Spatial Regression Models for Large-Cohort Studies Linking Community Air Pollution and Health

Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of Toxicology and Environmental Health, Part A Ročník 66; číslo 19; s. 1811 - 1824
Hlavní autoři:	Cakmak, Sabit, Burnett, Richard, Jerrett, Michael, Goldberg, Mark, Pope, C. Arden, Ma, Renjun, Gultekin, Timur, Thun, Michael, Krewski, Daniel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Informa UK Ltd 22.08.2003 Taylor and Francis
Témata:	Air Air Pollution - adverse effects Air. Soil. Water. Waste. Feeding Algorithms American Cancer Society Biological and medical sciences Cohort Studies Environment. Living conditions Environmental pollutants toxicology Geography Health Humans Linear Models Medical sciences Models, Statistical Public health. Hygiene Public health. Hygiene-occupational medicine Regression Analysis Risk Factors Sulfates - adverse effects Sulfates - analysis Toxicology Human Statistical analysis Toxicity Spatial analysis Cardiovascular disease Regression analysis Sulfates Epidemiology Suspended particle Health and environment Correlation analysis Heart disease Cohort study Air pollution Public health
ISSN:	1528-7394, 1087-2620
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.
AbstractList	Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component. Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.
Author	Ma, Renjun Thun, Michael Goldberg, Mark Jerrett, Michael Burnett, Richard Gultekin, Timur Krewski, Daniel Cakmak, Sabit Pope, C. Arden
Author_xml	– sequence: 1 givenname: Sabit surname: Cakmak fullname: Cakmak, Sabit – sequence: 2 givenname: Richard surname: Burnett fullname: Burnett, Richard – sequence: 3 givenname: Michael surname: Jerrett fullname: Jerrett, Michael – sequence: 4 givenname: Mark surname: Goldberg fullname: Goldberg, Mark – sequence: 5 givenname: C. Arden surname: Pope fullname: Pope, C. Arden – sequence: 6 givenname: Renjun surname: Ma fullname: Ma, Renjun – sequence: 7 givenname: Timur surname: Gultekin fullname: Gultekin, Timur – sequence: 8 givenname: Michael surname: Thun fullname: Thun, Michael – sequence: 9 givenname: Daniel surname: Krewski fullname: Krewski, Daniel
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15164322$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/12959845$$D View this record in MEDLINE/PubMed
BookMark	eNqFkU1vFCEYgImpsR969Wi46G0q3zMcm021Jms0Vo-GMMBsUQZWYKL772XTraYmjScI7_O8H7yn4Cim6AB4jtE5RgN6jTkZeioRRYIx9gictNe-I4Kgo3Zvwa5F2TE4LeUbQggzKZ6AY0wklwPjJ-Dr9VZXrwP85DbZleJThO-TdaHAKWW41nnjulW6SbnC67pY7wpc-_jdxw1cpXleoq87eOEz_JhCWOre19HCK6dDvXkKHk86FPfscJ6BL28uP6-uuvWHt-9WF-vOMCxq1xOOKWKccDOgNoBAo3WTpkL0I0JMOoRHSzWnVpBBcCQHbJ2wxtBRjmSU9Ay8us27zenH4kpVsy_GhaCjS0tRPRWUcD408MUBXMbZWbXNftZ5p-4-pAEvD4AuRocp62h8-ctxLBglpHHsljM5lZLdpIyvej9-zdoHhZHa70fd30_Tzv_R_mR-SDjU8bHtY9Y_Uw5WVb0LKd81d19R9VdtmvyvRh8o-RvbmrKB
CitedBy_id	crossref_primary_10_1007_s00477_007_0142_1 crossref_primary_10_1007_s11356_018_1307_9 crossref_primary_10_1016_j_envpol_2018_01_009 crossref_primary_10_1214_10_STS326 crossref_primary_10_1097_EDE_0b013e31819d9501 crossref_primary_10_1016_j_apr_2020_07_005 crossref_primary_10_1007_s00477_007_0133_2 crossref_primary_10_1186_1756_3305_7_216 crossref_primary_10_1186_1476_072X_13_6 crossref_primary_10_1080_15287390590935941 crossref_primary_10_1093_aje_kwi175 crossref_primary_10_1080_15287390306435 crossref_primary_10_1007_s10661_012_2630_0 crossref_primary_10_1016_j_scitotenv_2025_178999 crossref_primary_10_1016_j_tra_2025_104429 crossref_primary_10_1016_j_jacadv_2023_100334 crossref_primary_10_1080_15287390306438 crossref_primary_10_1080_15287390306447 crossref_primary_10_1080_15287390600884644
Cites_doi	10.1002/env.3170060108 10.1007/978-94-017-3048-8_11 10.1002/sim.4780100703 10.2307/3315060 10.1164/ajrccm/151.3_Pt_1.669 10.2113/gsecongeo.58.8.1246 10.2307/2286407 10.1007/978-1-4615-7826-0
ContentType	Journal Article
Copyright	Copyright Taylor & Francis Group, LLC 2003 2004 INIST-CNRS
Copyright_xml	– notice: Copyright Taylor & Francis Group, LLC 2003 – notice: 2004 INIST-CNRS
DBID	AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1080/15287390306444
DatabaseName	CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Public Health Pharmacy, Therapeutics, & Pharmacology Geography
EISSN	1087-2620
EndPage	1824
ExternalDocumentID	12959845 15164322 10_1080_15287390306444 10069678
Genre	Journal Article
GroupedDBID	.7F .QJ 0BK 0R~ 29L 30N 36B 4.4 53G 5GY 5VS AAENE AAGDL AAGME AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABFIM ABHAV ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACDHJ ACGEJ ACGFS ACGOD ACIWK ACPRK ACTIO ACZPZ ADCVX ADGTB ADOPC ADXPE AEISY AEOZL AFKVX AFRAH AFRVT AGDLA AGMYJ AI. AIJEM AIYEW AJWEG AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AURDB AVBZW AWYRJ BFWEY BLEHA CAG CCCUG CE4 COF CS3 CWRZV DGEBU DKSSO EBS ECGQY EJD E~A E~B F5P GTTXZ H13 HZ~ H~P IPNFZ J.P KYCEM M4Z NA5 NX0 O9- P2P PCLFJ PQQKQ RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TEI TFL TFT TFW TQWBC TTHFI TUROJ TWF UT5 UU3 VH1 YHZ ZGI ZGOLN ZXP ~S~ .GJ 3O- AAAVZ AAYXX ABFMO ACFTK ADXHL CITATION HF~ LJTGL ZCG ZE2 ADYSH IQODW CGR CUY CVF ECM EIF NPM 7X8
ID	FETCH-LOGICAL-c416t-7251304525c8010860bdefa3667b0049e01bd3a53d628650981de6dcc3b9b2b93
IEDL.DBID	TFW
ISICitedReferencesCount	16
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000185743200022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1528-7394
IngestDate	Thu Oct 02 10:01:17 EDT 2025 Wed Feb 19 01:32:31 EST 2025 Mon Jul 21 09:11:24 EDT 2025 Tue Nov 18 21:59:01 EST 2025 Sat Nov 29 06:46:28 EST 2025 Mon Oct 20 23:43:28 EDT 2025 Mon May 13 12:10:38 EDT 2019
IsPeerReviewed	true
IsScholarly	true
Issue	19
Keywords	Human Statistical analysis Toxicity Spatial analysis Cardiovascular disease Regression analysis Sulfates Epidemiology Suspended particle Health and environment Correlation analysis Heart disease Cohort study Air pollution Public health
Language	English
License	CC BY 4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c416t-7251304525c8010860bdefa3667b0049e01bd3a53d628650981de6dcc3b9b2b93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PMID	12959845
PQID	73632558
PQPubID	23479
PageCount	14
ParticipantIDs	pubmed_primary_12959845 proquest_miscellaneous_73632558 crossref_citationtrail_10_1080_15287390306444 pascalfrancis_primary_15164322 crossref_primary_10_1080_15287390306444 informaworld_taylorfrancis_310_1080_15287390306444
PublicationCentury	2000
PublicationDate	2003-08-22
PublicationDateYYYYMMDD	2003-08-22
PublicationDate_xml	– month: 08 year: 2003 text: 2003-08-22 day: 22
PublicationDecade	2000
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Journal of Toxicology and Environmental Health, Part A
PublicationTitleAlternate	J Toxicol Environ Health A
PublicationYear	2003
Publisher	Informa UK Ltd Taylor and Francis
Publisher_xml	– name: Informa UK Ltd – name: Taylor and Francis
References	Krewski D. (CIT0011) 2000 Miron J. (CIT0001) 1984 CIT0012 Pope C. A. III (CIT0002) 1995; 151 Kaluzny S. P. (CIT0010) 1998 Cakmak S. (CIT0006) 1998; 8 CIT0003 CIT0013 CIT0005 CIT0004 CIT0007 CIT0008 Hastie T. (CIT0009) 1990
References_xml	– ident: CIT0005 doi: 10.1002/env.3170060108 – start-page: 201 volume-title: Spatial statistics and models year: 1984 ident: CIT0001 doi: 10.1007/978-94-017-3048-8_11 – ident: CIT0008 doi: 10.1002/sim.4780100703 – ident: CIT0013 – volume: 8 start-page: 129 year: 1998 ident: CIT0006 publication-title: J. Expos. Anal. Environ. Epidemiol. – volume-title: Generalized additive models year: 1990 ident: CIT0009 – volume-title: Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Paniculate Air Pollution and Mortality. Special report year: 2000 ident: CIT0011 – ident: CIT0003 – ident: CIT0004 doi: 10.2307/3315060 – volume: 151 start-page: 669 year: 1995 ident: CIT0002 publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/ajrccm/151.3_Pt_1.669 – ident: CIT0012 doi: 10.2113/gsecongeo.58.8.1246 – ident: CIT0007 doi: 10.2307/2286407 – volume-title: S+ SpatialStats: User's manual for Windows and Unix year: 1998 ident: CIT0010 doi: 10.1007/978-1-4615-7826-0
SSID	ssj0001496 ssj0001687
Score	1.7472932
Snippet	Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as...
SourceID	proquest pubmed pascalfrancis crossref informaworld
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1811
SubjectTerms	Air Air Pollution - adverse effects Air. Soil. Water. Waste. Feeding Algorithms American Cancer Society Biological and medical sciences Cohort Studies Environment. Living conditions Environmental pollutants toxicology Geography Health Humans Linear Models Medical sciences Models, Statistical Public health. Hygiene Public health. Hygiene-occupational medicine Regression Analysis Risk Factors Sulfates - adverse effects Sulfates - analysis Toxicology
Title	Spatial Regression Models for Large-Cohort Studies Linking Community Air Pollution and Health
URI	https://www.tandfonline.com/doi/abs/10.1080/15287390306444 https://www.ncbi.nlm.nih.gov/pubmed/12959845 https://www.proquest.com/docview/73632558
Volume	66
WOSCitedRecordID	wos000185743200022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAWR databaseName: Taylor & Francis Journals Complete customDbUrl: eissn: 1087-2620 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001687 issn: 1528-7394 databaseCode: TFW dateStart: 19980101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3LjtMwFL2CEQskxGN4hUfxAg2bsUjjV7wcjahYjEYVKqIbFNmxAyONUtRkEP17rmN3QkFlActIthM7x_a5yfU5AK-1rKVtGk6l15Jy2whqJFNU586X3HDhp24wm1Dn5-Vyqecpq7JLaZUhhm6iUMSwVofJbWy3zYh7i1tOqTBWD-SZBylQpPQB3IvZp-tFGIm_HC_kYJQX6lGsyLfijX-0tbM57UiXhpxJ0-GwNdHvYj8hHTam2b3_6dJ9uJvoKDmJ-HkAN3x7CEfzqGe9OSaL8XhWd0yOyHxUut4cwp340Y_Es0wP4XMwOEZAkw_-S8yvbUkwW7vsCPaRnIWsc3q6-oqUn6QERnIWzRtIOqnSb8jJxZrMgwNzwAzBLqX2H8HH2bvF6Xua_BtojTSvpwq5U_gRW4ga90GMnXLrfGOYlGrIsPL51DpmBHPD-dhcI3f20tU1s9oWVrPHcNCuWv8UiKmFUUb4XNspF01hZcmc4FbJ2nnlTAZ0--KqOombB4-Ny2qaNFB3RziDN9flv0VZj70l6a84qPrhQ0pCwW9lq_5Hn0Hxl_Js300mO-gan0lgOItrbgavtnCrcP6Hnzqm9aurrlJMMgwLywyeRBSOdQstdMnFs395oudwe0hbzHEdLV7AQb--8i_hVv29v-jWE7ipluVkmHE_AfldJbA
linkProvider	Taylor & Francis
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VgkQlxKO8wqP1AZVLrWbjR2JxqipWRSyrFVpELyiyHQcqVdlqkyL23zOOk4YFLQc4RrKd2BnPw_5mPoBXSlppypJT6ZSk3JSCaslSquLCZVxz4UZFSzaRTqfZ2ZmabcGbPhfGwyp9DF2GQhGtrvab2x9G95C4I7Q5WYrBuveeOb8BNwUaWY_nm48_X6thdP3l8CBbqjzfkWJP3pdv_GOwNfO0VrzUoyZ1jQtXBsaLzS5pa5rG9_5rUvfhbueRkuMgQg9gy1W7cDALJa1Xh2Q-ZGjVh-SAzIZi16tduBPO_UhIZ3oIXzzHMco0-ei-BohtRTzf2kVNcJJk4oHn9GTxDb1-0mEYySTwN5AuWaVZkePzJZl5EmYvNgTn1I3_CD6N385PTmlH4UAtenoNTdF98nexibBoCjF8ik3hSs2kTFuQlYtHpmBasKJNkY0Vus9OFtYyo0xiFHsM29Wick-BaCt0qoWLlRlxUSZGZqwQ3KTSFi4tdAS0_3O57eqbe5qNi3zUlUFdX-EIXl-3vwyVPTa2pL8KQt60ZymdGPzWNm9-NBEkf2nPNr1kb028hm8SGNGi2o1gv5e3HFWAv9fRlVtc1XnKJMPIMIvgSRDDoW-ihMq4ePYvX7QPt0_nHyb55N30_XPYaVGMMarV5AVsN8sr9xJu2e_Neb3cazfeT22NKOk
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwGP0EAyEkxGXcwmXzAxovs5bGl8SP06ACUVUVKmIvKLJjByZN6dRkiP57PjvuQkHlAR4j2U6cHH_-7ByfA_BKyUqauuZUOiUpN7WgWrKcqtS6gmsu3MgGs4l8Oi1OT9UssirbSKv0a-i6F4oIsdoP7gtbrxlxRzjlFDmu1X3yzPl1uBFUsRDL8_HnqyiMmb8cLmRwyvMVKdbka_XGPxrbmJ02tEs9aVK3-N7q3vBie0YaZqbxvf_p0324G_NRctwD6AFcc80uHMx6QevVIZkP57PaQ3JAZoPU9WoX7vS7fqQ_zPQQvniHY0Q0-ei-9gTbhni3tfOWYB_JxNPO6cniG-b8JDIYyaR3byDxqEq3IsdnSzLzFsweNAS7FNt_BJ_Gb-cn72g0cKAV5nkdzTF58n9iM1HhRIiLp9RYV2smZR4oVi4dGcu0YDYckE0VJs9O2qpiRpnMKPYYdppF454C0ZXQuRYuVWbERZ0ZWTAruMllZV1udQJ0_eHKKqqbe5ON83IURVA333ACr6_KX_S6HltL0l9xUHZhJyWi4LeyZfejSyD7S3m27SZ7G-gankkgtDHoJrC_hluJAcD_1dGNW1y2Zc4kw3VhkcCTHoVD3UwJVXDx7F-eaB9uzd6My8n76YfncDtQGFOMqdkL2OmWl-4l3Ky-d2ftci8Mu5-eQieN
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatial+regression+models+for+large-cohort+studies+linking+community+air+pollution+and+health&rft.jtitle=Journal+of+toxicology+and+environmental+health.+Part+A&rft.au=Cakmak%2C+Sabit&rft.au=Burnett%2C+Richard+T&rft.au=Jerrett%2C+Michael&rft.au=Goldberg%2C+Mark+S&rft.date=2003-08-22&rft.issn=1528-7394&rft.volume=66&rft.issue=16-19&rft.spage=1811&rft_id=info:doi/10.1080%2F15287390306444&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1528-7394&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1528-7394&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1528-7394&client=summon