Predicting Persistent Developmental Stuttering Using a Cumulative Risk Approach
Purpose: The purpose of this study was to explore how well a cumulative risk approach, based on empirically supported predictive factors, predicts whether a young child who stutters is likely to develop persistent developmental stuttering. In a cumulative risk approach, the number of predictive fact...
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description | Purpose: The purpose of this study was to explore how well a cumulative risk approach, based on empirically supported predictive factors, predicts whether a young child who stutters is likely to develop persistent developmental stuttering. In a cumulative risk approach, the number of predictive factors indicating a child is at risk to develop persistent stuttering is evaluated, and a greater number of indicators of risk are hypothesized to confer greater risk of persistent stuttering. Method: We combined extant data on 3- to 5-year-old children who stutter from two longitudinal studies to identify cutoff values for continuous predictive factors (e.g., speech and language skills, age at onset, time since onset, stuttering frequency) and, in combination with binary predictors (e.g., sex, family history of stuttering), used all-subsets regression and receiver operating characteristic curves to compare the predictive validity of different combinations of 10 risk factors. The optimal combination of predictive factors and the odds of a child developing persistent stuttering based on an increasing number of factors were calculated. Results: Based on 67 children who stutter (i.e., 44 persisting and 23 recovered) with relatively strong speech-language skills, the predictive factor model that yielded the best predictive validity was based on time since onset ([greater than or equal to] 19 months), speech sound skills ([less than or equal to] 115 standard score), expressive language skills ([less than or equal to] 106 standard score), and stuttering severity ([greater than or equal to] 17 Stuttering Severity Instrument total score). When the presence of at least two predictive factors was used to confer elevated risk to develop persistent stuttering, the model yielded 93% sensitivity and 65% specificity. As a child presented with a greater number of these four risk factors, the odds for persistent stuttering increased. Conclusions: Findings support the use of a cumulative risk approach and the predictive utility of assessing multiple domains when evaluating a child's risk of developing persistent stuttering. Clinical implications and future directions are discussed. |
doi_str_mv | 10.1044/2021_JSLHR-21-00162 |
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In a cumulative risk approach, the number of predictive factors indicating a child is at risk to develop persistent stuttering is evaluated, and a greater number of indicators of risk are hypothesized to confer greater risk of persistent stuttering. Method: We combined extant data on 3- to 5-year-old children who stutter from two longitudinal studies to identify cutoff values for continuous predictive factors (e.g., speech and language skills, age at onset, time since onset, stuttering frequency) and, in combination with binary predictors (e.g., sex, family history of stuttering), used all-subsets regression and receiver operating characteristic curves to compare the predictive validity of different combinations of 10 risk factors. The optimal combination of predictive factors and the odds of a child developing persistent stuttering based on an increasing number of factors were calculated. Results: Based on 67 children who stutter (i.e., 44 persisting and 23 recovered) with relatively strong speech-language skills, the predictive factor model that yielded the best predictive validity was based on time since onset ([greater than or equal to] 19 months), speech sound skills ([less than or equal to] 115 standard score), expressive language skills ([less than or equal to] 106 standard score), and stuttering severity ([greater than or equal to] 17 Stuttering Severity Instrument total score). When the presence of at least two predictive factors was used to confer elevated risk to develop persistent stuttering, the model yielded 93% sensitivity and 65% specificity. As a child presented with a greater number of these four risk factors, the odds for persistent stuttering increased. Conclusions: Findings support the use of a cumulative risk approach and the predictive utility of assessing multiple domains when evaluating a child's risk of developing persistent stuttering. Clinical implications and future directions are discussed.</description><identifier>ISSN: 1092-4388</identifier><identifier>ISSN: 1558-9102</identifier><identifier>EISSN: 1558-9102</identifier><identifier>DOI: 10.1044/2021_JSLHR-21-00162</identifier><identifier>PMID: 34902288</identifier><language>eng</language><publisher>United States: American Speech-Language-Hearing Association</publisher><subject>Age Differences ; Articulation (Speech) ; At Risk Persons ; Background ; Child Development ; Child, Preschool ; Children ; Children & youth ; Developmental Delays ; Families & family life ; Gender Differences ; Humans ; Incidence ; Language ; Language Skills ; Longitudinal Studies ; Phonetics ; Physiological aspects ; Predictor Variables ; Preschool Children ; Risk factors ; Severity (of Disability) ; Speech ; Speech Production Measurement ; Speech Skills ; Speech therapists ; Standard scores ; Stuttering ; Stuttering - diagnosis ; Validity</subject><ispartof>Journal of speech, language, and hearing research, 2022-01, Vol.65 (1), p.70-95</ispartof><rights>COPYRIGHT 2022 American Speech-Language-Hearing Association</rights><rights>Copyright American Speech-Language-Hearing Association Jan 2022</rights><rights>Copyright © 2021 American Speech-Language-Hearing Association</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c591t-801f3291e33c5f1ab33762cf2bdac9715f50b507ea81e1fcbb540d946fed347e3</citedby><cites>FETCH-LOGICAL-c591t-801f3291e33c5f1ab33762cf2bdac9715f50b507ea81e1fcbb540d946fed347e3</cites><orcidid>0000-0003-1520-0606 ; 0000-0003-4448-9525</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27923,27924</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1325505$$DView record in ERIC$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34902288$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Singer, Cara M</creatorcontrib><creatorcontrib>Otieno, Sango</creatorcontrib><creatorcontrib>Chang, Soo-Eun</creatorcontrib><creatorcontrib>Jones, Robin M</creatorcontrib><title>Predicting Persistent Developmental Stuttering Using a Cumulative Risk Approach</title><title>Journal of speech, language, and hearing research</title><addtitle>J Speech Lang Hear Res</addtitle><description>Purpose: The purpose of this study was to explore how well a cumulative risk approach, based on empirically supported predictive factors, predicts whether a young child who stutters is likely to develop persistent developmental stuttering. In a cumulative risk approach, the number of predictive factors indicating a child is at risk to develop persistent stuttering is evaluated, and a greater number of indicators of risk are hypothesized to confer greater risk of persistent stuttering. Method: We combined extant data on 3- to 5-year-old children who stutter from two longitudinal studies to identify cutoff values for continuous predictive factors (e.g., speech and language skills, age at onset, time since onset, stuttering frequency) and, in combination with binary predictors (e.g., sex, family history of stuttering), used all-subsets regression and receiver operating characteristic curves to compare the predictive validity of different combinations of 10 risk factors. The optimal combination of predictive factors and the odds of a child developing persistent stuttering based on an increasing number of factors were calculated. Results: Based on 67 children who stutter (i.e., 44 persisting and 23 recovered) with relatively strong speech-language skills, the predictive factor model that yielded the best predictive validity was based on time since onset ([greater than or equal to] 19 months), speech sound skills ([less than or equal to] 115 standard score), expressive language skills ([less than or equal to] 106 standard score), and stuttering severity ([greater than or equal to] 17 Stuttering Severity Instrument total score). When the presence of at least two predictive factors was used to confer elevated risk to develop persistent stuttering, the model yielded 93% sensitivity and 65% specificity. As a child presented with a greater number of these four risk factors, the odds for persistent stuttering increased. Conclusions: Findings support the use of a cumulative risk approach and the predictive utility of assessing multiple domains when evaluating a child's risk of developing persistent stuttering. Clinical implications and future directions are discussed.</description><subject>Age Differences</subject><subject>Articulation (Speech)</subject><subject>At Risk Persons</subject><subject>Background</subject><subject>Child Development</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Children & youth</subject><subject>Developmental Delays</subject><subject>Families & family life</subject><subject>Gender Differences</subject><subject>Humans</subject><subject>Incidence</subject><subject>Language</subject><subject>Language Skills</subject><subject>Longitudinal Studies</subject><subject>Phonetics</subject><subject>Physiological aspects</subject><subject>Predictor Variables</subject><subject>Preschool Children</subject><subject>Risk factors</subject><subject>Severity (of Disability)</subject><subject>Speech</subject><subject>Speech Production Measurement</subject><subject>Speech Skills</subject><subject>Speech therapists</subject><subject>Standard scores</subject><subject>Stuttering</subject><subject>Stuttering - 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diagnosis</topic><topic>Validity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Singer, Cara M</creatorcontrib><creatorcontrib>Otieno, Sango</creatorcontrib><creatorcontrib>Chang, Soo-Eun</creatorcontrib><creatorcontrib>Jones, Robin M</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Linguistics and Language Behavior Abstracts (LLBA)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Education Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Science Database (Alumni Edition)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>Education Periodicals</collection><collection>STEM Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Education Collection</collection><collection>Linguistics Collection</collection><collection>Linguistics Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Education Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Social Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Education</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of speech, language, and hearing research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singer, Cara M</au><au>Otieno, Sango</au><au>Chang, Soo-Eun</au><au>Jones, Robin M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1325505</ericid><atitle>Predicting Persistent Developmental Stuttering Using a Cumulative Risk Approach</atitle><jtitle>Journal of speech, language, and hearing research</jtitle><addtitle>J Speech Lang Hear Res</addtitle><date>2022-01-01</date><risdate>2022</risdate><volume>65</volume><issue>1</issue><spage>70</spage><epage>95</epage><pages>70-95</pages><issn>1092-4388</issn><issn>1558-9102</issn><eissn>1558-9102</eissn><abstract>Purpose: The purpose of this study was to explore how well a cumulative risk approach, based on empirically supported predictive factors, predicts whether a young child who stutters is likely to develop persistent developmental stuttering. In a cumulative risk approach, the number of predictive factors indicating a child is at risk to develop persistent stuttering is evaluated, and a greater number of indicators of risk are hypothesized to confer greater risk of persistent stuttering. Method: We combined extant data on 3- to 5-year-old children who stutter from two longitudinal studies to identify cutoff values for continuous predictive factors (e.g., speech and language skills, age at onset, time since onset, stuttering frequency) and, in combination with binary predictors (e.g., sex, family history of stuttering), used all-subsets regression and receiver operating characteristic curves to compare the predictive validity of different combinations of 10 risk factors. The optimal combination of predictive factors and the odds of a child developing persistent stuttering based on an increasing number of factors were calculated. Results: Based on 67 children who stutter (i.e., 44 persisting and 23 recovered) with relatively strong speech-language skills, the predictive factor model that yielded the best predictive validity was based on time since onset ([greater than or equal to] 19 months), speech sound skills ([less than or equal to] 115 standard score), expressive language skills ([less than or equal to] 106 standard score), and stuttering severity ([greater than or equal to] 17 Stuttering Severity Instrument total score). When the presence of at least two predictive factors was used to confer elevated risk to develop persistent stuttering, the model yielded 93% sensitivity and 65% specificity. As a child presented with a greater number of these four risk factors, the odds for persistent stuttering increased. Conclusions: Findings support the use of a cumulative risk approach and the predictive utility of assessing multiple domains when evaluating a child's risk of developing persistent stuttering. Clinical implications and future directions are discussed.</abstract><cop>United States</cop><pub>American Speech-Language-Hearing Association</pub><pmid>34902288</pmid><doi>10.1044/2021_JSLHR-21-00162</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0003-1520-0606</orcidid><orcidid>https://orcid.org/0000-0003-4448-9525</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Age Differences Articulation (Speech) At Risk Persons Background Child Development Child, Preschool Children Children & youth Developmental Delays Families & family life Gender Differences Humans Incidence Language Language Skills Longitudinal Studies Phonetics Physiological aspects Predictor Variables Preschool Children Risk factors Severity (of Disability) Speech Speech Production Measurement Speech Skills Speech therapists Standard scores Stuttering Stuttering - diagnosis Validity |
title | Predicting Persistent Developmental Stuttering Using a Cumulative Risk Approach |
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