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dc.contributor.authorFang, Hua
dc.contributor.authorJohnson, Craig
dc.contributor.authorStopp, Christian
dc.contributor.authorEspy, Kimberly Andrews
dc.date2022-08-11T08:10:44.000
dc.date.accessioned2022-08-23T17:18:27Z
dc.date.available2022-08-23T17:18:27Z
dc.date.issued2011-01-01
dc.date.submitted2011-03-10
dc.identifier.citationNeurotoxicol Teratol. 2011 Jan-Feb;33(1):155-65. doi: 10.1016/j.ntt.2010.08.003. PubMed PMID: 21256430; PubMed Central PMCID: PMC3052936. <a href="http://dx.doi.org/10.1016/j.ntt.2010.08.003">Link to article on publisher's website</a>
dc.identifier.issn1872-9738
dc.identifier.doi10.1016/j.ntt.2010.08.003
dc.identifier.pmid21256430
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47859
dc.description<p>This is the authors' accepted manuscript which was formally published as: Fang H, Johnson C, Stopp C, Espy KA. A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering.
dc.description.abstractBACKGROUND: Prenatal tobacco exposure is a risk factor for the development of externalizing behaviors and is associated with several adverse health outcomes. Because pregnancy smoking is a complex behavior with both daily fluctuations and changes over the course of pregnancy, quantifying tobacco exposure is a significant challenge. To better measure the degree of tobacco exposure, costly biological specimens and repeated self-report measures of smoking typically are collected throughout pregnancy. With such designs, there are multiple, and substantially correlated, indices that can be integrated via new statistical methods to identify patterns of prenatal exposure. METHOD: A multiple-imputation-based fuzzy clustering technique was designed to characterize topography of prenatal exposure. This method leveraged all repeatedly measured maternal smoking variables in our sample data, including (a) cigarette brand; (b) Fagerstrom nicotine dependence item scores; (c) self-reported smoking; and (d) cotinine level in maternal urine and infant meconium samples. Identified exposure groups then were confirmed using a suite of clustering validation indices based on multiple imputed datasets. The classifications were validated against irritable reactivity in the first month of life and birth weight of 361 neonates (Male(_n)=185; Female(_n)=176; Gestational Age_(Mean)=39weeks). RESULTS: This proposed approach identified three exposure groups, non-exposed, lighter-tobacco-exposed, and heavier-tobacco-exposed based on high-dimensional attributes. Unlike cut-off score derived groups, these groupings reflect complex smoking behavior and individual variation of nicotine metabolism across pregnancy. The identified groups predicted differences in birth weight and in the pattern of change in neonatal irritable reactivity, as well as resulted in increased predictive power. Multiple-imputation-based fuzzy clustering appears to be a useful method to categorize patterns of exposure and their impact on outcomes.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21256430&dopt=Abstract">Link to article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052936/pdf/nihms-232138.pdf
dc.rights<p>Distributed under a CC-BY-NC-ND license per publisher's sharing policy at https://www.elsevier.com/about/company-information/policies/sharing#acceptedmanuscript.</p>
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPrenatal Injuries
dc.subjectMaternal Exposure
dc.subjectMaternal-Fetal Exchange
dc.subjectSmoking
dc.subjectTobacco Smoke Pollution
dc.subjectFuzzy Logic
dc.subjectData Interpretation, Statistical
dc.subjectUMCCTS funding
dc.subjectPrenatal tobacco exposure
dc.subjectFuzzy clustering
dc.subjectMultiple imputation
dc.subjectExposure pattern
dc.subjectIrritable reactivity
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectFemale Urogenital Diseases and Pregnancy Complications
dc.subjectMaternal and Child Health
dc.titleA new look at quantifying tobacco exposure during pregnancy using fuzzy clustering
dc.typeAccepted Manuscript
dc.source.journaltitleNeurotoxicology and teratology
dc.source.volume33
dc.source.issue1
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1969&amp;context=qhs_pp&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/970
dc.identifier.contextkey1864292
refterms.dateFOA2022-08-23T17:18:28Z
html.description.abstract<p>BACKGROUND: Prenatal tobacco exposure is a risk factor for the development of externalizing behaviors and is associated with several adverse health outcomes. Because pregnancy smoking is a complex behavior with both daily fluctuations and changes over the course of pregnancy, quantifying tobacco exposure is a significant challenge. To better measure the degree of tobacco exposure, costly biological specimens and repeated self-report measures of smoking typically are collected throughout pregnancy. With such designs, there are multiple, and substantially correlated, indices that can be integrated via new statistical methods to identify patterns of prenatal exposure.</p> <p>METHOD: A multiple-imputation-based fuzzy clustering technique was designed to characterize topography of prenatal exposure. This method leveraged all repeatedly measured maternal smoking variables in our sample data, including (a) cigarette brand; (b) Fagerstrom nicotine dependence item scores; (c) self-reported smoking; and (d) cotinine level in maternal urine and infant meconium samples. Identified exposure groups then were confirmed using a suite of clustering validation indices based on multiple imputed datasets. The classifications were validated against irritable reactivity in the first month of life and birth weight of 361 neonates (Male(_n)=185; Female(_n)=176; Gestational Age_(Mean)=39weeks).</p> <p>RESULTS: This proposed approach identified three exposure groups, non-exposed, lighter-tobacco-exposed, and heavier-tobacco-exposed based on high-dimensional attributes. Unlike cut-off score derived groups, these groupings reflect complex smoking behavior and individual variation of nicotine metabolism across pregnancy. The identified groups predicted differences in birth weight and in the pattern of change in neonatal irritable reactivity, as well as resulted in increased predictive power. Multiple-imputation-based fuzzy clustering appears to be a useful method to categorize patterns of exposure and their impact on outcomes.</p>
dc.identifier.submissionpathqhs_pp/970
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages155-165


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<p>Distributed under a CC-BY-NC-ND license per publisher's sharing policy at https://www.elsevier.com/about/company-information/policies/sharing#acceptedmanuscript.</p>
Except where otherwise noted, this item's license is described as <p>Distributed under a CC-BY-NC-ND license per publisher's sharing policy at https://www.elsevier.com/about/company-information/policies/sharing#acceptedmanuscript.</p>