8, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b3571af907ffface312df5d1edaef8d5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811815,"asset_id":112645877,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811815/download_file?st=MTczMjQ2NTY1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa 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Veterans Affairs Normative Aging Study" class="work-thumbnail" src="https://attachments.academia-assets.com/109811836/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/112645873/Prospective_Study_of_Lead_Exposure_and_Electrocardiographic_Conduction_Disturbances_in_the_Department_of_Veterans_Affairs_Normative_Aging_Study">Prospective Study of Lead Exposure and Electrocardiographic Conduction Disturbances in the Department of Veterans Affairs Normative Aging Study</a></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="79253475424e98547cf83a202f85fa40" class="wp-workCard--action" rel="nofollow" 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Exposure","url":"https://www.academia.edu/Documents/in/Lead_Exposure"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1"},{"id":1345381,"name":"Department of Veterans Affairs","url":"https://www.academia.edu/Documents/in/Department_of_Veterans_Affairs"}],"urls":[{"id":37984185,"url":"https://doi.org/10.1097/01.ede.0000392221.36015.ad"}]}, 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="112645872"><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/112645872/Air_Pollution_and_Cardiovascular_Health_Among_Subjects_with_Diabetes_in_Greater_Boston"><img alt="Research paper thumbnail of Air Pollution and Cardiovascular Health Among Subjects with Diabetes in Greater Boston" 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/112645872/Air_Pollution_and_Cardiovascular_Health_Among_Subjects_with_Diabetes_in_Greater_Boston">Air Pollution and Cardiovascular Health Among Subjects with Diabetes in Greater Boston</a></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, Sep 1, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studie...</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">ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studies of airborne particles and mortality and hospital admission have shown that people with diabetes are a vulnerable group, especially with regard to cardiovascular outcomes. Research examining whether intermediate markers of cardiovascular risk are associated with air pollution exposure in diabetic populations is necessary to explore possible mechanisms. We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. Linear regression analysis was performed for each of the clinical outcomes, including as predictors the pollutant index of interest and the subject&amp;#39;s age, race, gender, smoking history, and body mass index (BMI). We express results as percent change in the clinical outcome per interquartile range (IQR) increase in the pollutant, which were, for lag 0 values: PM2.5: 7.6 ug/m3, BC: 0.61 ug/m3, PN:20,993 #/cc, SO4: 2.1 ug/m3. Increases in pollution exposure were associated with increased levels of three of the inflammatory markers (CRP, ICAM, and VCAM). CRP increases were associated with BC lag 0 (52% increase in CRP per IQR of BC, 95% confidence interval (CI): (0.3%, 130%)). CRP was also marginally associated with PM2.5 3 day average (45% increase per IQR, 95% CI: -7%, 127%). PN and SO4 were significantly associated with increased ICAM, and SO4 with increased levels of VCAM. The associations with inflammatory blood markers are consistent with previous studies showing that air pollution exposure can increase inflammation and potentially change risk profiles for cardiac events or disease progression. Results with other outcomes were less consistent and suggest the need for further testing of model specifications. The opportunity to examine air pollution associations with risk factors for cardiovascular events in a population of people with diabetes may contribute to understanding of mechanisms for previously observed epidemiological associations.</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="112645872"><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="112645872"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645872; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645872]").text(description); $(".js-view-count[data-work-id=112645872]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645872; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645872']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645872, 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" } } $('.js-work-strip[data-work-id=112645872]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645872,"title":"Air Pollution and Cardiovascular Health Among Subjects with Diabetes in Greater Boston","translated_title":"","metadata":{"abstract":"ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studies of airborne particles and mortality and hospital admission have shown that people with diabetes are a vulnerable group, especially with regard to cardiovascular outcomes. Research examining whether intermediate markers of cardiovascular risk are associated with air pollution exposure in diabetic populations is necessary to explore possible mechanisms. We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. Linear regression analysis was performed for each of the clinical outcomes, including as predictors the pollutant index of interest and the subject\u0026amp;#39;s age, race, gender, smoking history, and body mass index (BMI). We express results as percent change in the clinical outcome per interquartile range (IQR) increase in the pollutant, which were, for lag 0 values: PM2.5: 7.6 ug/m3, BC: 0.61 ug/m3, PN:20,993 #/cc, SO4: 2.1 ug/m3. Increases in pollution exposure were associated with increased levels of three of the inflammatory markers (CRP, ICAM, and VCAM). CRP increases were associated with BC lag 0 (52% increase in CRP per IQR of BC, 95% confidence interval (CI): (0.3%, 130%)). CRP was also marginally associated with PM2.5 3 day average (45% increase per IQR, 95% CI: -7%, 127%). PN and SO4 were significantly associated with increased ICAM, and SO4 with increased levels of VCAM. The associations with inflammatory blood markers are consistent with previous studies showing that air pollution exposure can increase inflammation and potentially change risk profiles for cardiac events or disease progression. Results with other outcomes were less consistent and suggest the need for further testing of model specifications. The opportunity to examine air pollution associations with risk factors for cardiovascular events in a population of people with diabetes may contribute to understanding of mechanisms for previously observed epidemiological associations.","publisher":"Lippincott Williams \u0026 Wilkins","publication_date":{"day":1,"month":9,"year":2003,"errors":{}},"publication_name":"Epidemiology"},"translated_abstract":"ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studies of airborne particles and mortality and hospital admission have shown that people with diabetes are a vulnerable group, especially with regard to cardiovascular outcomes. Research examining whether intermediate markers of cardiovascular risk are associated with air pollution exposure in diabetic populations is necessary to explore possible mechanisms. We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. Linear regression analysis was performed for each of the clinical outcomes, including as predictors the pollutant index of interest and the subject\u0026amp;#39;s age, race, gender, smoking history, and body mass index (BMI). We express results as percent change in the clinical outcome per interquartile range (IQR) increase in the pollutant, which were, for lag 0 values: PM2.5: 7.6 ug/m3, BC: 0.61 ug/m3, PN:20,993 #/cc, SO4: 2.1 ug/m3. Increases in pollution exposure were associated with increased levels of three of the inflammatory markers (CRP, ICAM, and VCAM). CRP increases were associated with BC lag 0 (52% increase in CRP per IQR of BC, 95% confidence interval (CI): (0.3%, 130%)). CRP was also marginally associated with PM2.5 3 day average (45% increase per IQR, 95% CI: -7%, 127%). PN and SO4 were significantly associated with increased ICAM, and SO4 with increased levels of VCAM. 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While us...</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">Air pollution and social characteristics have been shown to affect indicators of health. While use of spatial methods to estimate exposure to air pollution has increased the power to detect effects, questions have been raised about potential for confounding by social factors. A study of singleton births in Eastern Massachusetts was conducted between 1996 and 2002 to examine the association between indicators of traffic, land use, individual and area-based socioeconomic measures (SEM), and birth outcomes (birth weight, small for gestational age and preterm births), in a two-level hierarchical model. We found effects of both individual (education, race, prenatal care index) and area-based (median household income) SEM with all birth outcomes. The associations for traffic and land use variables were mainly seen with birth weight, with an exception for an effect of cumulative traffic density on small for gestational age. Race/ethnicity of mother was an important predictor of birth outco...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="567bdc4a25d6f0d6c1275b153de10ae7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811838,"asset_id":112645867,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811838/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645867"><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="112645867"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645867; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645867]").text(description); $(".js-view-count[data-work-id=112645867]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645867; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645867']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645867, 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: "567bdc4a25d6f0d6c1275b153de10ae7" } } $('.js-work-strip[data-work-id=112645867]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645867,"title":"The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in Eastern Massachusetts","translated_title":"","metadata":{"abstract":"Air pollution and social characteristics have been shown to affect indicators of health. 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$a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112645866"><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/112645866/Long_Term_Exposure_to_Traffic_Emissions_and_Fine_Particulate_Matter_and_Lung_Function_Decline_in_the_Framingham_Heart_Study"><img alt="Research paper thumbnail of Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study" class="work-thumbnail" src="https://attachments.academia-assets.com/109811831/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/112645866/Long_Term_Exposure_to_Traffic_Emissions_and_Fine_Particulate_Matter_and_Lung_Function_Decline_in_the_Framingham_Heart_Study">Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study</a></div><div class="wp-workCard_item"><span>American journal of respiratory and critical care medicine</span><span>, Jan 15, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rationale: Few studies have examined associations between long-term exposure to fine particulate ...</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: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. Objectives: To determine if exposure to traffic and PM2.5 are associated with longitudinal changes in lung function in a population-based cohort in the Northeastern U.S., where pollution levels are relatively low. Methods: FEV1 and FVC were measured up to 2 times between 1995-2011 among 6,339 participants of the Framingham Offspring or Third Generation studies. We tested associations between residential proximity to a major roadway and PM2.5 exposure in 2001 (estimated by a land-use model using satellite measurements of aerosol optical thickness) and lung function. We examined differences in average lung function using mixed effects models and differences in lung function decline using linear regression models. Current smokers were excluded. Models were adjusted for age, sex, height, weight, pack-years, socioeconomic status indicators, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="55e4abf5509cfc1d19d284636405286e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811831,"asset_id":112645866,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811831/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645866"><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="112645866"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645866; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645866]").text(description); $(".js-view-count[data-work-id=112645866]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645866; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645866']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645866, 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: "55e4abf5509cfc1d19d284636405286e" } } $('.js-work-strip[data-work-id=112645866]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645866,"title":"Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study","translated_title":"","metadata":{"abstract":"Rationale: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. 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Models were adjusted for age, sex, height, weight, pack-years, socioeconomic status indicators, ...","publication_date":{"day":15,"month":1,"year":2015,"errors":{}},"publication_name":"American journal of respiratory and critical care medicine"},"translated_abstract":"Rationale: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. Objectives: To determine if exposure to traffic and PM2.5 are associated with longitudinal changes in lung function in a population-based cohort in the Northeastern U.S., where pollution levels are relatively low. Methods: FEV1 and FVC were measured up to 2 times between 1995-2011 among 6,339 participants of the Framingham Offspring or Third Generation studies. We tested associations between residential proximity to a major roadway and PM2.5 exposure in 2001 (estimated by a land-use model using satellite measurements of aerosol optical thickness) and lung function. We examined differences in average lung function using mixed effects models and differences in lung function decline using linear regression models. Current smokers were excluded. 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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="112645865"><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/112645865/A_time_series_study_of_drug_sales_and_turbidity_of_tap_water_in_Le_Havre_France"><img alt="Research paper thumbnail of A time series study of drug sales and turbidity of tap water in Le Havre, France" class="work-thumbnail" src="https://attachments.academia-assets.com/109811814/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/112645865/A_time_series_study_of_drug_sales_and_turbidity_of_tap_water_in_Le_Havre_France">A time series study of drug sales and turbidity of tap water in Le Havre, France</a></div><div class="wp-workCard_item"><span>Journal of Water and Health</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic s...</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">The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic springs, Radicatel and Saint-Laurent. Until 2000, the Radicatel water was settled when turbidity exceeded 3 NTU, then filtered and chlorinated, whereas the Saint-Laurent water was simply chlorinated. Our study aimed to characterize the link between water turbidity and the incidence of acute gastroenteritis (AGE). Records on drug sales used for the treatment of AGE were collected from January 1994 to June 1996 (period 1) and from March 1997 to July 2000 (period 2). Daily counts of drug sales were modeled using a Poisson Regression. We used data set 2 as a discovery set, identifying relevant (i.e. both significant and plausible) exposure covariates and lags. We then tested this model on period 1 as a replication dataset. In period 2, the daily drug sales correlated with finished water turbidity at both resources. Settling substantially modified the risk related to turbidity of both raw and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e4369bf124314c90f0f3dd101ea51ed7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811814,"asset_id":112645865,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811814/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645865"><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="112645865"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645865; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645865]").text(description); $(".js-view-count[data-work-id=112645865]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645865; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645865']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645865, 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: "e4369bf124314c90f0f3dd101ea51ed7" } } $('.js-work-strip[data-work-id=112645865]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645865,"title":"A time series study of drug sales and turbidity of tap water in Le Havre, France","translated_title":"","metadata":{"abstract":"The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic springs, Radicatel and Saint-Laurent. 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Effects of Cold Weather on Cardiovascular Mortality" 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/112645862/Influenza_Epidemics_Seasonality_and_the_Effects_of_Cold_Weather_on_Cardiovascular_Mortality">Influenza Epidemics, Seasonality, and the Effects of Cold Weather on Cardiovascular Mortality</a></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Epidemiological studies have shown that extremes in ambient temperature are associated with short...</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">Epidemiological studies have shown that extremes in ambient temperature are associated with short term increases in mortality. To control for seasonality, most previous time series studies used non-parametric functions of time. We conducted a US multi-city study evaluating whether adjustment for influenza epidemics changes the exposure-response function of temperature, and whether controlling for the remaining seasonal pattern could be modeled more simply. Counts of daily cardiovascular deaths and of emergency hospital admissions of the elderly for pneumonia during 1992-2000 were obtained for 48 cities. Applying city-specific Quasi-Poisson regression models we estimated the association between daily cardiovascular mortality and temperature. Models included day-of-the-week indicators and regression splines of temperature, relative humidity, barometric pressure. In the base model a regression spline of date with five degrees of freedom (df) per year captured trend and seasonality. In the alternative model a regression spline of pneumonia admissions, a sinusoidal function of time, and a regression spline of date with ten df were included instead. Temperatures were lower in the north-east compared to the south-west and the range differed greatly. The alternative model fit the data better than the base model based on GSV-scores, capturing well the regular seasonal pattern as well as the irregular pattern of the outcome. The temperature-response function was mostly U- or J-shaped and not greatly affected by adjusting for influenza. The pooled estimated increase in risk for a temperature decrease from 0 to -5&amp;deg;C was 1.6% (95% confidence interval (CI) 0.9-2.4%) in the base and 1.9% (95% CI 1.2-2.6%) in the alternative model. This study on the effect of temperature on mortality shows that including epidemic data explained most of the irregular seasonal pattern, allowing more parsimonious models than when adjusting for seasonality only with smooth functions of time. The effect of cold temperature is not confounded by epidemics</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="112645862"><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="112645862"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645862; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645862]").text(description); $(".js-view-count[data-work-id=112645862]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645862; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645862']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645862, 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" } } $('.js-work-strip[data-work-id=112645862]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645862,"title":"Influenza Epidemics, Seasonality, and the Effects of Cold Weather on Cardiovascular Mortality","translated_title":"","metadata":{"abstract":"Epidemiological studies have shown that extremes in ambient temperature are associated with short term increases in mortality. 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Counts of daily cardiovascular deaths and of emergency hospital admissions of the elderly for pneumonia during 1992-2000 were obtained for 48 cities. Applying city-specific Quasi-Poisson regression models we estimated the association between daily cardiovascular mortality and temperature. Models included day-of-the-week indicators and regression splines of temperature, relative humidity, barometric pressure. In the base model a regression spline of date with five degrees of freedom (df) per year captured trend and seasonality. In the alternative model a regression spline of pneumonia admissions, a sinusoidal function of time, and a regression spline of date with ten df were included instead. Temperatures were lower in the north-east compared to the south-west and the range differed greatly. The alternative model fit the data better than the base model based on GSV-scores, capturing well the regular seasonal pattern as well as the irregular pattern of the outcome. 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This article is available as HTML full text and PDF.</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="112645861"><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="112645861"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645861; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645861]").text(description); $(".js-view-count[data-work-id=112645861]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645861; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645861']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645861, 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" } } $('.js-work-strip[data-work-id=112645861]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645861,"title":"Exposure to Black Carbon and Cognitive Function in a Cohort of Older Men","translated_title":"","metadata":{"abstract":"ABSTRACT An abstract is unavailable. 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</script> <div class="js-work-strip profile--work_container" data-work-id="112645860"><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/112645860/Estimating_the_Independent_Effects_of_Multiple_Pollutants_in_the_Presence_of_Measurement_Error_An_Application_of_a_Measurement_Error_Resistant_Technique"><img alt="Research paper thumbnail of Estimating the Independent Effects of Multiple Pollutants in the Presence of Measurement Error: An Application of a Measurement-Error–Resistant Technique" class="work-thumbnail" src="https://attachments.academia-assets.com/109811840/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" 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Cardiovascular Health Among Subjects with Diabetes in Greater Boston" 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/112645872/Air_Pollution_and_Cardiovascular_Health_Among_Subjects_with_Diabetes_in_Greater_Boston">Air Pollution and Cardiovascular Health Among Subjects with Diabetes in Greater Boston</a></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, Sep 1, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studie...</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">ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studies of airborne particles and mortality and hospital admission have shown that people with diabetes are a vulnerable group, especially with regard to cardiovascular outcomes. Research examining whether intermediate markers of cardiovascular risk are associated with air pollution exposure in diabetic populations is necessary to explore possible mechanisms. We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. Linear regression analysis was performed for each of the clinical outcomes, including as predictors the pollutant index of interest and the subject&amp;#39;s age, race, gender, smoking history, and body mass index (BMI). We express results as percent change in the clinical outcome per interquartile range (IQR) increase in the pollutant, which were, for lag 0 values: PM2.5: 7.6 ug/m3, BC: 0.61 ug/m3, PN:20,993 #/cc, SO4: 2.1 ug/m3. Increases in pollution exposure were associated with increased levels of three of the inflammatory markers (CRP, ICAM, and VCAM). CRP increases were associated with BC lag 0 (52% increase in CRP per IQR of BC, 95% confidence interval (CI): (0.3%, 130%)). CRP was also marginally associated with PM2.5 3 day average (45% increase per IQR, 95% CI: -7%, 127%). PN and SO4 were significantly associated with increased ICAM, and SO4 with increased levels of VCAM. The associations with inflammatory blood markers are consistent with previous studies showing that air pollution exposure can increase inflammation and potentially change risk profiles for cardiac events or disease progression. Results with other outcomes were less consistent and suggest the need for further testing of model specifications. The opportunity to examine air pollution associations with risk factors for cardiovascular events in a population of people with diabetes may contribute to understanding of mechanisms for previously observed epidemiological associations.</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="112645872"><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="112645872"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645872; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645872]").text(description); $(".js-view-count[data-work-id=112645872]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645872; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645872']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645872, 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" } } $('.js-work-strip[data-work-id=112645872]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645872,"title":"Air Pollution and Cardiovascular Health Among Subjects with Diabetes in Greater Boston","translated_title":"","metadata":{"abstract":"ABSTRACT Funding: 5 T32 ES07069-22, NIEHS ES00002, EPAR827353, 5P01ES09825 Epidemiological studies of airborne particles and mortality and hospital admission have shown that people with diabetes are a vulnerable group, especially with regard to cardiovascular outcomes. Research examining whether intermediate markers of cardiovascular risk are associated with air pollution exposure in diabetic populations is necessary to explore possible mechanisms. We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. Linear regression analysis was performed for each of the clinical outcomes, including as predictors the pollutant index of interest and the subject\u0026amp;#39;s age, race, gender, smoking history, and body mass index (BMI). We express results as percent change in the clinical outcome per interquartile range (IQR) increase in the pollutant, which were, for lag 0 values: PM2.5: 7.6 ug/m3, BC: 0.61 ug/m3, PN:20,993 #/cc, SO4: 2.1 ug/m3. Increases in pollution exposure were associated with increased levels of three of the inflammatory markers (CRP, ICAM, and VCAM). CRP increases were associated with BC lag 0 (52% increase in CRP per IQR of BC, 95% confidence interval (CI): (0.3%, 130%)). CRP was also marginally associated with PM2.5 3 day average (45% increase per IQR, 95% CI: -7%, 127%). PN and SO4 were significantly associated with increased ICAM, and SO4 with increased levels of VCAM. The associations with inflammatory blood markers are consistent with previous studies showing that air pollution exposure can increase inflammation and potentially change risk profiles for cardiac events or disease progression. Results with other outcomes were less consistent and suggest the need for further testing of model specifications. 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We evaluated associations between air pollution and various physiological measurements of cardiovascular health taken at baseline visits from 342 Boston-area residents participating in clinical trials. Air pollutants (PM2.5, particle number (PN), balck carbon (BC), and sulfates (SO4) were measured at a facility approximately 1 km from where patients were seen. Pollutant concentrations for the day of examination, one day previous, and moving averages from 3 and 5 days previous were evaluated for associations with a variety of clinical and laboratory parameters. These included: inflammatory markers in the blood (C reactive protein (CRP), TNF-alpha, ICAM, VCAM, endothelin-1), blood pressure, liver function tests, serum lipids, platelets, hemoglobin and HbA 1c, an indicator of longer-term glycemic control. These variables were log-transformed where necessary to achieve normal distributions. 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While us...</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">Air pollution and social characteristics have been shown to affect indicators of health. While use of spatial methods to estimate exposure to air pollution has increased the power to detect effects, questions have been raised about potential for confounding by social factors. A study of singleton births in Eastern Massachusetts was conducted between 1996 and 2002 to examine the association between indicators of traffic, land use, individual and area-based socioeconomic measures (SEM), and birth outcomes (birth weight, small for gestational age and preterm births), in a two-level hierarchical model. We found effects of both individual (education, race, prenatal care index) and area-based (median household income) SEM with all birth outcomes. The associations for traffic and land use variables were mainly seen with birth weight, with an exception for an effect of cumulative traffic density on small for gestational age. Race/ethnicity of mother was an important predictor of birth outco...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="567bdc4a25d6f0d6c1275b153de10ae7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811838,"asset_id":112645867,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811838/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645867"><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="112645867"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645867; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645867]").text(description); $(".js-view-count[data-work-id=112645867]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645867; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645867']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645867, 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: "567bdc4a25d6f0d6c1275b153de10ae7" } } $('.js-work-strip[data-work-id=112645867]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645867,"title":"The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in Eastern Massachusetts","translated_title":"","metadata":{"abstract":"Air pollution and social characteristics have been shown to affect indicators of health. 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$a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112645866"><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/112645866/Long_Term_Exposure_to_Traffic_Emissions_and_Fine_Particulate_Matter_and_Lung_Function_Decline_in_the_Framingham_Heart_Study"><img alt="Research paper thumbnail of Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study" class="work-thumbnail" src="https://attachments.academia-assets.com/109811831/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/112645866/Long_Term_Exposure_to_Traffic_Emissions_and_Fine_Particulate_Matter_and_Lung_Function_Decline_in_the_Framingham_Heart_Study">Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study</a></div><div class="wp-workCard_item"><span>American journal of respiratory and critical care medicine</span><span>, Jan 15, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Rationale: Few studies have examined associations between long-term exposure to fine particulate ...</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: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. Objectives: To determine if exposure to traffic and PM2.5 are associated with longitudinal changes in lung function in a population-based cohort in the Northeastern U.S., where pollution levels are relatively low. Methods: FEV1 and FVC were measured up to 2 times between 1995-2011 among 6,339 participants of the Framingham Offspring or Third Generation studies. We tested associations between residential proximity to a major roadway and PM2.5 exposure in 2001 (estimated by a land-use model using satellite measurements of aerosol optical thickness) and lung function. We examined differences in average lung function using mixed effects models and differences in lung function decline using linear regression models. Current smokers were excluded. Models were adjusted for age, sex, height, weight, pack-years, socioeconomic status indicators, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="55e4abf5509cfc1d19d284636405286e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811831,"asset_id":112645866,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811831/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645866"><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="112645866"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645866; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645866]").text(description); $(".js-view-count[data-work-id=112645866]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645866; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645866']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645866, 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: "55e4abf5509cfc1d19d284636405286e" } } $('.js-work-strip[data-work-id=112645866]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645866,"title":"Long-Term Exposure to Traffic Emissions and Fine Particulate Matter and Lung Function Decline in the Framingham Heart Study","translated_title":"","metadata":{"abstract":"Rationale: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. 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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="112645865"><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/112645865/A_time_series_study_of_drug_sales_and_turbidity_of_tap_water_in_Le_Havre_France"><img alt="Research paper thumbnail of A time series study of drug sales and turbidity of tap water in Le Havre, France" class="work-thumbnail" src="https://attachments.academia-assets.com/109811814/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/112645865/A_time_series_study_of_drug_sales_and_turbidity_of_tap_water_in_Le_Havre_France">A time series study of drug sales and turbidity of tap water in Le Havre, France</a></div><div class="wp-workCard_item"><span>Journal of Water and Health</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic s...</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">The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic springs, Radicatel and Saint-Laurent. Until 2000, the Radicatel water was settled when turbidity exceeded 3 NTU, then filtered and chlorinated, whereas the Saint-Laurent water was simply chlorinated. Our study aimed to characterize the link between water turbidity and the incidence of acute gastroenteritis (AGE). Records on drug sales used for the treatment of AGE were collected from January 1994 to June 1996 (period 1) and from March 1997 to July 2000 (period 2). Daily counts of drug sales were modeled using a Poisson Regression. We used data set 2 as a discovery set, identifying relevant (i.e. both significant and plausible) exposure covariates and lags. We then tested this model on period 1 as a replication dataset. In period 2, the daily drug sales correlated with finished water turbidity at both resources. Settling substantially modified the risk related to turbidity of both raw and ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e4369bf124314c90f0f3dd101ea51ed7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109811814,"asset_id":112645865,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109811814/download_file?st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NTY1OSw4LjIyMi4yMDguMTQ2&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="112645865"><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="112645865"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645865; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645865]").text(description); $(".js-view-count[data-work-id=112645865]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645865; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645865']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645865, 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: "e4369bf124314c90f0f3dd101ea51ed7" } } $('.js-work-strip[data-work-id=112645865]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645865,"title":"A time series study of drug sales and turbidity of tap water in Le Havre, France","translated_title":"","metadata":{"abstract":"The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic springs, Radicatel and Saint-Laurent. Until 2000, the Radicatel water was settled when turbidity exceeded 3 NTU, then filtered and chlorinated, whereas the Saint-Laurent water was simply chlorinated. Our study aimed to characterize the link between water turbidity and the incidence of acute gastroenteritis (AGE). Records on drug sales used for the treatment of AGE were collected from January 1994 to June 1996 (period 1) and from March 1997 to July 2000 (period 2). Daily counts of drug sales were modeled using a Poisson Regression. We used data set 2 as a discovery set, identifying relevant (i.e. both significant and plausible) exposure covariates and lags. We then tested this model on period 1 as a replication dataset. In period 2, the daily drug sales correlated with finished water turbidity at both resources. Settling substantially modified the risk related to turbidity of both raw and ...","publisher":"IWA Publishing","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"Journal of Water and Health"},"translated_abstract":"The 80,000 inhabitants of the lower part of Le Havre obtain their water supply from two karstic springs, Radicatel and Saint-Laurent. Until 2000, the Radicatel water was settled when turbidity exceeded 3 NTU, then filtered and chlorinated, whereas the Saint-Laurent water was simply chlorinated. Our study aimed to characterize the link between water turbidity and the incidence of acute gastroenteritis (AGE). Records on drug sales used for the treatment of AGE were collected from January 1994 to June 1996 (period 1) and from March 1997 to July 2000 (period 2). Daily counts of drug sales were modeled using a Poisson Regression. 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Effects of Cold Weather on Cardiovascular Mortality" 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/112645862/Influenza_Epidemics_Seasonality_and_the_Effects_of_Cold_Weather_on_Cardiovascular_Mortality">Influenza Epidemics, Seasonality, and the Effects of Cold Weather on Cardiovascular Mortality</a></div><div class="wp-workCard_item"><span>Epidemiology</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Epidemiological studies have shown that extremes in ambient temperature are associated with short...</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">Epidemiological studies have shown that extremes in ambient temperature are associated with short term increases in mortality. To control for seasonality, most previous time series studies used non-parametric functions of time. We conducted a US multi-city study evaluating whether adjustment for influenza epidemics changes the exposure-response function of temperature, and whether controlling for the remaining seasonal pattern could be modeled more simply. Counts of daily cardiovascular deaths and of emergency hospital admissions of the elderly for pneumonia during 1992-2000 were obtained for 48 cities. Applying city-specific Quasi-Poisson regression models we estimated the association between daily cardiovascular mortality and temperature. Models included day-of-the-week indicators and regression splines of temperature, relative humidity, barometric pressure. In the base model a regression spline of date with five degrees of freedom (df) per year captured trend and seasonality. In the alternative model a regression spline of pneumonia admissions, a sinusoidal function of time, and a regression spline of date with ten df were included instead. Temperatures were lower in the north-east compared to the south-west and the range differed greatly. The alternative model fit the data better than the base model based on GSV-scores, capturing well the regular seasonal pattern as well as the irregular pattern of the outcome. The temperature-response function was mostly U- or J-shaped and not greatly affected by adjusting for influenza. The pooled estimated increase in risk for a temperature decrease from 0 to -5&amp;deg;C was 1.6% (95% confidence interval (CI) 0.9-2.4%) in the base and 1.9% (95% CI 1.2-2.6%) in the alternative model. This study on the effect of temperature on mortality shows that including epidemic data explained most of the irregular seasonal pattern, allowing more parsimonious models than when adjusting for seasonality only with smooth functions of time. The effect of cold temperature is not confounded by epidemics</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="112645862"><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="112645862"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112645862; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112645862]").text(description); $(".js-view-count[data-work-id=112645862]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112645862; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112645862']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 112645862, 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" } } $('.js-work-strip[data-work-id=112645862]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112645862,"title":"Influenza Epidemics, Seasonality, and the Effects of Cold Weather on Cardiovascular Mortality","translated_title":"","metadata":{"abstract":"Epidemiological studies have shown that extremes in ambient temperature are associated with short term increases in mortality. 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In the alternative model a regression spline of pneumonia admissions, a sinusoidal function of time, and a regression spline of date with ten df were included instead. Temperatures were lower in the north-east compared to the south-west and the range differed greatly. The alternative model fit the data better than the base model based on GSV-scores, capturing well the regular seasonal pattern as well as the irregular pattern of the outcome. The temperature-response function was mostly U- or J-shaped and not greatly affected by adjusting for influenza. The pooled estimated increase in risk for a temperature decrease from 0 to -5\u0026amp;deg;C was 1.6% (95% confidence interval (CI) 0.9-2.4%) in the base and 1.9% (95% CI 1.2-2.6%) in the alternative model. This study on the effect of temperature on mortality shows that including epidemic data explained most of the irregular seasonal pattern, allowing more parsimonious models than when adjusting for seasonality only with smooth functions of time. The effect of cold temperature is not confounded by epidemics","publisher":"Ovid Technologies (Wolters Kluwer Health)","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Epidemiology"},"translated_abstract":"Epidemiological studies have shown that extremes in ambient temperature are associated with short term increases in mortality. To control for seasonality, most previous time series studies used non-parametric functions of time. We conducted a US multi-city study evaluating whether adjustment for influenza epidemics changes the exposure-response function of temperature, and whether controlling for the remaining seasonal pattern could be modeled more simply. Counts of daily cardiovascular deaths and of emergency hospital admissions of the elderly for pneumonia during 1992-2000 were obtained for 48 cities. Applying city-specific Quasi-Poisson regression models we estimated the association between daily cardiovascular mortality and temperature. Models included day-of-the-week indicators and regression splines of temperature, relative humidity, barometric pressure. In the base model a regression spline of date with five degrees of freedom (df) per year captured trend and seasonality. In the alternative model a regression spline of pneumonia admissions, a sinusoidal function of time, and a regression spline of date with ten df were included instead. Temperatures were lower in the north-east compared to the south-west and the range differed greatly. The alternative model fit the data better than the base model based on GSV-scores, capturing well the regular seasonal pattern as well as the irregular pattern of the outcome. 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