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Risk and Protective Factors Associated With Speech and Language Impairment in a Nationally Representative Sample of 4- to 5-Year-Old Children Purpose: To determine risk and protective factors for speech and language impairment in early childhood. Method: Data are presented for a nationally representative sample of 4,983 children participating in the Longitudinal Study of Australian Children (described in McLeod & Harrison, 2009). Thirty-one child, parent, family, and community factors previously reported as being predictors of speech and language impairment were tested as predictors of (a) parent-rated expressive speech/language concern and (b) receptive language concern, (c) use of speech-language pathology services, and (d) low receptive vocabulary. Results: Bivariate logistic regression analyses confirmed 29 of the identified factors. However, when tested concurrently with other predictors in multivariate analyses, only 19 remained significant: 9 for 24 outcomes and 10 for 1 outcome. Consistent risk factors were being male, having ongoing hearing problems, and having a more reactive temperament. Protective factors were having a more persistent and sociable temperament and higher levels of maternal well-being. Results differed by outcome for having an older sibling, parents speaking a language other than English, and parental support for childrens learning at home. Conclusion: Identification of children requiring speech and language assessment requires consideration of the context of family life as well as biological and psychosocial factors intrinsic to the child. KEY WORDS: risk factor, protective factor, epidemiology, speech, language, communication S peech and language acquisition in early childhood is a powerful indicator of the developmental and cognitive abilities that under- pin childrens successful transition to school (Nelson, Nygren, Walker, & Panoscha, 2006). Longitudinal results from the U.S. National Institute of Child Health and Human Development [NICHD] Study of Early Child Care and Youth Development have demonstrated that multiple path- ways all funnel through one final common pathway, namely the childs language skills, just before entering school I to define the childs read- inessfor school(NICHD, 2004, p. 28). These findings, particularly when viewed in combination with prevalence studies (e.g., King et al., 2005; Law, Boyle, Harris, Harkness, & Nye, 2000; McLeod & Harrison, 2009; McLeod & McKinnon, 2007) showing that a significant proportion of chil- dren do not successfully acquire speech and language prior to school, are compelling. They point to the need to identify and provide support for Linda J. Harrison Sharynne McLeod Charles Sturt University, Bathurst, Australia Journal of Speech, Language, and Hearing Research Vol. 53 508529 April 2010 D American Speech-Language-Hearing Association 508 on June 11, 2010 jslhr.asha.org Downloaded from

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Risk and Protective Factors Associated With Speech and Language Impairment in a Nationally Representative Sample of 4- to 5-Year-Old ChildrenLinda J. Harrison Sharynne McLeodCharles Sturt University, Bathurst, Australia Purpose: To determine risk and protective factors for speech and language impairment in early childhood. Method: Data are presented for a nationally representative sample of 4,983 children participating in the Longitudinal Study of Australian Children (described in McLeod & Harrison, 2009). Thirty-one child, parent, family, and community factors previously reported as being predictors of speech and language impairment were tested as predictors of (a) parent-rated expressive speech/language concern and (b) receptive language concern, (c) use of speech-language pathology services, and (d) low receptive vocabulary. Results: Bivariate logistic regression analyses confirmed 29 of the identified factors. However, when tested concurrently with other predictors in multivariate analyses, only 19 remained significant: 9 for 24 outcomes and 10 for 1 outcome. Consistent risk factors were being male, having ongoing hearing problems, and having a more reactive temperament. Protective factors were having a more persistent and sociable temperament and higher levels of maternal well-being. Results differed by outcome for having an older sibling, parents speaking a language other than English, and parental support for childrens learning at home. Conclusion: Identification of children requiring speech and language assessment requires consideration of the context of family life as well as biological and psychosocial factors intrinsic to the child. KEY WORDS: risk factor, protective factor, epidemiology, speech, language, communication

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peech and language acquisition in early childhood is a powerful indicator of the developmental and cognitive abilities that underpin childrens successful transition to school (Nelson, Nygren, Walker, & Panoscha, 2006). Longitudinal results from the U.S. National Institute of Child Health and Human Development [NICHD] Study of Early Child Care and Youth Development have demonstrated that multiple pathways all funnel through one final common pathway, namely the childs language skills, just before entering school I to define the childs readiness for school (NICHD, 2004, p. 28). These findings, particularly when viewed in combination with prevalence studies (e.g., King et al., 2005; Law, Boyle, Harris, Harkness, & Nye, 2000; McLeod & Harrison, 2009; McLeod & McKinnon, 2007) showing that a significant proportion of children do not successfully acquire speech and language prior to school, are compelling. They point to the need to identify and provide support for

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children at risk of speech and language impairment in their early childhood years, which is when they are most likely to benefit from early intervention (Almost & Rosenbaum, 1998; Gibbard, Coglan, & MacDonald, 2004). Early detection and early intervention can reduce the severity and longevity of speech and language difficulties (Gibbard et al., 2004; Schwarz & Nippold, 2002); however, many children who are eventually identified as having speech and language impairment display no organic basis and few obvious indicators in the first years of life (Dollaghan & Campbell, 2009; Roulstone, Miller, Wren, & Peters, 2009). Consequently, not all children who may benefit from and be eligible for speech-language pathology services are identified or referred prior to commencing school. Primary care professionals, such as doctors, nurses, and early childhood teachers, are often expected to identify children who may be at risk and may require a speech and language assessment to diagnose a speech and language impairment. The methods that primary care professionals use to diagnose impairment tend to be (a) comparison with other children of a similar age, (b) acknowledgment of parental concern, and (c) completion of checklists of speech and language milestones such as having fewer than 50 words or not combining words at 24 months (e.g., Coplan, Gleason, Ryan, Bourke, & Williams, 1982; Luinge, Post, Wit, & Goorhuis-Brouwer, 2006). Recognition and identification of known risk and protective factors is another method that can be employed, particularly when the above three screening methods are either unavailable, inappropriate for a particular context, or lack sensitivity. Tomblin, Hardy, and Hein (1991) have recommended that programs of preschool identification should consider the inclusion of a registry of children who are at risk for a communication disorder (p. 1096). However, as Nelson et al. (2006) concluded, A list of specific risk factors to guide primary care physicians in selective screening has not been developed or tested (p. e302). Current bioecological theories (Bronfenbrenner, 2005) provide a useful framework for addressing risk and protective factors for childrens health and development. Bioecological models elucidate the interacting influences of proximal social and psychological contexts (e.g., parental and family characteristics) and distal social contexts (e.g., community characteristics and supports) with the inherited and biological characteristics of the individual. This approach accords with the International Classification of Functioning, Disability, and Health (World Health Organization, 2007), which recognizes the complex interrelationships that exist between biological, individual, and societal factors that influence child functioning. Research investigations of the predictors of speech and language impairment that are consistent with these bioecological models seek to include a wide range of variables

describing parental, family, and neighborhood attributes, along with documentation of child health and psychosocial characteristics (e.g., Reilly et al., 2007; Zubrick, Taylor, Rice, & Slegers, 2007). Such studies are able to examine concurrently a large number of possible predictors of speech and language delay or impairment and use complex statistical analyses to identify the best set of predictors. Multivariable designs of this type, however, are relatively rare in studies of speech and language impairment. A systematic review of risk and protective factors associated with screening for speech and language impairment in early childhood undertaken by the U.S. Preventative Services Task Force (Nelson et al., 2006; U.S. Preventative Services Task Force, 2006) showed that no studies encompassed all the potential predictor domains (such as family history, child gender, socioeconomic status (SES), birth order, perinatal factors, parental education, medical conditions, and other). Most of the 16 studies reviewed had addressed only one or two of these domains. Nelson et al. (2006) concluded The most consistently reported risk factors include a family history of speech and language delay, male gender, and perinatal factors; however, their role in screening is unclear (p. e302). For the present study, articles included in the U.S. Preventative Services Task Force study (2006) have been reviewed again, along with additional literature, to summarize evidence that confirms or disconfirms an association between each risk factor and childhood speech and language impairment. Risk/protective factors are reviewed within the domains identified by Nelson et al. (2006) plus additional domains: child hearing status, oral sucking habits, temperament, parent language spoken at home, minority status/race, maternal mental health and maternal age, family support for learning, family smoking habits, and neighborhood disadvantage (see Table 1). These domains are grouped within three broad categories: child, parent, and family/community. A brief summary of the literature for each domain is presented, followed by consideration of design issues that may influence or explain any differences in the reported findings.

Child FactorsSex. Sex of child was examined in 14 studies, as shown in Table 1. A significant association between being male and having an increased risk for speech and/or language impairment was found in 11 of these studies (Campbell et al., 2003; Chevrie-Muller, Watier, Arabia, Arabia, & Dellatolas, 2005; Choudhury & Benasich, 2003; Prior et al., 2008; Reilly et al., 2006, 2007; StantonChapman, Chapman, Bainbridge, & Scott, 2002; Tomblin et al., 1991; Yliherva, Olsen, Maki-Torkko, Koiranen, & Jarvelin, 2001; Yoshinaga-Itano, Sedey, Coulter, & Mehl, 1998; Zubrick et al., 2007).

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Perinatal factors. Perinatal factors were examined in 11 studies, and a significant association with speech and language impairment was found in 5. Prenatal factors were associated with risk for speech and language impairment in Fox, Dodd, and Howard (2002), who identified significant effects for extreme stress, maternal infections, and medications that could cause damage to the fetus during pregnancy, and in a population-based investigation of birth risk factors for specific language impairment (SLI) by Stanton-Chapman et al. (2002), who noted that late or no prenatal care was a significant predictor of SLI 6 years later, at school. Less consistent results have been reported for perinatal difficulties. For example, Fox et al. (2002) found that forceps or ventouse delivery, induced delivery, complications such as umbilical cord prolapse, infections, preterm birth, and post-partum resuscitation (p. 122) were significant predictors of speech impairment. Studies by Weindrich, Jennen-Steinmetz, Laucht, Esser, and Schmidt (2000) and Yliherva et al. (2001) have also linked perinatal factors to speech and language problems. In contrast, Peters, Grievink, van Bon, van den Bercken, and Schilder (1997); Reilly et al. (2006, 2007); Tomblin et al. (1991); and Tomblin, Smith, and Zhang (1997) found that postnatal factors did not present significant risks for speech and language impairment. For example, Tomblin et al. (1991) found that birth events such as infections, low birth weight, breathing difficulty, ototoxic drugs, feeding problems, transfusions, and birth defects (p. 1101) did not predict poor communication status. Tomblin et al. (1997) also found that birth events including type of delivery, induction of labor, duration of labor, and labor and birth complications were not significant risk factors. Existing findings are also inconsistent for prematurity and low birth weight. Reilly et al. (2006, 2007) found that being born preterm (< 36 weeks) was not a significant risk factor for early language delay. Similarly, Tomblin et al. (1997), who used < 2,500 g as the cutoff, and Reilly et al. (2007), who used a continuous scale of birth weight in kilograms, have reported that low birth weight was not a significant predictor. In contrast, Zubrick et al. (2007) found that low birth weight and premature birth were independently significant for late language emergence. Similar results were noted by StantonChapman et al. (2002): low birth weight (< 2,500 g), very low birth weight (< 1,500 g), and a low 5-min Apgar score were significant risk factors for school-age specific language impairment. Multiple birth. Two studies based on the same cohort have examined the association between multiple birth and the risk for language impairment. Reilly et al. (2006) found twin birth to be a significant risk factor for communication impairment in 8- and 12-month-old infants; however, by the time this cohort reached 2 years of

age, twin status was not a significant risk factor (Reilly et al., 2007). Medical conditions. Mixed findings have been reported for the impact of child illness and infection on speech and language impairment. Medical conditions were examined in seven studies and a significant association was found in five. Singer et al. (2001) found that patent ductus arteriosus and bronchopulmonary dysplasia were significant risk factors associated with speech and language impairment. Choudhury and Benasich (2003) reported that autoimmune diseases presented a significant risk, but asthma did not. The U.S. Preventative Services Task Force review (2006) excluded studies of otitis media (OME) as a risk factor because it is a complex and controversial area (Nelson et al., 2006, p. e302). Two studies have reported a significant association between OME and speech impairment in bivariate analyses but a nonsignificant association when multivariate analyses were applied (Campbell et al., 2003; Fox et al., 2002). Peters et al. (1997) indicated that OME even when combined with a number of other risk factors produces only minor effects on later language (p. 31). In contrast, Shriberg, Friel-Patti, Flipsen Jnr, and Brown (2000), using structural equation modeling, reported a significant relationship between otitis media and speech / language outcomes. Hearing status. Impaired hearing was found to be a significant risk factor for difficulties with speech, language, and learning in a large study of 8,370 Finnish children (Yliherva et al., 2001) but not a significant risk factor in the study by Singer et al. (2001) of over 200 children. In a study of 150 children with hearing impairment, Yoshinaga-Itano et al. (1998) reported that children had greater difficulties with speech and language development if they were identified with a hearing impairment after the age of 6 months; identification prior to 6 months coupled with subsequent early intervention was associated with increased language scores. Oral sucking habits. Oral sucking habits, including breast-feeding, have been found to be both a risk and a protective factor in studies that have examined this factor. Fox et al. (2002) found that excessive sucking of pacifiers, or thumb or bottle usage as a pacifier, was a moderate predictor of speech impairment. On the other hand, Tomblin et al. (1997) demonstrated that breastfeeding for less than 9 months was associated with an increased risk of speech and language impairment. Temperament. Three studies have examined child temperament characteristics as possible risk or protective factors for speech and language impairment. Hauner, Shriberg, Kwiatkowski, and Allen (2005) considered the effect of different aspects of child temperament as risk factors for increased severity of expression of speech delay (p. 635). Increased severity was related to negative

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Table 1. Summary of significant and nonsignificant risk factors for speech and language impairment for children based on the systematic review by the U.S. Preventative Task Force (2006) and additional studies.Demographic information Child variables Oral Study and country Number Age in months Outcome measure Male sex Perinatal factors Multiple birth Medical condition Hearing status sucking habits Temperament Family history Languages spoken Minority status or race Mothers education Fathers education Parent variables Parental mental health Maternal age at birth Family size and birth order Family and community variables Home learning activities Smoking in the household Socioeconomic status Neighborhood disadvantage

Brookhouser et al. (1979) USA Campbell et al. (2003) USA ChevrieMuller et al. (2005)* France Choudhury & Benasich (2003) USA Felsenfeld & Plomin (1997)* USA Fox et al. (2002) Germany Hauner et al. (2005)* USA Lyytinen et al. (2001) Finland

24 cases

2862

Language

y

n

398 cases and 241 controls 2059 in cohort

36 42

Speech Language

y y

n

y

y

n

y y

y

n

42 cases and 92 controls 156 adopted and nonadopted children 65 cases and 48 controls

36

Language

y

n

y/n

y

n

n

y/n

y

n

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84

Speech

n

y

n

3286 3672 054

Speech Speech Speech and language Language Language Language Language Language

n

y

n

y

y

y y

29 cases and 87 controls 107 with risk of dyslexia and 93 without Peters et al. (1997) 946 in cohort Netherlands Prior et al. (2008)* 1,911 in cohort Australia Reilly et al. (2006)* 1,911 in cohort Australia Reilly et al. (2007)* 1,720 in cohort Australia Singer et al. (2001) 98 cases and USA 70+95 controls Stanton-Chapman 5,862 cases et al. (2002) and USA 201,834 not identified 76 cases and 54 controls 662 in cohort 177 cases and 925 controls

8496 12 and 24 8 and 12 24 36

n y y y/n

n n n

y n

y y

n

y

y y

y y/n

y

y y/n n

y

y n n

y/n

n

y

y n

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7284

Language

y

y

n

y

n

y

n

Tallal et al. (1989) USA Tomblin et al. (1991) USA Tomblin et al. (1997) USA

4859 3060 kindergarten

Language Speech and language Speech and language

y/n

n n

y

y y y/n

y n y

y y y

y

y

y

(Continued on the following page)

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Table 1 Continued. Summary of significant and nonsignificant risk factors for speech and language impairment for children based on the systematic review by the U.S. Preventative Task Force (2006) and additional studies.Demographic information Child variables Oral Study and country Number Age in months Outcome measure Male sex Perinatal factors Multiple birth Medical condition Hearing status sucking habits Temperament Family history Languages spoken Minority status or race Mothers education Fathers education Parent variables Parental mental health Maternal age at birth Family size and birth order Family and community variables Home learning activities Smoking in the household Socioeconomic status Neighborhood disadvantage

Weindrich et al. (2000) Germany Whitehurst et al. (1991) Yliherva et al. (2001) Finland YoshinagaItano

320 in cohort

54 and 96 Speech and language

y

y

y

y

62 cases and 55 controls 8,370 in cohort 150 cases

2438 96 1336 24

Language Speech and language Speech and language Language

y y y

y y

y/n

y y

n

n y

n y/n

y n n

n

n

y n

y y

n

n n

n

et al. (1998) USA Zubrick et al. 1,766 in cohort (2007)* Australia Note.

y = yes, the variable was examined and there was a statistically significant association; n = no, the variable was examined and was not associated with speech and/or language delay; y/n = different findings for different outcomes; = the variable was not examined.

Asterisk indicates studies that were not included in the review by the U.S. Preventative Services Task Force (2006).

affect associated with low approachability/sociability, negative mood, and low task persistence. Prior et al. (2008) found that having a shy temperament was negatively related to vocabulary production and communication and symbolic development in a large cohort of 1- to 2-year-old children. On the other hand, in Zubrick et al. (2007), only one of nine dimensions of child temperament (negative mood) occurred more frequently in 2-year-old children with late language emergence.

as African-American was not a significant risk factor. Minority status was not a significant factor in the study conducted by Yoshinaga-Itano et al. (1998). Educational level of mother and father. Of 14 studies that have examined the association between parents educational level on childrens speech and language acquisition, 10 reported a risk for speech and language impairment at low parental educational level. These included studies of only mothers education (Campbell et al., 2003; Peters et al., 1997; Reilly et al., 2007; StantonChapman et al., 2002; Yliherva et al., 2001), only fathers education (Tomblin et al., 1991), and both mothers and fathers education (Chevrie-Muller et al., 2005; Tallal et al., 1989; Tomblin et al., 1997; Weindrich et al., 2000). In contrast, 4 studies have shown that parental education level was not a significant risk factor (Choudhury & Benaisch, 2003; Singer et al., 2001; Yoshinaga-Itano et al., 1998; Zubrick et al., 2007). Parental mental health. Five studies that have examined this domain reported mixed results. Three studies found that indicators of parental mental health were not associated with speech and language impairment in 8- to 12-month-old infants (Reilly et al., 2006) or 2-yearolds (Reilly et al., 2007; Zubrick et al., 2007). In contrast, Prior et al. (2008) reported that maternal psychosocial indices, specifically mothers rate of coping and partner relationship satisfaction, were positively associated with language development at 24 months, and Weindrich et al. (2000) found that parental mental health was a risk factor for speech, language, reading, and spelling in children aged 54 and 96 months. Maternal age at birth of child. Studies that examined maternal age at the birth of the child have reported mixed findings for speech and/or language impairment. Younger mothers have been identified in risk groups for children with specific language impairment (Tomblin et al., 1997) and poor speech and language abilities (Yliherva et al., 2001). Choudhury and Benasich (2003) noted that younger maternal age was a characteristic of families with a history of speech language impairment but was not linked to childrens assessed receptive and expressive language at age 3 years. Similarly, StantonChapman et al. (2002) reported no relationship between maternal age and school-identified specific language impairment, after accounting for the effects of other biological and environmental risks. Reilly et al. (2007) found that older maternal age was a significant risk factor for communication and symbolic behavior at age 24 months but not for vocabulary production.

Parent FactorsFamily history of speech and language problems. Thirteen studies recorded family history of speech, language, and/or learning difficulties, with 11 identifying this as a risk factor for childhood speech and language impairment (Campbell et al., 2003; Choudhury & Benasich, 2003; Felsenfeld & Plomin, 1997; Fox et al., 2002; Lyytinen et al., 2001; Reilly et al., 2006, 2007; Tallal, Ross, & Curtiss, 1989; Tomblin et al., 1991, 1997; Zubrick et al., 2007). Of these, Tomblin et al. (1997) found that paternal family history was significant, but maternal history was not. Three other studies have reported that family history of speech / language impairment (Brookhouser, Hixson, & Matkin, 1979; Whitehurst et al., 1991) and family history of hearing loss (Tomblin et al., 1991) were not significantly associated with language impairment or poor communication status in children. The impact of family history may be due to genetic or environmental influences or to a combination of both. This question has been examined by Felsenfeld and Plomin (1997) in a study of adopted and nonadopted children. Family history for biological parents was a significant risk factor for speech impairment, whereas for adoptive parents it was not. Their results support the view that the biological basis of family history has a stronger influence on childrens speech and language than the home learning environment. Languages spoken. Risk for speech and language impairment in children with a nondominant language background has been demonstrated in the case of nonEnglish speakers in an English-dominant society (Reilly et al., 2007), non-French speakers in a French-dominant society (Chevrie-Muller et al., 2005), and non-Dutch speakers in a Dutch-dominant society (Peters et al., 1997). In contrast, Stanton-Chapman et al. (2002), who studied an English-dominant U.S. state with a large Spanishspeaking population, reported that Spanish and other non-English speakers were less likely to be placed in SLI classrooms than native-English speakers (p. 397). Minority status or race. Risk for speech and language impairment has been studied in relation to minority status or race. Singer et al. (2001) reported that children of a minority race were at greater risk than their peers; however, Campbell et al. (2003) found that identification

Family and Community FactorsFamily size. Findings linking speech and language acquisition with the number of siblings, the number of

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children in the household, and birth order have been largely consistent. Choudhury and Benasich (2003) demonstrated that an increased number of children in the household was a significant risk factor for language impairment. Yliherva et al. (2001) found that having more than four children in the household increased risk of speech, language, and learning difficulties. Tomblin et al. (1991) demonstrated that children who held later birth positions in the family were more likely to have a poor communication status between 2.5 and 5 years of age, and, similarly, Stanton-Chapman et al. (2002) reported that children whose birth position was third or higher were more likely to be identified as having specific language impairment at age 67 years. Zubrick et al. (2007) reported that the presence of two or more siblings was a risk for language delay in a sample of 2-year-olds, and Reilly et al. (2007), who studied the same age group, reported that birth order was a risk for vocabulary production but not for communication and symbolic behavior. Home learning activities. In a study of adopted and nonadopted children, Felsenfeld and Plomin (1997) used the HOME Scale of family environment (Caldwell & Bradley, 1984) and found that family environment was not significantly associated with speech outcome at age 7. Smoking in the household. Tomblin et al. (1997) found that maternal smoking in the household increased the risk of speech and language difficulties but that this was mediated by maternal education levels. Zubrick et al. (2007), on the other hand, reported no effect of maternal smoking (current and during/before pregnancy) on late language emergence, and Stanton-Chapman et al. (2002) found no effect of smoking during pregnancy on schoolidentified specific language impairment. Socioeconomic factors. Of the five studies that considered family SES (i.e., combined yearly income, occupational prestige, education levels, and qualification for Medicaid health insurance), only one found a significant risk for language impairment (Singer et al., 2001). Low SES was not identified as a risk factor by Campbell et al. (2003), Choudhury and Benasich (2003), Yoshinaga-Itano et al. (1998), or Zubrick et al. (2007). Neighborhood disadvantage. Two studies included information provided by the census-based Socio-Economic Indexes for Areas (SEIFA) from the Australian Bureau of Statistics (ABS, 2003) as a measure of neighborhood disadvantage. Reilly et al. (2006, 2007) reported that lower scores on the Index of Disadvantage (i.e., living in a more disadvantaged area) was a significant predictor for language difficulties at age 812 months but not at age 24 months. Zubrick et al. (2007) noted no difference for children with and without late language emergence by SEIFA scores.

Methodological Considerations When Examining Studies to Determine Risk FactorsAs can be seen from the collation of findings set out in Table 1, varying results have been reported as to whether or not each of the factors reviewed presents as a significant risk for childhood speech and language impairment. These differences make it difficult to provide a definitive list of specific risk factors to guide primary care professionals (cf. Nelson et al., 2006). There are three main reasons for the observed differences in study results: (a) differences in the size and nature of the samples, (b) differences in the speech and language outcome measures that have been used to identify impairment, and (c) differences in the range and number of possible predictor variables included in the design and the analyses that have been applied to these variables. Size and nature of the sample. The reviewed studies have used two sampling techniques: clinical sampling with or without a control group and cohort studies that identify children with and without speech and language impairment within the full population. Studies utilizing the former approach tend to have relatively small samples (e.g., 24 children aged 2862 months in Brookhouser et al., 1979; 63 cases and 48 controls in Fox et al., 2002) with some exceptions (e.g., 177 cases and 923 controls in Tomblin et al., 1997), whereas the latter includes larger samples (e.g., 8,370 Finnish children recruited by Yliherva et al., 2001; 1,911 Australian children reported on by Prior et al., 2008, and Reilly et al., 2006; 207,693 children in the Florida cohort surveyed by Stanton-Chapman et al., 2002). Larger population-based samples have greater variability across predictors, which may mask or blur differences observed in clinical-control samples. The samples also differ by the target age and the age range of the study children. In some studies, children were examined at a specific age (e.g., 36 months in Campbell et al., 2003; 96 months in Yliherva et al., 2001), whereas other studies examined children over a wide age range (e.g., 3060 months in Tomblin et al., 1991; birth to 54 months in Lyytinen et al., 2001). Sampling differences, both in terms of the heterogeneity of the samples (cf. Nelson et al., 2006) and the likelihood of sampling confounds (Zubrick et al., 2007), have constrained the evaluation of risk factors for speech and language impairment in early childhood. Furthermore, it is likely that predictive relationships change over time (Zubrick et al., 2007, p. 1588) such that certain risk factors become more salient at younger or older ages (see Reilly et al., 2006, vs. Reilly et al., 2007; see Table 1). Identification of speech and language impairment. Differences in results also reflect the type and specificity of the speech and language measures used to identify

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the target group. The majority of the reviewed studies considered both speech and language outcomes, but some considered speech outcomes only whereas others considered language outcomes only. Reilly et al. (2006, 2007) found that different outcome measures identified different risk and protective factors. The source of diagnosis also varies, being based on parent or teacher report or on direct assessment of speech and language skills. Although there is evidence of correspondence across these sources (see McLeod & Harrison, 2009, for a discussion), there has not, as yet, been a systematic comparison of risk factors in relation to parent, teacher, and direct assessment sources of identification. Range, number, and analysis of possible predictor variables. Table 1 illustrates the range, number, and type of predictors that were included and analyzed as risk or protective factors in the reviewed studies. Some studies focused on the predictive strength of a small number of domains (e.g., two in Brookhouser et al., 1979, and Whitehurst et al., 1991); others have a broader coverage (e.g., five to seven domains in Campbell et al., 2003; Tomblin et al., 1991, 1997); and some have included most of the identified domains (e.g., 10 in Reilly et al., 2007; 12 in Zubrick et al., 2007). The inclusion of measures from a large number of domains enables the use of multivariate analysis techniques, which have been shown to negate previous findings using bivariate analyses. For example, using multivariate analyses, Reilly et al. (2007) and Zubrick et al. (2007) examined a large number of known risk factors for language delay in two samples of Australian 2-year-olds. In both studies, results confirmed only three or four predictors: male sex, perinatal factors, family history, and presence of siblings in the Zubrick et al. study; and family history, low maternal education, and non-English-speaking background in the Reilly et al. study. Note, however, that a large sample size is required when seeking to test predictors from multiple domains. The discrepancies between the small number of significant predictors identified in multivariate analyses versus the more diverse range of predictors identified in bivariate analyses warrant further investigation. Such work is needed to investigate possible confounding relationships between child biological factors and parental or family factors.

comprehensive range of previously identified risk and protective factors for speech /language acquisition. It also sought to address some of the weaknesses of previous research by focusing on a large population-based sample of children of a similar age who were approaching the beginning of formal schooling and by including a range of sources and types of information to identify speech/language impairment status. This article reports on the second of two studies examining speech and language impairment in a nationally representative population sample of Australian children. The first study (McLeod & Harrison, 2009) determined the prevalence of expressive and receptive speech and language impairment based on four measures: two reported by parents, one directly assessed by trained interviewers, and period prevalence of attendance at speech-language pathology services as reported by parents and teachers. Building on these findings, the present study used the same four measures of speech and language impairment as binomial outcomes to test the effects of a wide range of previously identified risk/protective factors. Bivariate effects for each of these factors were tested, followed by multivariate analyses to identify the best set of predictors from a range of child, parent, family, and community factors.

MethodThe StudyAs with the companion article (McLeod & Harrison, 2009), the current study examined data collected from the kindergarten cohort of children in the first wave (age 45 years) of Growing Up in AustraliaThe Longitudinal Study of Australian Children (LSAC; Sanson et al., 2002). LSAC is the first comprehensive national study of Australian children, funded by the Australian Government to examine childrens health and development over time and within the social, economic, and cultural environments of the families and communities in which they are growing up. Recruitment of the sample using the most comprehensive database of Australias population was facilitated by the Australian Government and the Health Insurance Commission. An overview of LSAC and additional details about sample recruitment and data weighting are provided in McLeod and Harrison (2009). LSAC data were weighted to allow for unequal probabilities of inclusion in the study and to ensure that the LSAC sample matched families in the Australian population with a 4- to 5-year-old child on a wide range of parental and family characteristics, including parents ethnicity, country of birth, education, and income; family size and structure; and whether the mother spoke a language other than English (LOTE) at home. Weighted sample data were used in all the analyses reported in this article.

Aim of the Present StudySeveral groups of researchers (Campbell et al., 2003; Nelson et al., 2006; Reilly et al., 2006, 2007; Tomblin et al., 1991; Zubrick et al., 2007) have sought to identify a set of predictors that would provide a means of identifying children for speech and language assessment, using different analytical techniques and with differing levels of success. The present study sought to extend this work by assessing the unique and collective effects of a

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ParticipantsA total of 4,983 children (2,537 boys; 2,446 girls) and their parents participated in the kindergarten cohort of the LSAC study. Children ranged in age from 4;3 (years; months) to 5;7, but the majority (80%) were within a 6-month age span: 4;6 to 5;0 with a mean age of 56.91 months (SD = 2.64). Parents reported that over 96% of children were attending an early childhood service such as a child care center, preschool, or school (Harrison & Ungerer, 2005; Harrison, Ungerer, et al., 2009). With permission from parents, each childs teacher was approached and asked to complete a brief questionnaire. A total of 3,276 childrens teachers participated.

Risk and protective factors. Risk and protective factors identified by the U.S. Preventative Services Task Force (Nelson et al., 2006; U.S. Preventative Services Task Force, 2006) and our review of these and additional studies (see Table 1) were matched to relevant items in the LSAC dataset. All but the family history of speech and language impairment were available. Television viewing was a risk factor that had not been considered in previous studies but was added to the present study due to its currency in public debate. A total of 31 potential risk/protective factors were identified from parent report. Child factors. Postnatal factors were described by prematurity (defined as < 36 weeks of pregnancy), birth weight (low birth weight defined as < 2500 g), whether the child received neonatal intensive care, and the occurrence of multiple birth versus single birth. Medical conditions identified by parents included ongoing problems of asthma, bronchiolitis, and ear infections. Hearing status was identified by the occurrence of ongoing hearing problems. Oral sucking habits were described by whether the child was breastfed for > 9 months. Child temperament was assessed using the 12-item Short Temperament Scale for Children (STSC; Sanson, Prior, Garino, Oberklaid, & Sewell, 1987). The STSC provides ratings for three subscales: sociability (e.g., This child is shy when first meeting new children), persistence (e.g., This child stays with an activity [e.g., puzzle, construction kit, reading] for a long time), and reactivity (e.g., When shopping together, if I do not buy what this child wants [e.g., sweets, clothing], he/she cries and yells). Items are scored on a scale from 1 = almost never to 6 = almost always and combined to generate a mean score for each subscale. Childrens proficiency in an LOTE was described by two factors: regularly spoken to in a language other than English and speaks a language other than English in the home. Parent factors. Demographic characteristics included mothers age at childs birth and mothers and fathers years of education. Parental minority status or race was recorded if either parent self-identified as being of Aboriginal or Torres Strait Islander background (indigenous status). Parents language status was also self-identified as speaks a language other than English (parents LOTE status). There were 40 different home languages spoken by the parents in this study. English (86%) was the main language spoken at home, followed by Arabic (1.6%), Cantonese (1.3%), Vietnamese (1.0%), Mandarin (0.8%), Greek (0.8%), Italian (0.7%), Samoan (0.5%), Spanish (0.5%), Hindi (0.4%), and other languages. Such cultural diversity is typical of the Australian population. Maternal mental health was measured using the 6-item screening version of the Kessler scale of nonspecific psychological distress (K6; Kessler et al., 2002). The K6 is an effective self-report measure for probing symptoms of anxiety and depression and is a good predictor of mood

MeasuresOutcome measures. Four outcome measures, developed and described in McLeod and Harrison (2009), were used to determine childhood risk status for speech and language impairment. These measures drew on multiple informants, including the childs parent and teacher, and direct assessment by a trained interviewer. 1. Parent report of expressive speech and language concern based on the Parents Evaluation of Developmental Status (PEDS; Glascoe, 2000) question Do you have any concerns about how your child talks and makes speech sounds? (25.2% of the sample was identified as impaired). Parent report of receptive language concern based on the PEDS question Do you have any concerns about how your child understands what you say to him / her? (9.5% of the sample was identified as impaired). Parent and teacher report of use of speech-language pathology services in the past 12 months (14.5% of the sample was identified as impaired). Assessed scores of vocabulary comprehension on the Adapted Peabody Picture Vocabulary TestIII (PPVTIII; Rothman, 2003). Children were identified as having difficulty if they scored more than or equal to 1 SD below the mean (14.7% of the sample was identified as impaired).

2.

3.

4.

There was a low-to-moderate overlap across these four groups. For example, of the children whose parents were concerned about their expressive speech and language (Group 1), 27.2% had parents who were concerned about receptive language, 43% were attending speech-language pathology services, and 22.9% were in the low vocabulary group. The use of four different outcome measures enabled examination of distinct contributions of risk and protective factors to explain the disparate findings summarized in Table 1.

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or anxiety disorders measuring concurrent mental state (Furukawa, Kessler, Slade, & Andrews, 2003; Kessler et al., 2003). Items (e.g., In the past 4 weeks, how often did you feel nervous?) are rated on a 5-point Likert scale (1 = none of the time, 5 = all of the time) and combined to provide an overall mean score. Family factors. SES was described by weekly household income, which was defined as combined yearly income before tax. In addition, a specific index of family financial hardship was determined by asking the primary parent whether he or she had experienced any of seven different types of financial hardship in the past 12 months, such as being unable to pay gas, electricity, or telephone bills on time or going without meals. Summary categories of financial hardship were defined by the total number of indices endorsed by the parent (none, one, two, three, or more). Family size was described by three related variables: the number of children in the household and whether the LSAC child had older siblings or had younger siblings. Home learning activities were determined by asking the primary parent whether he or someone in the household had provided any of seven different types of learning support, such as reading to the child, drawing or doing craft activities with the child, or playing with the child (Australian Institute of Family Studies, 2007). A weekly score was recorded for each index on a scale from 0 = none to 3 = every day and averaged to generate an overall mean. Television watching were reported by the primary parent as the number of hours on typical weekdays and weekend days that the child watches TV or videos. Scores ranged from 1 = does not watch TVor videos to 5 = 5 hours or more. Smoking in the household was recorded if either parent reported that they smoked. Community factors. Neighborhood disadvantage was described by SEIFA score (ABS, 2003), which provides a general indicator of neighborhood advantage or disadvantage based on information collected in the 2001 census for each postcode (zip code). A bivariate measure of community disadvantage was computed, with disadvantage being equivalent to SEIFA scores > 1 SD below the mean.

Logistic regression analysis (Hosmer & Lemeshow, 1989) was used to examine the unique contribution of each of these 31 risk/protective factors to the four measures of speech/language impairment, in which impairment was coded as a binary variable (impaired = 1; nonimpaired = 0). Results report the odds ratio (OR) for each equation. ORs that are above 1.00 indicate that an increase in the predictor increases the odds of impairment, whereas ORs that are below 1.00 indicate that an increase in the predictor decreases the odds of impairment. The closer the OR is to 1.00, the smaller the effect of the predictor. The criteria for significance ( p < .05) of the OR are based on the ORs for the 95% confidence interval (CI) not including 1.00; for example, OR = 1.24, CI [1.05, 1.47] is significant, whereas OR = 1.22, CI [0.83, 1.79] is not significant. A predictor with a significant OR > 1.00 is considered to be a risk factor for being in the impaired group, and a predictor with a significant OR < 1.00 is considered to be a protective factor. See Tomblin et al. (1997) and Zubrick et al. (2007) for further description of the application of ORs to the field of speech-language pathology. Multivariate logistic regression was then used to test the effects of individual predictor variables after adjusting multivariately for the effects of all other child-related, parent-related, family, and community predictors. The full set of variables identified as being significant predictors in bivariate analyses was entered in four separate regression equations, one for each of the outcome measures. These analyses assess the combined predictive effect of the full set of variables, as reported by the Model c2 and the Nagelkerke pseudo R2. The Nagelkerke measure is an analog to the R2 produced by multivariate linear regression and provides an approximation of the percent of variance explained (Tabachnick & Fidell, 2001). Effect size can be assessed by calculating the square-root of the R2 and using Cohens (1988) guidelines: small effect size, R = .1; medium, R = .3; large, R = .5. As noted previously, the effect of each individual variable in the model is indicated by the OR. Results of the analyses also provide three additional statistics: the overall percent of cases classified correctly; sensitivity or percent identification of true positives (impaired group), being the number of true positives divided by true positives plus false negatives; and specificity or percent identification of true negatives (nonimpaired group), being the number of true negatives divided by true negatives plus false positives.

Analysis PlanOutcome measures were four binomial indicators of speech/language risk: parent expressive language concern, parent receptive language concern, use of speech-language pathology services, and low vocabulary comprehension score on the Adapted PPVTIII, each of which identified a speech/language impairment group (impaired) and a nonspeech/language impairment group (nonimpaired). Descriptive analyses determined the frequency (n and % for binomial variables) or mean scores (and SD for continuous variables) for the 31 risk/protective factors for impaired and nonimpaired groups on each of the four outcomes.

ResultsBivariate AnalysesTable 2 presents the results of descriptive and logistic regression analyses for each of the 31 risk and protective factors for the four outcome measures: (a) parent-reported

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Table 2. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired (impaired) and non-speech and language impaired (nonimpaired) groups.Outcome 1: Parent-reported expressive speech and language concern n = 4,980 to 3,197 Risk/protective factors LSAC variable Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 2: Parent-reported receptive language concern n = 4,980 to 3,196 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 3: Use of speech-language pathology services n = 4,179 to 3,183 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 4: Low receptive vocabulary PPVTIII score < 1 SD n = 4,375 to 2,916 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI)

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Sex Postnatal factors

Male Prematurity Birth weight (< 2500 g) Neonatal intensive care Multiple birth

808 (64.4%) 77 (6.4%) 109 (8.9%) 231 (18.5%) 39 (3.1%) 296 (23.6%) 217 (17.5%) 145 (11.6%) 96 (7.6%) 446 (35.9%) 3.64 (1.02) 2.89 (1.00) 3.77 (1.20)

1,743 (46.8%) 152 (4.2%) 223 (6.1%) 513 (13.8%) 101 (2.7%) 754 (20.3%) 572 (15.5%) 254 (6.8%) 71 (1.9%) 1,533 (41.4%) 3.99 (0.93) 2.66 (0.90) 3.84 (1.23)

2.06 (1.802.35) 1.55 (1.172.05) 1.50 (1.181.91) 1.41 (1.191.68) 1.14 (0.781.66) 1.22 (1.041.42) 1.15 (0.971.37) 1.78 (1.442.21) 4.27 (3.125.84) 0.79 (0.690.90) 0.69 (0.640.75) 1.29 (1.201.39) 0.95 (0.901.01)

307 (64.5%) 37 (8.0%) 50 (10.7%) 109 (22.9%) 13 (2.7%) 127 (26.7%) 99 (21.1%) 76 (16.0%) 64 (13.4%) 122 (25.8%) 3.29 (1.10) 3.19 (1.04) 3.74 (1.27)

Child factors 2,244 1.84 (49.8%) (1.512.24) 193 1.88 (4.4%) (1.312.71) 282 1.75 (6.4%) (1.272.40) 635 1.81 (14.1%) (1.442.28) 127 (2.8%) 924 (20.6%) 689 (15.4%) 323 (7.2%) 102 (2.3%) 1,855 (41.4%) 3.97 (0.93) 2.67 (0.91) 3.83 (1.22) 0.94 (0.521.69) 1.41 (1.141.75) 1.47 (1.161.86) 2.46 (1.883.22) 6.67 (4.809.26) 0.49 (0.400.61) 0.50 (0.450.56) 1.73 (1.561.92) 0.94 (0.871.02)

391 (64.6%) 47 (8.0%) 56 (9.3%) 114 (18.9%) 20 (3.3%) 169 (27.9%) 122 (20.4%) 74 (12.2%) 55 (9.1%) 208 (34.7%) 3.67 (1.06) 2.86 (1.01) 3.78 (1.25)

1,760 (49.2%) 150 (4.3%) 223 (6.3%) 517 (14.5%) 103 (2.9%) 733 (20.6%) 543 (15.3%) 263 (7.4%) 86 (2.4%) 1,487 (41.8%) 3.94 (0.94) 2.69 (0.92) 3.83 (1.22)

1.89 (1.582.56) 1.95 (1.392.74) 1.52 (1.122.07) 1.38 (1.111.72) 1.16 (0.721.89) 1.49 (1.231.82) 1.43 (1.151.77) 1.75 (1.332.30) 4.09 (2.885.80) 0.74 (0.610.89) 0.76 (0.690.83) 1.21 (1.101.32) 0.97 (0.901.04)

357 (55.6%) 33 (5.4%) 56 (8.9%) 103 (16.0%) 23 (3.6%) 145 (22.6%) 94 (14.8%) 57 (8.9%) 28 (4.4%) 208 (32.0%) 3.61 (1.01) 3.02 (1.00) 3.64 (1.22)

1,873 (50.2%) 162 (4.5%) 222 (6.0%) 543 (14.6%) 96 (2.6%) 787 (21.1%) 619 (16.7%) 287 (7.7%) 105 (2.8%) 1,570 (42.3%) 3.97 (0.94) 2.65 (0.90) 3.86 (1.22)

1.24 (1.051.47) 1.22 (0.831.79) 1.54 (1.142.09) 1.12 (0.891.41) 1.40 (0.882.23) 1.10 (0.901.34) 0.86 (0.681.09) 1.17 (0.871.57) 1.60 (1.052.43) 0.65 (0.540.77) 0.68 (0.620.75) 1.51 (1.371.66) 0.87 (0.810.93)

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Single vs. multiple births Medical conditions

Asthma Bronchiolitis Ear infections

Hearing status

Oral sucking habits/ breastfeeding Temperament Persistence Reactivity Social

Ongoing hearing problems Breastfed for > 9 months

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Table 2 Continued. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired (impaired) and non-speech and language impaired (nonimpaired) groups.Outcome 1: Parent-reported expressive speech and language concern n = 4,980 to 3,197 Risk/protective factors LSAC variable Impaired n (%) or M (SD ) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 2: Parent-reported receptive language concern n = 4,980 to 3,196 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 3: Use of speech-language pathology services n = 4,179 to 3,183 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 4: Low receptive vocabulary PPVTIII score < 1 SD n = 4,375 to 2,916 Impaired n (%) or M (SD ) Nonimpaired n (%) or M (SD) OR (95% CI)

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Languages spoken

Regularly spoken to in LOTE Speaks an LOTE

219 (17.5%) 216 (17.2%) 29.60 (5.48) 13.92 (2.51) 14.34 (2.42) 4.18 (0.73) 50 (4.0%) 146 (11.8%)

870 (23.4%) 867 (23.3%) 29.85 (5.39) 14.17 (2.68) 14.63 (2.51) 4.34 (0.61) 133 (3.6%) 626 (17.1%)

0.69 (0.590.82) 0.69 (0.580.81) 0.99 (0.981.10) 0.96 (0.940.99) 0.95 (0.930.98) 0.70 (0.630.77) 0.65 (0.540.79) 1.13 (0.811.57)

112 (23.6%) 112 (23.6%) 28.63 (5.77) 13.57 (2.34) 14.09 (2.49) 3.94 (0.81) 30 (6.3%) 83 (17.8%)

Child factors 979 1.11 (21.7%) (0.891.39) 973 (21.6%) 1.12 (0.901.40)

84 (13.9%) 84 (13.9%) 30.10 (5.59) 13.92 (2.43) 14.29 (2.48) 4.23 (0.71) 21 (3.5%) 60 (10.0%)

753 (21.1%) 747 (20.9%) 29.94 (5.26) 14.25 (2.63) 14.66 (2.48) 4.31 (0.63) 121 (3.4%) 514 (14.6%%)

0.60 (0.470.77) 0.61 (0.480.78) 1.01 (0.991.02) 0.95 (0.920.98) 0.94 (0.910.98) 0.83 (0.730.95) 0.64 (0.480.85) 1.05 (0.661.67)

278 (43.3%) 278 (43.3%) 28.81 (5.63) 13.19 (2.54) 13.74 (2.58) 4.02 (0.84) 43 (6.7%) 227 (37.0%)

634 (17.0%) 628 (16.8%) 30.02 (5.22) 14.32 (2.57) 14.71 (2.42) 4.36 (0.58) 103 (2.8%) 408 (11.1%)

3.73 (3.134.46) 3.78 (3.164.52) 0.96 (0.940.97) 0.84 (0.810.87) 0.85 (0.810.88) 0.50 (0.440.57) 4.71 (3.885.72) 2.52 (1.753.65)

Maternal age Maternal age at birth at birth of child of child Educational level Mother s years of parents of education Fathers years of education Maternal mental Maternal health psychological well-being Minority status Parents or race indigenous status Parents LOTE Languages spoken and status proficiency in English

Parent factors 29.91 0.96 (5.36) (0.940.97) 14.17 (2.66) 14.61 (2.49) 4.34 (0.61) 153 (3.4%) 690 (15.6%) 0.92 (0.890.95) 0.92 (0.880.96) 0.47 (0.410.53) 1.17 (0.911.51) 1.89 (1.262.85)

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Table 2 Continued. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired (impaired) and non-speech and language impaired (nonimpaired) groups.Outcome 1: Parent-reported expressive speech and language concern n = 4,980 to 3,197 Risk/protective factors LSAC variable Impaired n (%) or M (SD ) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 2: Parent-reported receptive language concern n = 4,980 to 3,196 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 3: Use of speech-language pathology services n = 4,179 to 3,183 Impaired n (%) or M (SD) Nonimpaired n (%) or M (SD) OR (95% CI) Outcome 4: Low receptive vocabulary PPVTIII score < 1 SD n = 4,375 to 2,916 Impaired n (%) or M (SD ) Nonimpaired n (%) or M (SD) OR (95% CI)

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Socioeconomic Household factors income Financial Financial hardship hardship Family size Number of children in the household Older siblings Younger siblings Home learning Support for activities childrens learning Television TV watching watching (weekdays) TV watching (weekends) Smoking in the Smoking in the household household Neighborhood SEIFA > 1 SD disadvantage below mean

9.95 (2.88) 0.69 (0.74) 2.54 (1.08)

10.33 (2.71) 0.57 (0.70) 2.50 (1.06)

0.95 (0.930.98) 1.27 (1.161.38) 1.03 (0.971.10)

9.27 (2.79) 0.85 (0.76) 2.47 (1.11)

Family factors 10.34 0.87 (2.73) (0.840.90) 0.57 1.65 (0.70) (1.461.87) 2.51 0.97 (1.06) (0.881.06)

10.11 (2.62) 0.63 (0.71) 2.50 (0.98)

10.39 (2.70) 0.56 (0.70) 2.48 (1.03)

0.96 (0.930.995) 1.15 (1.021.30) 1.02 (0.941.11)

9.00 (2.72) 0.78 (0.78) 2.88 (1.34)

10.53 (2.64) 0.55 (0.69) 2.44 (0.98)

0.82 (0.790.84) 1.53 (1.371.72) 1.41 (1.311.51)

773 (61.6%) 573 (45.7%) 1.69 (0.55) 3.13 (0.73) 3.06 (0.86) 154 (14.7%) 186 (14.8%)

2,154 (57.8%) 1,725 (46.3%) 1.71 (0.55) 3.04 (0.72) 2.99 (0.83) 440 (14.2%) 528 (14.2%)

1.17 (1.031.34) 0.98 (0.861.10) 0.92 (0.821.03) 1.20 (1.091.31) 1.10 (1.021.18) 1.05 (0.861.28) 1.05 (.881.26)

249 (52.4%) 234 (49.3%) 1.61 (0.57) 3.17 (0.81) 3.13 (0.93) 84 (21.5%) 88 (18.5%)

2,679 (59.5%) 2,063 (45.8%) 1.71 (0.55) 3.05 (0.71) 3.00 (0.83) 508 (13.5%)

0.75 (0.620.91) 1.15 (0.951.39) 0.71 (0.600.84) 1.26 (1.101.43) 1.21 (1.081.35) 1.76 (1.362.28)

378 (62.5%) 277 (45.9%) 1.74 (0.54) 3.12 (0.71) 3.09 (0.83) 88 (15.4%) 79 (13.1%)

2,066 (57.8%) 1,654 (46.3%) 1.70 (0.55) 3.05 (0.72) 3.00 (0.82) 496 (14.0%) 512 (14.3%)

1.22 (1.021.46) 0.98 (0.831.17) 1.16 (0.991.35) 1.15 (1.021.29) 1.13 (1.021.26) 1.12 (0.881.43) 0.90 (0.701.16)

418 (65.0%) 321 (49.9%) 1.57 (0.55) 3.16 (0.80) 3.15 (0.95) 133 (26.2%) 127 (19.8%)

2,154 (57.7%) 1,729 (46.3%) 1.74 (0.55) 3.03 (0.70) 2.98 (0.80) 372 (11.5%) 476 (12.8%)

1.36 (1.151.62) 1.15 (0.981.36) 0.57 (0.490.67) 1.29 (1.141.45) 1.28 (1.151.41) 2.73 (2.183.42) 1.69 (1.362.10)

Community factors 626 1.41 (13.9%) (1.101.80)

Note. PPVTIII = Adapted Peabody Picture Vocabulary TestIII (Rothman, 2003); LSAC = The Longitudinal Study of Australian Children (Sanson et al., 2002); LOTE = language other than English; SEIFA = Socio-Economic Indexes for Areas (Australian Bureau of Statistics, 2003). Significant outcomes (p < .05) are indicated by bold type.

expressive speech and language concern, (b) parent-reported receptive language concern, (c) use of speech-language pathology services, and (d) low vocabulary comprehension (score < 1 SD below the mean on the Adapted PPVTIII). An examination of the ORs and CIs for these analyses showed that all but 2 of the 31 predictor variables were significant predictors of at least one measure of speech/language impairment. Twenty-two variables were significant for three or four of the four outcome measures. Risk factors for the child were being male, having a history of perinatal complications (prematurity, low birth weight, use of neonatal intensive care), having past and ongoing medical conditions (asthma, ear infections, hearing problems), having a more reactive temperament, and having older siblings. Higher levels of financial hardship and more weekend and weekday television viewing were family risk factors for all four outcomes. Protective factors for the child were being breastfed for > 9 months and having a more persistent temperament. Family protective factors were mothers and fathers having completed more years of education, higher levels of maternal psychological well-being, and higher household income. Predictors that were identified as both a risk and a protective factor for different outcomes were the child speaking or being spoken to in LOTE, and parents being of indigenous background or speaking LOTE. A further seven variables were identified as significant predictors for one or two of the outcomes: childhood bronchiolitis (risk), greater temperamental sociability (protective), older maternal age at birth (protective), larger family size (risk), greater support for childrens learning in the home (protective), smoking in the household (risk), and living in a more disadvantaged neighborhood (risk). Two predictors, being a multiple birth and having younger siblings, were not significant for any of the four outcomes.

ongoing problems with ear infections (OR = 1.41) and hearing problems (OR = 3.18), having a more reactive temperament (OR = 1.13), and having older siblings (OR = 1.26). Reduced odds for being in the impaired group were associated with the following protective factors: having a more persistent temperament (OR = 0.75), mothers having higher ratings for psychological well-being (OR = 0.74), and parents speaking an LOTE (OR = 0.52). The full model accounted for 11.7% ( pseudo R2 = .117) of the variance, which is equivalent to a medium effect size (R = .34; Cohen, 1988). In comparison, Reilly et al. (2007), using multivariate linear regression and a smaller set of significant predictors (4), reported a lower proportion of the explained variance (6.4% and 7.0%) for mothers ratings of their 2-year-olds speech and expressed vocabulary. The proportion of cases classified correctly was relatively low (76.5%). Although the model had high specificity (97.7% of nonimpaired cases were correctly identified), it had poor sensitivity (11.3% of impaired cases were correctly identified). Significant predictors for Outcome 2: Receptive language concern. Seven variables were significant predictors for parental report of receptive language concern. Increased odds were evident for the following risk factors: being male (OR = 1.39), having ongoing hearing problems (OR = 4.43), and having a more reactive temperament (OR = 1.47). Reduced odds were evident for the following protective factors: being breastfed for > 9 months (OR = 0.70), having a more persistent temperament (OR = 0.54), mothers having higher ratings for psychological well-being (OR = 0.66), and the presence of older siblings (OR = 0.67). The full model accounted for 21.5% of the variance. This is equivalent to a moderate-to-large effect size (R2 = .215, R = .46; Cohen, 1988). The proportion of cases classified correctly was relatively high (91.8%), but this was due to the models high specificity (99.5% of nonimpaired cases were correctly identified). The model had poor sensitivity (9.8% of impaired cases were correctly identified). Significant predictors for Outcome 3: Attendance at speech-language pathology. Ten predictors were associated with childrens attendance at speech-language pathology. Increased odds were noted for being male (OR = 1.71), being premature (OR = 1.84), having asthma (OR = 1.33), having ongoing hearing problems (OR = 2.95), having older siblings (OR = 1.39), and spending more time watching television (OR = 1.10). Increased odds were also seen for home support for learning (OR = 1.36), with more support being associated with greater likelihood of being in the impaired group. This finding is difficult to explain in that it may reflect the use of speech-language pathology services by parents who are also more supportive of their childrens learning, but equally it may reflect the additional support that parents are providing at home as a result of their children attending

Multivariate AnalysesMultivariate logistic regressions were used to test the above predictor variables collectively. Variables that were found to be nonsignificant in bivariate tests were excluded. Highly intercorrelated variables were either represented by a single variable (e.g., child speaking or being spoken to in LOTE was dropped and parents LOTE status was retained) or combined (e.g., weekday and weekend television viewing were summed). This process left 26 factors, which were included in four separate multivariate logistic regression analyses. Results are presented in Table 3. Significant predictors for Outcome 1: Expressive speech and language concern. Of the 26 putative risk /protective factors included in the model, 8 achieved significance as a predictor of parent-reported expressive speech and language concern (impaired group). Increased odds for being in the impaired group were associated with the following risk factors: being male (OR = 1.97), having

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Table 3. Multivariate logistic regression: adjusted odds ratio and cumulative amount of variance (pseudo R2) for child, parent, family, and community predictors of speech and language impairment.Outcome 1: Parent-reported expressive speech and language concern Model c2(26, N = 3,197) = 256.22; pseudo R2 = .117 Adjusted OR (p)

LSAC identifier Child factors Male Premature Birth weight ( 9 months Asthma Bronchiolitis Ear infections Ongoing hearing problems Temperament: Social Temperament: Persistence Temperament: Reactivity Parent factors Maternal age at birth of child Mothers years of education Fathers years of education Maternal psychological distress/well-being Parents LOTE status Parents indigenous status Family factors Household income Financial hardship Number of children in the household Older siblings Support for childrens learning at home TV watching (weekdays + weekends) Smoking in the household Community factors SEIFA > 1 SD below mean Proportion of cases classified correctly Sensitivity Specificity Note.

Outcome 2: Parent-reported receptive language concern Model c2(26, N = 3,196) = 314.05; pseudo R2 = .215 Adjusted OR (p)

Outcome 3: Use of speechlanguage pathology services Model c2(26, N = 3,183) = 164.93; pseudo R2 = .092 Adjusted OR (p)

Outcome 4: Low receptive vocabulary PPVT < 1 SD Model c2(26, N = 2,916) = 369.63; pseudo R2 = .239 Adjusted OR (p)

1.97 (