The primary aim of this study was to investigate the accuracy of the Alberta Infant Motor Scale (AIMS) and Neuro-Sensory Motor Developmental Assessment (NSMDA) over the first year of life for predicting motor impairment at 4 years in preterm children. The secondary aims were to assess the predictive value of serial assessments over the first year and when using a combination of these two assessment tools in follow-up.nd-edition (MABC-2). We calculated accuracy of the AIMS and NSMDA for predicting CP and MABC-2 scores ≤15th (at-risk of motor difficulty) and ≤5th centile (significant motor difficulty) for each test (AIMS and NSMDA) at 4, 8 and 12 months, for delay on one, two or all three of the time points over the first year, and finally for delay on both tests at each time point.Children born <30 weeks’ gestation were prospectively recruited and assessed at 4, 8 and 12 months’ corrected age using the AIMS and NSMDA. At 4 years’ corrected age children were assessed for cerebral palsy (CP) and motor impairment using the Movement Assessment Battery for Children 2th and ≤15th) was most accurately predicted by the AIMS at 4 months, whereas CP was most accurately predicted by the NSMDA at 12 months. In regards to serial assessments, the likelihood ratio for motor impairment increased with the number of delayed assessments. When combining both the NSMDA and AIMS the best accuracy was achieved at 4 months, although results were similar at 8 and 12 months.Accuracy for predicting motor impairment was good for each test at each age, although false positives were common. Motor impairment on the MABC-2 (scores ≤5Motor development during the first year of life in preterm infants assessed with the AIMS and NSMDA is predictive of later motor impairment at preschool age. However, false positives are common and therefore it is beneficial to follow-up children at high risk of motor impairment at more than one time point, or to use a combination of assessment tools.ACTRN12606000252516ACTR.org.au  Very preterm infants  have delayed motor development trajectories during the first year of life compared with infants born at term. The deveIt has been recommended that very preterm children have a structured, age-appropriate neuromotor examination at least twice during the first year of life to identify those at risk of future motor difficulties. Whilst tSeveral tools to assess early motor development are available, which vary in clinical utility  and psychometric properties . Several systematic reviews have demonstrated that the General Movements (GMs) assessment and Test of Infant Motor Performance (TIMP) are useful in early infancy for predicting later outcome, however, they cannot be used after 4–5 months of age.,14 The AThe primary aim of this study was to evaluate the accuracy of the AIMS and NSMDA during the first year of life for identifying motor impairment in very preterm children at 4 years. The secondary aim was to examine whether the accuracy of the AIMS and NSMDA improved with repeated assessments over the first year of life. Due to the wide natural variation in motor development seen during the first year of life, several authors have suggested that assessments at multiple time points should be used to guide clinical decisions, rather than making a decision based on a single assessment.–18 NonetThe study cohort was recruited as part of a previously published randomised controlled trial of a preventive care program to improve developmental outcomes, which recruited 120 very preterm infants from the Royal Women’s Hospital or Royal Children’s Hospital, Melbourne, Australia. The firsInfants were assessed at 4, 8 and 12 months’ corrected age at home or the Royal Children’s Hospital, using the AIMS and the NSMDA by one of six physiotherapists or occupational therapists who had attended a training workshop with an author of each of the assessment tools. Reliability between assessors was established prior to the study commencement and all assessors were blinded to the infant’s clinical history.th centile at 4 months and the 5th centile at 8 months based upon the normative data have been shown to be predictive of abnormal development at 18 months based on a sample of 201 infants at risk of adverse motor development.[The AIMS was designed to monitor motor development in infants at risk of central nervous system dysfunction, such as preterm infants, who might display subtle deviations in performance that other motor assessment tools might not be sensitive to detect. It can belopment. Whilst telopment. the purpThe NSMDA was designed in Australia to assess development over a period of time, grading qualitatively and quantitatively the same aspects of neurosensory-motor development from the ages 1 month to 6 years. Items ar<15th centile used to classify the child “at-risk of motor difficulty” and <5th centile “significant motor difficulty”.At 4 years’ corrected age, motor outcome was assessed using the MABC-2 at the Royal Children’s Hospital by one of two physiotherapists who were blinded to the child’s previous assessment results and clinical history. No formal training is required, however, reliability between assessors was established prior to study commencement. The MABC-2 includes three subscales: manual dexterity, aiming and catching, and balance, which are summed to give a total motor score. The MABCA diagnosis of CP was made by the child’s paediatrician and confirmed by the assessing physiotherapist at 4 years. The gross motor function classification system (GMFCS) was used to further classify motor function for children with CP.<10th centile at 4 months and <5th centile at 8 and 12 months on the AIMS, and if the child was categorised in the mild to severe motor dysfunction range on the NSMDA. Sensitivity, specificity, positive and negative predictive values, accuracy, and the positive and negative likelihood ratios, along with 95% confidence intervals (CIs), were used to assess the AIMS and NSMDA at 4, 8 and 12 months as a diagnostic tool for predicting MABC-2 scores at ≤5th and ≤15th centile and CP at 4 years. This analysis was repeated using the number of delayed infant assessments  on the AIMS and NSMDA, and whether they were delayed on both assessment tools at a given time point (i.e. delayed on both) to examine the predictive validity of multiple assessment time points and the combination of the two assessments.All data were analysed using Stata version 13. Summary statistics  were used to compare perinatal characteristics between children who did and did not participate in the 4-year assessment, restricted to those in the substudy of serial motor assessments. Children’s motor development was classified as delayed if they scored <5th centile on the MABC-2. Overall, 26% of the children scored ≤15th centile on the MABC-2. Of the 6 children with CP, 2 had no intraventricular haemorrhage (IVH), 1 had grade I IVH, 1 grade III IVH and 2 had grade IV. Of the remaining 5 children who had grade III/IV, 1 scored ≤5th centile on the MABC-2, 1 scored between 5th-15th centile on the MABC-2 and 3 scored within the normal range.Of the 99 infants, two died (one at 4 months and one at 2 years) and 10 did not return for follow-up, resulting in 87 children (88% of survivors) being assessed for CP at 4 years . Of the The sensitivity, specificity, positive and negative predictive value, accuracy and positive and negative likelihood rations of the AIMS and NSMDA assessments for predicting motor outcomes at 4 years are reported in In regards to serial motor assessments using the AIMS, 48 (60%) children consistently performed within the normal range, whilst 17 (21%) had one delayed assessment, 10 (12%) had two and 12 (15%) had three. For the NSMDA, 59 (72%) children were consistently classified within the normal-minimal motor dysfunction range, 7 (9%) had one delayed assessment, 12 (15%) had two and 9 (11%) had three. The accuracy was best when all 3 time points demonstrated delay rather than just a single assessment, with the positive likelihood ratio for motor impairment increasing with the number of delayed assessments .When combining both motor tools, only 13% were delayed at 4 months on both tools, 22% at 8 months and 16% at 12 months. Four months had the best accuracy for all types of motor impairment, although the results were similar at 8 and 12 months . Of thosThis is the first prospective study to report serial motor assessment of very preterm children using the AIMS and NSMDA during the first year of life for predicting long-term motor outcome at 4 years. The rate of motor delay in infancy differed according to the assessment tool used. The rate of children falling below the cut-offs progressively increased with age during the first year on the AIMS, whilst it peaked at 8 months with the NSMDA. When using a single time point to assess later motor outcome, delay on the MABC-2 at age 4 was most accurately predicted by the AIMS at 4 months, and CP at age 4 by the NSMDA at 12 months. Prediction of motor impairment at age 4 years was improved when results from all 3 assessment time points were utilised. Accuracy was further improved when results from both assessments at each time point were combined.The benefit of serial, longitudinal assessment of motor development in infancy to assist in more accurate prediction and diagnosis of later motor impairment in very preterm children is supported by the current study findings., 22 WhilThe specific assessment tool used to identify motor delay in the first year influences prediction, with the AIMS classifying a larger number of infants with delay at one or more time points than the NSMDA. This may be explained by the AIMS only assessing gross motor development through observation, whilst the NSMDA involves handling the infant to assess a range of motor areas. In additPrediction of motor impairment is challenging due to the multifactorial elements involved in child development, and in both clinical and research settings we are unlikely to be able to predict with absolutely certainty whether a child will go on to have cerebral palsy or another developmental impairment from one assessment alone. It is imThe strengths of the current study include the use of two standardised motor assessments to monitor development of the same cohort of very preterm children over time with high follow-up rates. However, there are some limitations. There were 5 children who were unable to complete the MABC-2 at 4 years due to behaviour issues, which may result in underestimation of motor impairment at 4 years. Long term motor outcome was assessed at 4 years, an important time to assess motor function because children are reaching school age, however the validity of assessments at this age has been questioned. As preteIn conclusion, assessment of infants during the first year of life is an important part of neonatal follow-up programs to examine and predict motor development, and to ensure intervention is targeted to those at greatest risk. The AIMS


Receptive vocabulary development is a component of the human language system that emerges in the first year of life and is characterised by onward expansion throughout life. Beginning in infancy, children's receptive vocabulary knowledge builds the foundation for oral language and reading skills. The foundations for success at school are built early, hence the public health policy focus on reducing developmental inequalities before children start formal school. The underlying assumption is that children's development is stable, and therefore predictable, over time. This study investigated this assumption in relation to children's receptive vocabulary ability. We investigated the extent to which low receptive vocabulary ability at 4 years was associated with low receptive vocabulary ability at 8 years, and the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years. The study sample comprised 3,847 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Multivariate logistic regression was used to investigate risks for low receptive vocabulary ability from 4–8 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. In the multivariate model, substantial risk factors for receptive vocabulary delay from 4–8 years, in order of descending magnitude, were low receptive vocabulary ability at 4 years, low maternal education, and low school readiness. Moderate risk factors, in order of descending magnitude, were low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The following risk factors were not significant: One or more siblings, low family income, not reading to the child, high maternal work hours, and Aboriginal or Torres Strait Islander ethnicity. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in predicting low receptive vocabulary ability from 4–8 years. The initiation and expansion of language from childhood onwards is a universal and remarkable developmental accomplishment Longitudinal studies have shown a consistent pattern of early emergence of disparities in language acquisition that persist over time The rapid growth in receptive vocabulary in the preschool and school years invites a careful consideration of the underlying mechanisms that drive this growth. Necessarily, investigations of predictors of variation in the onset and growth of language abilities require large numbers of children and collection of data on a broad range of candidate predictors. The trade-off is that measures of language ability are generally narrow because direct behavioural assessment of children's language abilities is expensive. Such large-scale studies are rare in the field of language acquisition and largely designed to investigate a number of key developmental outcomes, not just language development. Therefore, in population level studies, measurement is typically restricted to one component of language higher rate of receptive vocabulary growth: Maternal NESB, low school readiness and maternal mental health distress, although these higher rates of growth did not completely close the receptive vocabulary ability gap for children with and without these risks. The only risk factor associated with a lower onward rate of growth was socio-economic area disadvantage, which was actually not a risk for low receptive vocabulary ability at the intercept  A recent study of receptive vocabulary growth from 4–8 years showed substantial variation in receptive vocabulary ability at 4, 6 and 8 years and that predictor significance and strength also varied over time In a study that used data from the 1970 British Cohort Study, low receptive vocabulary at 5 years was a significant risk factor for low functional literacy in adulthood What remains unknown is the extent to which low language ability persists or improves beyond toddlerhood, and the extent to which other factors in the child's wider developmental environment are associated with this in the preschool and early school years. This study had two aims. First, to investigate the extent to which low receptive vocabulary at 4 years was associated with onward low receptive vocabulary at 8 years. Second, to estimate the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years toward the prediction of low language ability at 8 years. This study offers a rare assessment of a comprehensive set of risk factors for the persistence of low receptive vocabulary in kindergarten- and school-age children in a representative population level sample.The Longitudinal Study of Australian Children (LSAC) is conducted in a partnership between the Australian Government Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The study has ethics approval from the Australian Institute of Family Studies Ethics Committee. The Ethics Committee is registered with the Australian Health Ethics Committee, a subcommittee of the National Health and Medical Research Council (NHMRC). Caregivers gave written informed consent to the survey. As the study children were all minors at the time these data were collected, written informed consent was obtained from the caregiver on behalf of each of the study children. The signed consent forms are retained by the field agency (ABS).http://www.dss.gov.au/our-responsibilities/families-and-children/programs-services/growing-up-in-australia-the-longitudinal-study-of-australian-children-lsac. Accessed 2014 Feb 7Aug 2).Confidentialised LSAC data are publicly available. Researchers can apply to the Australian Government Department of Social Services for permission to access and use Longitudinal Study of Australian Children (LSAC) data . The LSAC is a national longitudinal study that commenced in 2004. Data collection for the study is led by a consortium of expert Australian researchers The study uses a cross-sequential design of biennial face-to-face visits with the family and study child. In this study we used data from the child cohort collected at 4 and 8 years. The child cohort comprised 4,983 children at 4 years and 4,331 children at 8 years. Response rates to the outcome measure, the Adapted Peabody Picture Vocabulary Test-III (PPVT), and the candidate predictors varied; 4,406 children completed the PPVT at 4 years, 4,273 completed the PPVT at 8 years; and 3,847 children completed the PPVT at 4 and 8 years.The LSAC sampling frame was extracted from the Medicare Australia enrolment database, which was validated to ensure coverage of Australian children within the target age-range. The initial study sample was designed to be representative of Australian children within the selected age cohort, proportional to the regional distribution of children in the Australian population. An initial sample size of 5,000 was chosen as to ensure there would still be a sufficient sample for detailed analysis after attrition over the number of years of the longitudinal study.The study entailed a two-stage clustered design, first selecting postcodes then children within postcodes. Stratification was used to ensure proportional geographic representation for states/territories and capital city statistical division/rest of state areas. Cluster sampling was utilised because it provides a cost effective way to conduct face-to-face interviews, as well as an opportunity to collect and analyse community-level effects. Postcodes were selected with probability proportional to size selection where possible, and with equal probability for small population postcodes. Children were selected from 311 postcodes Analyses show that the initial sample was broadly representative of the general Australian population when compared with 2001 Census data, but slightly under-representative of families who were single-parent, non-English speaking and living in rental properties. Attrition somewhat increased these biases. For example, the overall attrition rate between ages 4 and 8 was 13%, but children with mothers classified as Non-English speaking background decreased from 15.7% at age 4 to 13.8% at age 8, an attrition rate of 23%. The proportion of mothers who had a year 11 or less education decreased from 39.2% at age 4 to 36.5% at age 8, an attrition rate of 19% The selection of measures used in this paper is guided by the LSAC ethos, empirical and/or theoretical associations with language development, and our previous work in the area.The LSAC was funded as part of the (then) Department of Family and Community Services Stronger Families and Communities Strategy, which aimed to establish partnerships to strengthen families and communities and develop and deliver solutions at a local level Our variable selection in this paper is guided by our previous work in this area Our previous work, using a multi-level modelling approach to receptive vocabulary growth, revealed some counter-intuitive findings Our response variable is a measure of one the major component systems of language development – the semantic system. Children were assessed with the Adapted Peabody Picture Vocabulary Test-III (PPVT), a test of receptive vocabulary designed for the LSAC study The Adapted PPVT-III was administered directly to each child during the home interview. For each word presented, the child was shown a card containing four pictures and was asked to point to the picture corresponding to the word . Scaled scores for the Adapted PPVT-III were used in all analyses. In this study we wanted to examine whether children's receptive vocabulary, relative to their peers, at age 4 (51–67 months) is predictive of their receptive vocabulary, relative to their peers, at age 8 (95–119 months). Our previous work To do this we created age-groups of approximately equal size within the 4-year and 8-year age-cohorts see . We thenth percentile in PPVT performance. This enabled us to ask whether, relative to their peers, having low receptive vocabulary at 4 years is predictive of their receptive vocabulary at 8 years.Finally, as we were interested in the risks of low receptive vocabulary, we defined the children in each of the age 4 and age 8 cohorts as having low receptive vocabulary where they fell at or below the lowest 15Who Am I? scores as our reference group. The analytic sample for each candidate predictor varied somewhat, depending on item completeness.A total of 29 candidate predictor variables were used. These were grouped into child, maternal, and family and home environment characteristics. All predictor variables were measured when the child was 4 years of age. Predictors were modelled as risk variables with the lowest level of risk as the reference category see . For eacThe child characteristics in our models were: Gender, ethnicity, birth weight, ear infections, school readiness and temperament. There were equal proportions of girls and boys in the sample. A small proportion of children  were of Aboriginal and/or Torres Strait Islander decent and were coded to distinguish them from those who were not. Primary carers were asked to report their child's birth weight which was subsequently coded into those children who were born with low birth weight  and those who weighed more than this (> = 2500 grams). A single item indicator of ongoing ear infections at 4 years was included.Who Am I?Who Am I? of 0.31 for the LSAC children at age 4. In this report, study children have been grouped into quintiles of performance based on the total Who Am I? score with high quintiles representing higher levels of performance.In addition to the Adapted PPVT-III, each study child was directly assessed at 4 years using the Child temperament was measured at 4 years with the Short Temperament Scale for Children (STSC) The maternal characteristics in our models were: Age at the birth of the child, problematic alcohol use, smoking, mental health distress, education, hours of paid employment and parenting. The biological mother's age at the birth of the child was grouped into categories representing teen birth (< age 20 years), 20–39 years and 40 or more years at birth with the vast majority of mothers (94.5%) of study children in the age range 20–39 years.Information on current tobacco and alcohol was gathered from the mothers. We defined problematic alcohol use where women reported their daily alcohol consumption to exceed 2 standard drinks and/or where they reported frequent binge drinking of 5 or more alcoholic drinks at least 2–3 times per month with 12.0% being so classified. Study children's mothers were asked about tobacco use and also categorised as either current smokers (21.8%) or not current smokers.In this study, we used the Kessler-6 (K-6) scale to measure maternal non-specific psychological distress. Women with scores of 8 or more were classified as having symptomatic psychological distress. This threshold is consistent with other studies In Australia, at the time of this study, 10 years of education was compulsorily mandated. Maternal education in years was grouped into three levels according to those who had completed 11 years (36.4%), 12 years (32.9%), and those who had completed more than 12 years (i.e. University education) (30.7%).Mothers were variously employed at the time when the children were first measured. We used total hours of paid maternal employment to distinguish mothers who were not in paid employment (0 hours), from those in part time paid employment  and in full time paid employment . Similar proportions of women were either not in paid employment (41.0%) or working part time (45.5%) with the remainder (13.5%) working full time.The parenting characteristics of both parents were measured in a self-complete form, using four measures of parenting warmth, hostility, consistency and inductive reasoning developed for the LSAC The characteristics of the family home environment in our models were: NESB, family structure, number of siblings, income, health care card, financial hardship, socioeconomic disadvantage, reading to the study child, playgroup and child care. As the focus of this study is explicitly on English language development and because language development is known to vary where more than one language is spoken in the home, we used the mother's Non-English Speaking Background (NESB) as a general indicator for language other than English spoken in the household at 4 years. About 14% of mothers were predominately non-English speaking at the time of the interview.With respect to family composition, two variables were selected as candidate predictors of vocabulary development: Family structure (sole parent vs. other) and number of siblings . About 11.5% of the study children were living in single mother families at 4 years. The majority had one or two siblings (49.5% and 27.4% respectively) or were singletons (10.9%) at age 4. The study questionnaire did not permit the establishment of birth order.Families were asked to report their total weekly family income from all sources. Responses were partitioned into relatively equal quintiles: those families earning under $600, $600–$999, $1000–$1499, $1500–$1999, and $2000 or more per week. In Australia where income falls below a defined threshold and/or certain hardship criteria are met families also qualify for a health care card. About 19.7% of LSAC families had a health care card and this is used as an indicator of financial need in the LSAC families. Additionally, an indicator of family hardship was also derived where families reported, due to shortage of money over the last 12 months that: they had not been able to pay gas, electricity or telephone bills on time; they had not been able to pay the mortgage or rent on time; adults or children had gone without meals; they had been unable to heat or cool their home; they had pawned or sold something; or sought assistance from a welfare or community organisation. About thirty per cent of families reported at least one of these occurrences in the previous 12 months at 4 years.An area measure of socioeconomic disadvantage was also estimated for each participating family. The family home was coded with Socio-Economic Indicators for Area (SEIFA) disadvantage, indexed in quintiles – lower quintiles represent greater levels of disadvantage. The neighbourhood SEIFA disadvantage index summarizes information from the Australian Census of Population and Housing as this relates to economic and social disadvantage in small areas, such as low income, low educational attainment and high unemployment Several indicators of the child's learning environment were gathered. The frequency with which the primary caregiver read to the study child was assessed via face-to-face interview. A total of 129 (3.0%) parents reported not reading to the child at all, 802 (18.8%) reported reading 1 or 2 days a week, 1,255 (29.4%) reported reading to the child 3–5 days a week, and 2085 (48.8%) reported reading to the child daily. Mothers were asked if their child had attended a playgroup in the 12 months prior to the 4-year interview with about one third indicating this to be the case. Finally, hours per week in child care or early education were identified by asking the primary carer “how many hours a week on average does the child go to school, kindergarten, pre-school, and/or day-care?”. A total of 258 (6.3%) attended 8 or less hours a week, 2,705 (65.8%) attended 9–20 hours a week, 818 (19.9%) attended 21–30 hours a week, and 332 (8.1%) attended 31or more hours a week.Who Am I?) at age 4, 25.7% were in the low PPVT group at age 8. In contrast, of the 935 children in the most favourable quintile for school readiness (Who am I?) at age 4, only 8.3% were in the low PPVT group at age 8. Of the 511 children who scored in the low PPVT group at age 4, 37.6% were in the low PPVT group at age 8, while of the 612 children tested in the middle-high PPVT group at age 4 only 2.6% were in the low PPVT group at age 8. The latter observation is important as it illustrates that while low PPVT at age 4 indicates an increased risk of low PPVT at age 8, the majority of children with low PPVT at age 4 moved into the middle-high group at age 8.The data were analysed using logistic regression in SAS 9.3 Our analysis proceeded in four steps. First, we used logistic regression to estimate the association between each of our candidate predictors and PPVT group  at 8 years. Second, we grouped candidate predictors by their estimated effect size. Third, we established a multivariate logistic regression model. Finally, we undertook a sensitivity-specificity analysis to examine the predictive utility of our multivariate model.To establish the effect sizes for the various predictors we used the odds ratios as estimated by logistic regression. For logistic regression, the odds ratio itself represents the effect size of interest Of the 29 candidate predictors, twelve were above the odds ratio cut-off of 2.0. In order of increasing magnitude of effect size these were: Low maternal work hours (2.00), NESB status (2.17), Aboriginal and/or Torres Strait Islander status (2.67), low persistence (2.75), low maternal parenting consistency (2.77), socio-economic area disadvantage (2.97), low school readiness (3.79), low maternal education (3.87), four or more siblings (3.97), low family income (4.15), child not read to at home (4.86), and low PPVT score at 4 years (5.32).Nine candidate predictors were statistically significant in the bivariate model, but below the OR 2.0 cut-off: Problematic alcohol use (1.37), financial hardship (1.39), maternal hostility (1.56), single mother status (1.56), maternal mental health distress (1.6), maternal smoking (1.67), high temperamental reactivity (1.75), low temperamental sociability (1.77), and having a health care card (1.88).The remaining eight candidate predictors of low PPVT at age 8 were not statistically significant in the bivariate model: male gender, low birthweight, ear infections, teenage mother status, low maternal inductive reasoning, low maternal warmth, not attending playgroup, and daily use of non-parental child care.The twelve predictors meeting the initial cut-off criterion of an odds ratio of 2.00 or higher in the unadjusted analyses were then included in the multivariate model see .In the multivariate model, low PPVT at 4 years, low maternal education and low school readiness showed moderate or higher adjusted effects on the odds of low PPVT at 8 years. The odds of low PPVT at age 8 were 3.49 times higher for children who had low PPVT at age 4 relative to children who had middle-high PPVT at age 4; 2.55 times higher for children whose mothers had only completed year 11 of Australian Schooling relative to children of University educated mothers; and 2.50 times higher for children in the lowest quintile of school readiness at age 4 relative to those in the highest quintile of school readiness.The following risk factors had adjusted effects on low PPVT at 8 years below our criterion of moderate effect size (2.0): Low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The odds of low PPVT at age 8 were 1.89 times higher for children of mothers in the lowest quintile for maternal parenting consistency relative to those children of mothers in the highest quintile of parenting consistency; 1.78 times higher for children living in the most disadvantaged neighbourhood relative to those children in the least disadvantaged neighbourhoods; 1.65 times higher for children who were in the lowest quintile of temperamental persistence relative to those in the highest quintile; and 1.59 times higher for children of mothers who spoke a language other than English relative to those children whose mother spoke English.The following risk factors were not statistically significant in the multivariate model: Aboriginal and/or Torres Strait Islander status, low maternal work hours, low family income, child not read to at home, and one or more siblings.Odds ratios and statistically significant associations do not necessarily lead to clear predictive relationships. Inevitably, researchers and practitioners turn their attentions towards asking how our knowledge of the associations demonstrated here might affect decisions about screening children for low language ability, predicting the persistence of this status, and offering interventions. Our intention in this section is not aimed at producing a practical algorithm for screening children for low language status per se. That would be premature. What we do want to do though, is widen the developmental description beyond just the empirical documentation of language growth and its associations. This entails including assessing the implications of the emerging evidence of high intra-individual variability in the growth of language, as measured here, the relatively low level of association between language measures and important predictors, and what this might portend for those interested in early detection and intervention. To do this, we include analyses of the predictive utility of these estimates.We have provided the odds ratios for each predictor measure in In contrast, the multivariate model estimates the probability that each study child has low PPVT at age 8, based on all of the selected candidate predictors at age 4. The multivariate model had an ROC of 0.75, and shows that the addition of other candidate predictors along with the age 4 PPVT performance improves discriminative utility – in other words, including additional predictors in the model has improved how well the model discriminates cases from non-cases. However, in order to evaluate predictive utility the researcher needs to establish a cut-point at which a child is classified as a case or non-case  Following examination of the ROC curve see , a probaSo, at a practical level, if decisions were being made about offering ongoing professional monitoring of developmental status, or offering intervention, our model is good at predicting those children who will not have low receptive vocabulary status at age 8. In other words, we know who is less likely to need monitoring or intervention. Admittedly, of the children we predicted at age 4 to not have low receptive vocabulary at age 8, we will have misclassified 5.9% of these who actually go on to have low receptive vocabulary at age 8. This might be deemed a tolerable miss-rate. In contrast though, for the children at age 4 who were predicted to go on to have low receptive vocabulary at age 8, 74.2% of these children would actually go on to be classified as not having low receptive vocabulary at age 8. In other words, it is possible that considerable resources will be deployed towards children who, in all likelihood, won't need intervention. Decisions would need to be made as to whether this represents a tolerable deployment of monitoring and/or intervention to assist the 25.8% of children classified at age 4 who would otherwise have a low receptive vocabulary status at age 8.We would note that it is possible to select different threshold criteria for classification. Changes in the selection of the threshold criterion produce changes in onward sensitivity and specificity, and these in turn, affect positive and negative predictive values. Relaxing the threshold identifies at age 4 more of the children who actually go on to be classified as having low receptive vocabulary at age 8, but at the consequence of identifying a larger number of ultimately unaffected children. For example, a cut-off of 0.05, increases the sensitivity (92.2%), decreases specificity (28.2%), decreases positive predictive value (15.7%), and increases negative predictive value (94.3%). In contrast, increasing the threshold criterion to 0.20 decreases the sensitivity (50.0%), increases specificity (86.1%), increases positive predictive value (34.1%), and decreases negative predictive value (92.2%). While our criterion was selected to balance sensitivity and specificity, the establishment of the threshold criterion rests with decisions and values about the cost and effectiveness of monitoring and/or treatment, the outcome benefits, and the consequences of not intervening.Vocabulary acquisition is a major component of language development and a central marker of the semantic system. In this study we describe the extent to which low language ability, as measured by receptive vocabulary, persists or improves beyond toddlerhood, and the extent to which other factors in the child's wider developmental environment are associated with this in the preschool and early school years. To do this, we estimated the extent to which low receptive vocabulary at 4 years was associated with onward low receptive vocabulary at 8 years and estimated the contribution of child, maternal and family risk factors to predicting from age 4 onward low receptive vocabulary at 8 years.The results show substantial variability in receptive vocabulary performance in the epoch from age 4 to age 8 with weak – at best, modest – contributions from child, maternal and family factors. This finding in children aged 4 to 8 mirrors the existing studies demonstrating high variability in vocabulary acquisition in children in the toddler range. The poor positive predictive value of persistent low language ability from toddlerhood has been attributed to the immaturity of the emergent language system whereby low language ability is a transient variant of typical language development for most children. The results of our study suggest that, for most children, low receptive language ability at 4 years is also a transient variant of typical language development. Therefore, the catch-up pattern observed in follow-up studies of two-year-olds, was also evident in this study of language outcomes for four-year-olds.Our findings also demonstrate that the configuration of risks for low receptive vocabulary ability from 4–8 years was quite different to the configuration of risks in studies of toddlers. In toddler studies, maternal and family environment risk factors are conspicuous for their absence. Instead, child risk factors dominate the etiological models. These risk factors are subtle and best characterised as nonclinical neurobiological vulnerabilities that result in a slower start in language acquisition There are limitations to these findings.First, in developing our multivariate logistic model, we set an odds ratio of 2 to represent a criterion effect size of substantive interest. This is not a commonly used device, particularly with logistic regression, and other approaches in predictor selection and retention could have been taken. For example, researchers commonly choose to include all statistically significant variables initially screened for subsequent entry to the multivariate model. To test the robustness of our approach, we repeated the analysis and included all statistically significant variables from the bivariate modelling into a multivariate model, and then used a stepwise elimination of non-significant variables until a final model was achieved. The results of this analysis were consistent with the results presented in this paper. There were no substantial changes in the pattern of effects or in the model's predictive utility. To test whether the model's predictive utility could be improved by a more inclusive set of predictors, and by treating predictors as continuous rather than categorical where appropriate we also developed a predictive logistic model using all 29 candidate predictors. This model represented a marginal improvement on the final model presented in this paper, but did not change the pattern of findings in a meaningful fashion.Second, our modelling only permits examination of the predictive utility of a single language component – semantics, as measured by receptive vocabulary. We accept that a clinical diagnosis of language impairment is based on psychometric assessment of multiple dimensions of language as well as naturalistic observation and information about the child's communication in the home and other environments such as school. We do not recommend our model as a clinical screening tool, but as an illustration of the difficulties in effectively implementing screening in a population representative sample based on a broad range of child, maternal and family characteristics, without extensive and time-consuming testing. Further, different dimensions of language  have different growth characteristics that produce different patterns of growth over time. For example, children's acquisition of grammar proceeds in known steps towards the adult grammar. This means that children's performance can be interpreted in relation to progress towards a known developmental endpoint – that is, mastery of the adult grammar Third, it could be argued that interests in and concerns about the persistence of low language ability and its prediction in the period from 4 to 8 focusses “too late” in the language development trajectory. And yet, what we demonstrate here is that the high variability in language acquisition and the weak predictive associations about language development empirically documented in infants and toddlers is also being observed in the epoch from 4 years to 8 years. This is substantively important in shaping our thinking about the nature of language variability. It is also the case that what happens in language development from 4 to 8 years is equally important in terms of onward lifecourse outcomes and has a direct bearing on readiness to learn.Fourth, it could be argued the predictive utility of our models would have been enhanced had we included more extensive measures of child cognitive or language ability at age 4, or if we had combined measures into composites to reduce measurement error. We cannot comment in detail on the predictive utility of measures which were unavailable to us, but would note that the variables included in the LSAC were based upon known risk factors from established longitudinal studies and a consideration of those variables which could meaningfully impact policy interventions Finally, it could be argued that as we have no measure of any remedial treatment, speech therapy or other interventions which children in our study have received between ages 4 and 8 we understate the efforts of parents and educators in assisting those children who started with low receptive vocabulary. If our model had included measures of intervention between ages 4 and 8, we would likely have a stronger predictive model of receptive vocabulary at age 8. However, whilst this seems a plausible limitation, this study was explicitly aimed at examining predictors from age 4 on age 8 receptive vocabulary. Further, this does not change our thesis that variability in children's language development is an important developmental phenomenon, and that the predictive utility of models of language is under-examined.Notwithstanding these limitations, what are the implications of these findings?First, much of the extant literature on language development trajectories is focussed on documenting the typical variability in language growth and assessing its predictors. This work is relatively silent about the utility of these predictors. Some of this reticence reflects methodological constraints which preclude offering these insights. But where there are good longitudinal data, of well described samples and populations, with relatively robust and replicated measures, some advance should be made in extending the implications of the findings towards demonstrating what they might practically mean. How do these associations guide planning, funding, and arranging services and interventions?In prompting for more of this, we would suggest that the high developmental variability in receptive vocabulary performance from age 4 to age 8 will make accurate identification of children at age 4 for intervention on the basis of their low receptive vocabulary performance and a mix of child, maternal and family risks, challenging. We found the strongest predictor of low receptive vocabulary at age 8 was low receptive vocabulary at age 4. However, despite a moderate odds ratio with vocabulary ability at 8, receptive vocabulary was nonetheless a limited predictor of persistently low receptive vocabulary ability at 8 years. Dale et al., reported comparable findings, in a population level sample of young twins from 2–4 years In addition to low age 4 receptive vocabulary, we found that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in improving prediction of low onward receptive vocabulary ability at age 8. While we are not proposing that the specific model we have fitted here be used for screening general populations, the empirical literature on the prediction of language outcomes for unselected populations of children is not encouraging Finally, some readers may be concerned that we are dismissive of the risks for persistent language difficulties. This is far from the case. In a recent Australian study, the financial costs associated with parents accessing health services for pre-schoolers with communication disorders were substantial The emergent finding in studies of language growth trajectories is that variability in language growth needs to be seriously considered. Those who start below age expectations may not be those who continue to stay behind age expectations. Children need to be monitored throughout their development. The “best” predictor may actually be the persistence of low language performance over time rather than status at a given point in time. Our findings illustrate the tension between stability and change over time. This needs to be considered when observing developmental phenomena, particularly in a population representative sample rather than a sample of children already identified as ‘at risk’. Whilst it is difficult to predict with great accuracy which children will have poor language at age 8, it is also clear that some children are exposed to far greater risk than others.By school age, English-speaking children have reached adult levels of competence in some dimensions of language  while other dimensions such as receptive vocabulary ability continue to develop throughout life. Once children start formal schooling, learning to read and write become new dimensions of the language system. While oral language is the substrate for literacy, literacy also facilitates receptive vocabulary knowledge by giving children access to experiences, and words to describe those experiences, beyond the day to day. It will be important for future research to determine the extent to which low receptive vocabulary ability from 4 to 8 years projects to low literacy ability and the configuration of risks for low literacy ability.


Animal research has established that effects of hormones on social behaviour depend on characteristics of both individual and environment. Insight from research on humans into this interdependence is limited, though. Specifically, hardly any prior testosterone experiments in humans scrutinized the interdependency of testosterone with the social environment. Nonetheless, recent testosterone administration studies in humans repeatedly show that a proxy for individuals’ prenatal testosterone-to-estradiol ratio, second-to-fourth digit-ratio (2D:4D ratio), influences effects of testosterone administration on human social behaviour. Here, we systematically vary the characteristics of the social environment and show that, depending on prenatal sex hormone priming, testosterone administration in women moderates the effect of the social environment on trust. We use the economic trust game and compare one-shot games modelling trust problems in relations between strangers with repeated games modelling trust problems in ongoing relations between partners. As expected, subjects are more trustful in repeated than in one-shot games. In subjects prenatally relatively highly primed by testosterone, however, this effect disappears after testosterone administration. We argue that impairments in cognitive empathy may reduce the repeated game effect on trust after testosterone administration in subjects with relatively high prenatal testosterone exposure and propose a neurobiological explanation for this effect. A growing body of evidence from social neuroscience and neuroeconomics recognizes that testosterone has powerful effects on social behaviour in humans12467269106Testosterone has also been related to aggressive behaviour in rodents. Research has shown that testosterone underlies many features of human social behaviour but does not trigger aggressive and antisocial behaviour per se. Two approaches on testosterone effects in humans have emerged. An approached not pursued in this article maintains that the hormone induces behaviours that are motivated by seeking dominance and status in social interactions1158171861While consensus is emerging that behavioural effects of testosterone in humans and effects of social environment are interrelated61519222324262715The trust game is played by two subjects, one in the role of an investor, the other in the role of a trustee . The inv26If the trust game is played only once between the investor and the trustee, a selfish trustee will abuse trust by not sharing whatever has been received due to an investment of the investor, since sharing would be costly. Anticipating such behaviour of the trustee, the investor has an incentive not to invest in the first place. In the repeated game, incentives and requirements for cognitive empathy are differentThe difference between the one-shot and the repeated game models a key feature of trust problems in human interactions. Repeated interactions in ongoing relations are a feature of many economic and social exchanges. Theory and empirical research, including many experimental studies, also show they are a major driving force of trustfulness and trustworthiness6P = 0.375, n = 81). Since effects of testosterone administration on cognitive empathy seem to depend on priming through prenatal testosterone, we included effects of 2D:4D ratio in the analyses. 2D:4D ratio has been established as an individual marker for differences in prenatal testosterone-to-estradiol ratioOur experiment was a double-blind, placebo-controlled study , employi8222366Studying how trust in humans is affected by the interplay of biological factors and the social environment required a complex experimental design. The comparison between one-shot and repeated games was crucial. Each subject played six one-shot trust games with six different partners and a repeated game of six rounds with one and the same partner. The two series of games resembled each other as much as possible. The series had to be long enough to ensure sufficient opportunities for conditional behaviour and reputation building in the repeated game. Subjects were informed about behaviour of their partner after each game in both series of games. Half of the subjects started with the one-shot games, the other half with the repeated game. Each subject played in the same role throughout all games. As a result, we had twelve observations per subject, six in the one-shot games and six in the repeated game, with a total of 480 observations from 40 investors and 492 observations from 41 trustees  nor to 2D:4D ratio . The multivariate model including the interaction between the administered substance and 2D:4D ratio did not explain behaviour in the dictator game  = 0.52, P = 0.671). As a further task, and between the two series of trust games, subjects had to make decisions in a series of seven incentivized gamblesP = 0.902, n = 81) nor with 2D:4D ratio . The model including the interaction between these two variables was likewise insignificant  = 0.27, P = 0.850).Altruism may affect behaviour in trust games23P < 0.001). This is consistent with many earlier findings on trust gamesP = 0.605) between the testosterone (AME = 1.30) and the placebo group (AME = 1.65) as such. Comparing the group with above-median 2D:4D ratio (AME = 1.13) with below median 2D:4D ratio (AME = 1.92), we found that the 2D:4D ratio as such had likewise no effect on investments . To study the dependence of the effect of testosterone administration on 2D:4D ratio, we included interaction effects between the continuous variable for the 2D:4D ratio, the testosterone condition and whether the game was one-shot or repeated in the multilevel regression analysis , while this effect did not appear in the group that received testosterone and had low 2D:4D ratio . This likewise confirmed our hypothesis and was in line with recent results showing that the effect of testosterone administration on cognitive empathy depends on 2D:4D ratioP = 0.020).To facilitate interpretation of the results and qualify the interaction effects with 2D:4D ratio further, we split both the testosterone and placebo group in two subgroups of subjects with either above or below median 2D:4D ratio and compared the average marginal effects of playing the repeated game separately for these four groups . It thenP = 0.804). Also, the 2D:4D ratio did not change the effect of being in the repeated game. Subjects with a low 2D:4D ratio shared 5.21 MU more on average in the repeated game and the subjects with a high 2D:4D ratio shared 5.69 MU more. These groups are again based on a median split of the 2D:4D ratio. The difference was again not significant . We then considered the combination of testosterone administration and the 2D:4D ratio .For the analysis of sharing behaviour of the trustee, we needed to also control for investors’ behaviour because trustees would, on average, share more when they received more. In addition, the relation between investments and sharing might depend on whether the one-shot or repeated game was played. Indeed, if the investor invested nothing, the trustee returned virtually nothing in the one-shot game (0.2 MU on average) and in the repeated game (0.9 MU), while the trustee returned 11.5 MU in the one-shot and 20.5 MU in the repeated game when the investor invested 12 MU. Thus, we did find that trustees shared more in the repeated game. Averaged over all investments by investors, trustees shared 7.3 MU more in the repeated than in the one-shot games. We did not find that administering testosterone had an effect on the difference in sharing between one-shot games and repeated games. The AME for the testosterone group was 5.37 MU and for the placebo group 5.60 MU. The difference between these effects was not significant , administration of testosterone moderates the effect of the social environment on trust behaviour. Thus, the interplay of biological factors and characteristics of the social environment affects human interactions, including interactions with important implications for economic and social life. While claims are not new that it is the interplay of biological and social factors, rather than only their separate effects, that shapes behaviour, supporting such claims with experimental evidence requires biological as well as social factors to be varied systematically. Our results correspond to prior research also showing that relatively high prenatal testosterone exposure boosts the negative effect of testosterone administration on cognitive empathyOur results are well in line with previous studies6As noted, 2D:4D ratio is interactively shaped by testosterone and estradiol in utero4776777635Remaining questions are whether genomic or fast acting non-genomic mechanisms are involved in the present effects of testosterone on social behaviour, and what brain structures might be involved7In sum, both genomic and non-genomic mechanisms are hypothetically involved in effects of testosterone administration on a broad range of social and emotional behavioursFinally, testosterone seems to act acutely on social behaviour by inducing changes in the functional connectivity of the brain404546474840How to cite this article: Buskens, V. et al. Testosterone Administration Moderates Effect of Social Environment on Trust in Women Depending on Second-to-Fourth Digit Ratio. Sci. Rep.6, 27655; doi: 10.1038/srep27655 (2016).


Substantial evidence indicates that exposure to electromagnetic fields (EMF) above certain levels can affect human health through triggering some biological responses. According to WHO, short-term exposure to EMF at the levels present in the home/environment do not cause any apparent detrimental effects in healthy individuals. However, now, there is a debate on whether long-term exposure to low level EMF can evoke detrimental biological responses. Although based on the Communications Act of 1934, selling, advertising, using, or importing mobile jammers which block cell phone calls and text messages are illegal acts, in some countries these devices are being used for security purpose and for prevention of cheating during examinations. In this study 30 male Wistar rats were randomly divided into 3 groups of 10 each. The control group received no radiation. The sham exposure group was exposed to a switched-off jammer device. After fasting for 12 hours, the exposure group was exposed to EMFs at a distance of 50 cm from the jammer. Blood samples were collected from the tail vein after 24, 48 and72 hours and fasting blood sugar was measured by using a common blood glucose monitor . The significance level was considered 5% and SPSS Ver. 21 was used for statistical analysis. The data were analyzed by ANOVA followed by Tukey’s test. A statistically significant difference was observed between blood sugar level in the control and exposure groups after 24, 48 and 72 hours of continuous irradiation . No significant difference was found between the level of fasting blood sugar in control and sham groups. Short-term exposure to electromagnetic field generated by mobile phone jammer can reduce blood sugar level in adult male rats. These findings, in contrast with our previous results, lead us to this conclusion that the use of these signal blocking devices in very specific circumstances may have some therapeutic effects. However, further studies have to be performed to find out the exact mechanism by which Jammer EMFs reduce fasting blood sugar. Nowadays, mobile phones are widely used in our daily life. The broad use of cell phones has led to some global concerns about the safety of these devices. Mobile phone use may be problematic in some cases. The solution for this problem is using mobile phone jammer devices which block cell phones. These devices are known as cell phone jammers or “GSM jammers”. A GSM jIn recent years, the effect of electromagnetic fields generated by mobile phones-10, mobiThe jammer (model MB06) used in this study was capable of blocking mobile communications within distances ranged up to 40 meters. Thirty male rats, weighing 200-250 g, were randomly divided into three groups of 10 each; consisting the control, sham (exposed to a switched off jammer device), and case group (exposure to jammer radiation). Case group were placed in restrainers, at the distance of 50 cm from the Jammer, for 24, 48 and 72 hours (continuous irradiation). The animals were maintained in a standard environmental condition and were fed with standard diet and tap water. All animal experiments were approved by the animal experiment ethics committee of the Shiraz University of Medical Sciences.  The animals were deprived of food for 12 hours since 6 PM until 6 AM in the next morning. Blood sampling was performed from tail vein at 8 AM. All statistical tests were carried out at the significance level of 0.05 using SPSS version 21. The data were analyzed by ANOVA followed by Tukey test. In this study, there was a significant difference between fasting blood sugar level in the control and case groups after 24 , 48  and 72  hours of continuous irradiation by a Jammer device. However, there were no significant differences between the level of fasting blood sugar in control and sham groups in adult male rats. Furthermore, the duration of exposure was not linked to the level of fasting blood sugar. The effects of exposure to electromagnetic fields produced by activated mobile phones on fasting blood glucose have been studied before. Meo and Rubeaan showed that rats exposed to mobile phone radiation for durations longer than 15 min/day for 3 months revealed a significant increased fasting blood glucose level compared to the control group. These researchers concluded that long-term exposure to activated mobile phone can be associated with the increase in fasting blood glucose in rats. Havas rThe result of this study is not in line with the findings of other researchers who reported increased glucose level after exposure to electromagnetic field. This inconsistency may be due to different durations of exposure to EMF; which was a short-term 24-72 h) exposure in our study, while it was long term (10-90 days) exposure in other studies. It has been shown that binding capacity of insulin to its receptors was reduced 87% after exposure to pulsed electric fields, which leads to increased blood sugar-72 h exp. In anotShort-term exposure to electromagnetic fields of jammer device leads to decreased blood glucose level, which may occur via increased brain activity and therefore increased brain glucose metabolism.