{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:14:54Z","timestamp":1790626494157,"version":"4.1.0"},"reference-count":57,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Allergy Immunology"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>There is growing interest in the use of latent trajectory methodology to identify wheeze patterns in heterogeneous populations of children. This study systematically reviewed and meta\u2010analyzed childhood wheeze trajectory studies to identify childhood wheeze trajectory group\u2010specific risk factors among children from birth through to adolescence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We included studies that used latent trajectory methodology to identify wheeze trajectories and associated risk factors. We searched PubMed, EMBASE, and Google Scholar from 2000 through September 30, 2019, for relevant studies. The study was conducted according to the PRISMA recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirteen cohort studies conducted in eleven high\u2010income countries were included in our meta\u2010analysis with the length of follow\u2010up ranging from 3 to 18\u00a0years. Five distinct latent wheeze trajectory groups were identified: Never\/Infrequent, Early\u2010Transient, Early\u2010Persistent, Intermediate\u2010Onset, and Late\u2010Onset. We found moderate\u2010to\u2010strong evidence that family history of asthma predicted persistent childhood wheezing among male children but with lower risk among first\u2010born children. There was weak\u2010to\u2010moderate evidence for childhood atopy, male sex, short duration of breastfeeding, tobacco exposure, daycare attendance, and having siblings as risk factors for Early\u2010Transient wheezing; except for breastfeeding, these factors were also associated with intermediate and Late\u2010Onset wheezing with varying effect sizes in high\u2010risk vs general population cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings confirm the consistency of wheeze trajectory groups defined by onset, peak prevalence, and duration; we also suggest a common nomenclature for future trajectory studies. With the exception of the relationship between a family history of asthma and persistent childhood wheezing, commonly suspected wheeze risk factors (childhood atopy, male sex, short duration of breastfeeding, tobacco exposure, daycare attendance, and having siblings) are not trajectory\u2010specific and have varying effects in high\u2010risk vs general population cohorts. Delineation of time\u2010varying risk factor effects may be critical to the specificity of wheeze trajectory group prediction to better inform prognosis and targeted early preventive intervention among at\u2010risk children.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/pai.13313","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T19:29:43Z","timestamp":1594841383000},"page":"34-50","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Childhood wheeze trajectory\u2010specific risk factors: A systematic review and meta\u2010analysis"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4580-7428","authenticated-orcid":false,"given":"Arthur H.","family":"Owora","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics School of Public Health Indiana University Bloomington IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6638-2641","authenticated-orcid":false,"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics School of Public Health Indiana University Bloomington IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2222.2010.03541.x"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2018.10.059"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2011.04.026"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/1744666X.2017.1257940"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.4159\/9780674041318"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/1082-989X.4.2.139"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.4135\/9781412986311.n19"},{"key":"e_1_2_10_9_1","unstructured":"VermuntJ.Growth models for categorical response variables: Standard latent\u2010class and hybrid approaches. 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