{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T02:58:29Z","timestamp":1778295509948,"version":"3.51.4"},"reference-count":82,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2018,3,1]],"date-time":"2018-03-01T00:00:00Z","timestamp":1519862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Rationale<\/jats:title>\n                    <jats:p>Short- and long-term fine particulate matter (particulate matter \u22642.5 \u03bcm in aerodynamic diameter [PM2.5]) pollution is associated with asthma development and morbidity, but there are few data on the effects of long-term exposure to coarse PM (PM10\u20132.5) on respiratory health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To understand the relationship between long-term fine and coarse PM exposure and asthma prevalence and morbidity among children.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A semiparametric regression model that incorporated PM2.5 and PM10 monitor data and geographic characteristics was developed to predict 2-year average PM2.5 and PM10\u20132.5 exposure during the period 2009 to 2010 at the zip-code tabulation area level. Data from 7,810,025 children aged 5 to 20 years enrolled in Medicaid from 2009 to 2010 were used in a log-linear regression model with predicted PM levels to estimate the association between PM exposure and asthma prevalence and morbidity, adjusting for race\/ethnicity, sex, age, area-level urbanicity, poverty, education, and unmeasured spatial confounding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements and Main Results<\/jats:title>\n                    <jats:p>Exposure to coarse PM was associated with increased asthma diagnosis prevalence (rate ratio [RR] for 1-\u03bcg\/m3 increase in coarse PM level, 1.006; 95% confidence interval [CI], 1.001\u20131.011), hospitalizations (RR, 1.023; 95% CI, 1.003\u20131.042), and emergency department visits (RR, 1.017; 95% CI, 1.001\u20131.033) when adjusting for fine PM. Fine PM exposure was more strongly associated with increased asthma prevalence and morbidity than coarse PM. The estimates remained elevated across different levels of spatial confounding adjustment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Among children enrolled in Medicaid, exposure to higher average coarse PM levels is associated with increased asthma prevalence and morbidity. These results suggest the need for direct monitoring of coarse PM and reconsideration of limits on long-term average coarse PM pollution levels.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1164\/rccm.201706-1267oc","type":"journal-article","created":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T10:16:37Z","timestamp":1513332997000},"page":"737-746","source":"Crossref","is-referenced-by-count":100,"title":["Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid"],"prefix":"10.1093","volume":"197","author":[{"given":"Corinne A.","family":"Keet","sequence":"first","affiliation":[{"name":"Division of Pediatric Allergy and Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua P.","family":"Keller","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, 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