We systematically reviewed the evidence for the interaction of microsatellite instability status (MSI) and treatment with 5FU in colorectal cancer to determine how well MSI status predicts health outcomes in patients undergoing 5FU-based chemotherapy.We conducted a search of four electronic databases through June 2013. We considered studies that included both colorectal cancer patients treated with 5FU-based chemotherapy and untreated patients with survival outcomes presented by MSI status.We identified 16 studies for qualitative analysis  with 14 studies eligible for meta-analysis. The microsatellite stable (MSS) group showed an effect of 5FU treatment on disease-free survival  and overall survival , indicating that MSS patients who received 5FU treatment had longer survival than MSS patients who were untreated. The effect of 5FU treatment was not statistically significant for microsatellite high (MSI-H) patients for disease-free survival  or overall survival . However, the summarized point estimates of the effects of 5FU treatment for the MSS and MSI-H groups were not different at a statistically significant level.Our analyses indicate that treatment with 5FU-based chemotherapy improves disease-free and overall survival in CRC patients, but that there is no difference in the effect of treatment based on MSI status. Therefore, the use of MSI status to guide treatment decisions about the use of 5FU treatment for CRC has no significant benefits for patients.The online version of this article (doi:10.1186/s12885-015-1093-4) contains supplementary material, which is available to authorized users. MLH1 gene in sporadic CRCs, but can also occur through inherited mutations in any one of four genes . Tumors with MMR deficiency exhibit a high frequency of microsatellite instability (MSI-H) because these regions of the genome are particularly unstable and susceptible to errors that do not get corrected because of the defective MMR system [MLH1, MSH2, and MSH6, and PMS2 [Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States; about 1 in 20 Americans will develop CRC in their lifetime. Although the death rate from CRC has been dropping, it remains the third leading cause of cancer-related deaths [R system . Tumor Mand PMS2 .In advanced stage CRC patients, the fluoropyrimidine 5-fluorouracil (5FU) has been the most widely used chemotherapeutic agent since the late 1950s . RecentlThere is substantial data on the relationship between MSI status and prognosis in CRC patients. A meta-analysis with 7,642 cases demonstrated that patients with MSI-H tumors have a significantly better prognosis than those with MSS tumors [We systematically reviewed the evidence for the interaction of microsatellite instability status (MSI) and treatment with 5FU in colorectal cancer to determine how well MSI status predicts health outcomes in patients undergoing 5FU-based chemotherapy.We considered studies that included both CRC patients treated with 5FU-based chemotherapy and untreated patients with survival outcomes presented by MSI status. We accepted either MSI or IHC testing to indicate MSI status. While any treatment containing 5FU therapy was included, we required that >90% of patients received 5FU in the treatment group for inclusion of the study. Eligible study designs included randomized controlled or controlled clinical trials and prospective or retrospective cohort studies. We excluded studies that did not report disease free survival (DFS) or overall survival (OS) outcome separately for patients based on MSI and treatment status .Systematic literature searches were performed in the following databases through June 27, 2013: MEDLINE, PubMed, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. The search strategy was built to comprehensively capture the literature related to MSI and chemotherapeutic agents in the context of CRC treatment. Search details are provided in the supplementary materials  was evaluated, and incomplete if one to three proteins were tested.Testing was classified by ‘completeness.’ MSI testing was considered complete if at least the 5 marker NCI panel was tested according to NCI standards. Testing using fewer markers was considered incomplete. For IHC, testing was considered complete if the presence of proteins for all four genes  FOLFOX, FOLFIRI, or CAPOX were classified as multi-drug treatment.Studies were classified depending on the type of cancer for the patient, including colon only, rectal only, and colon/rectal combined.When possible, we recorded outcomes data from multivariate analyses that adjusted for other potentially confounding variables. If only univariate analysis was reported, we recorded these results instead, but also retained an indicator of which analysis is reported.The hazard ratio (HR) and 95% confidence interval was abstracted from each article where possible. In cases where the HR was not reported, the value was estimated using the methods described by Tierney et al.  HR analyWe conducted random-effects meta-analyses to estimate the pooled effect size of disease-free survival (DFS) and overall survival (OS). We used the metan procedure in Stata 11.2®  for all 2 statistic as a measure of statistical heterogeneity. We applied the Cochrane Collaboration’s rules of thumb for interpreting I2: less than 40 percent likely represents unimportant heterogeneity, 30 to 60 percent represents moderate heterogeneity, 50 to 90 percent represents substantial heterogeneity; above 75 percent indicates considerable heterogeneity among the studies [We examined the I studies . We did DFS is defined in various ways throughout the CRC literature. Measures of DFS vary in their beginning time point  and included endpoints . In addition to studies of DFS, other studies reported related surrogate endpoints including recurrence-free survival or time to recurrence. Similar variation exists in the measurement of overall survival (OS) in oncology literature. Some studies of survival include only cancer-specific deaths while others include all-cause deaths. It is unclear whether this lack of consistency between trials may alter the findings of a systematic review . In ordeThe literature search identified 815 articles for evaluation against the eligibility criteria. We excluded 624 articles based on review of the abstract, and retrieved and evaluated 191 full text articles. After review, 16 studies were included for qualitative analysis and 14 studies were eligible for meta-analysis  that would have otherwise been included. For DFS, there were 6 articles that were retained for the meta-analysis of HR -25. For All papers were rated as fair quality with the exception of one paper  that wasOverall, nine studies were classified as having complete testing for MSI status ,29,33-35Characteristics of the eligible studies are provided in Table Studies were included in the analysis regardless of patient stage at diagnosis. Although in most cases the proportion of patients at each stage was reported, results were not stratified by stage, so results are reported for all stages combined. An analysis focused on stage II cases is presented after the results that included all stages combined. Most studies included both rectal and colon cancer.patients who received 5FU treatment had longer DFS than patients who did not receive this treatment. We did not detect evidence of heterogeneity across studies for the MSS group for the HR analysis .Forest plots of the effect of 5FU treatment on DFS by MSI status are shown using HRs Figure . For the2 = 13.9%, p = 0.33). The sample size for the MSI-H group is smaller than the sample size for the MSS group (651 vs. 4434), and the confidence interval for each study is wider for the MSI-H group compared with the MSS group.In contrast, the effect of 5FU treatment on DFS was not statistically significant for MSI-H patients with a pooled HR of 0.84 . We did not detect evidence of heterogeneity across studies for the MSI-H group . One study  reportedWe conducted sensitivity analyses to determine whether the results of the analysis are affected by the definition of the DFS outcome. The primary analysis we presented above is the broadest and most inclusive, and incorporated all studies that presented a HR of anything described by the authors as DFS or regression free survival (RFS), which could include substantially different definitions. In sensitivity analyses, we restricted the analysis only to studies that include death as an event for DFS and used the FDA definition for DFS ,22. When2 = 79%, p < 0.001). The largest estimate of the effect of treatment was seen in the study with the shortest followup [Forest plots of the effectiveness of 5FU treatment on OS by MSI status are shown using the HR Figure . 5FU trefollowup . In gene2 = 66%, p < 0.001). This heterogeneity across studies may be driven by lower estimates of the effect of treatment for two studies [The effect of 5FU treatment on OS was very similar for MSI-H patients with a pooled HR of 0.66 , although the difference between those treated and not treated with 5FU was not statistically significant in this group. There was evidence of substantial heterogeneity across studies for the MSI-H group . Two studies ,35 reporAs with DFS, we conducted sensitivity analyses to determine whether the results of the analysis were affected by definition of the OS outcome. In sensitivity analyses, we used increasingly restrictive definitions: 1) only studies that use all-cause mortality ,26,31,33We assessed the relationship between MSI status and treatment with 5FU in stage II cases only, because the NCCN guidelines recommend MSI testing for treatment decisions only in this group . There wRibic et al.  reportedIn order to incorporate data from as many studies as possible, we also considered all of the studies ranked by percentage of stage II patients. We did not observe a pattern suggesting a relationship between the percentage of stage II patients and response to 5FU by MSI status  and OS  for CRC patients, they had inconclusive results for MSI status as a predictive marker of response to treatment with 5FU chemotherapy. Similar to our analysis, they determined that there was a significant beneficial effect of 5FU chemotherapy in patients with MSS tumors . However, the sample size for patients with MSI-H tumors in their analysis was small (n = 396). The point estimate for the effect of 5FU treatment in MSI-H patients was similar to what we found  for OS, and there was significant heterogeneity across studies (p = 0.03), so no clear conclusion could be reached. Our analysis included a larger population of 1293 MSI-H patients; however, statistically significant heterogeneity was still identified in the overall survival analysis.Our results expand upon the results of a previous meta-analysis of the same topic by Guastadisegni et al. , which iDes Guetz et al.  also repbetter response to 5FU chemotherapy than MSS patients  or subjects with different stages of CRC.A previous study addressed this question and reached seemingly different conclusions. Des Guetz et al.  conducteThe data that we report do not support the recommendation for determination of MSI status in stage II CRC patients. One guideline recommends determination of MSI status for all patients with stage II disease because stage II MSI-H patients may have good prognosis and do not benefit from 5FU treatment . This guThere were several limitations to our meta-analysis. First, not all studies reported hazard ratios or counts of events, which were the primary data necessary to include the studies in the meta-analysis. In some cases, we were able estimate the HR value using the methods described by Tierney et al. . We condNone of the studies reported in our primary meta-analysis used contemporary treatment regimens that include 5FU in combination therapies, such as FOLFOX or FOLFIRI. This is a strength of the available data, since it is then possible to explore the ability of MSI status to predict 5FU treatment response in isolation, and without contamination of the effects of other treatments given simultaneously. However, it is not clear that these findings represent the effect of MSI status on response to 5FU in combination therapy.17 have been associated with improved survival in patients treated with 5FU-based chemotherapy [We did not consider whether molecular subtypes of MSI-H patients experience differential response to 5FU chemotherapy. Greater heterogeneity of MSI-H tumors is now recognized  with varotherapy ,57. FutuFinally, despite the increase in sample size for this analysis compared with previously reported meta-analyses on this topic, the number of MSI-H patients available for analysis remains limited, with substantially larger confidence intervals and lower statistical power compared with the findings for the MSS group. This limitation is coupled with an evidence base that relies primarily on retrospective study designs that have a greater potential for selection bias, and substantial  heterogeneity of effect sizes reported in studies. One ongoing prospective study was identified in our search for additional clinical trials and meeting abstracts: The Eastern Cooperative Oncology Group (ECOG) E5202 Trial  will assign treatment arms for stage II colorectal cancer patients based on MSI status in conjunction with 18q LOH. Those patients who are considered high risk (based on MSS and 18q LOH) will be randomized to FOLFOX6 with or without bevacizumab. Patients considered low risk due to retention of 18q alleles or MSI-H status will receive observation alone. While no publications were identified for this study, the clinical trial record indicates that primary data collection is completed.We conclude that the results of this meta-analysis do not support the use of MSI status for the direct benefit of the patient in guiding decisions about the use of 5FU treatment for CRC. Testing for MSI status among CRC patients is becoming increasingly common , given e


Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable.Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. Children’s language development builds the foundation for literacy, educational achievement and employment , 2. LiteImproving language and literacy standards is an important global  and natiThe predominant theory about reading, “The Simple View of Reading,” was first advanced by Gough and Tunmer  and is pEffective early language and literacy intervention promotes children’s code-related and oral language abilities . DecodinMost longitudinal studies of literacy outcomes for children and adults with a history of low language ability have been designed to quantify the persistence of low abilities over time , 18, 19.On close inspection, it is possible to find studies of evidence of change of literacy ability over time. An early study of Phillips et al  noted maThese observations of instability in the emergence and establishment of literacy have a parallel in recent studies of early vocabulary acquisition. Taylor et al.  and ChriGiven the documented association between early vocabulary development and subsequent reading ability we investigate here the onward strength of this association and its predictive utility. Specifically we investigate the uncertainty about the early developmental variability in onward language and literacy and whether this reduces and converges as children age and acquire skills. To do this we use a large representative sample of Australian children to estimate the longitudinal predictive relationship between vocabulary status at ages 4, 6 and 8 years and onward literacy performance at age 10. We focus on three questions: 1) What are the patterns of stability and change in children’s progression from early vocabulary development to their onward literacy; 2) what are the predictors of this progression, and; 3) what is the predictive utility of this relationship?The Longitudinal Study of Australian Children (LSAC) is conducted in a partnership between the 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.growingupinaustralia.gov.au/data/dataaccess.html Accessed 29 June 2015).Confidentialised LSAC data are publicly available. Researchers can apply to the Commonwealth of Australia Department of Social Services (DSS) for permission to access and use Longitudinal Study of Australian Children (LSAC) data , and had data for the key variables (language and literacy) at each wave of the survey. The analytic sample was less than the wave 4 sample, as not all children responded to all study items. The sample size thus varied slightly between analyses, based on item response.The LSAC is a national longitudinal study that commenced in 2004. 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, 6, 8 and 10 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 , 24.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 . AttritiThe measure of language ability was the Adapted Peabody Picture Vocabulary Test-III (PPVT) and the measure of literacy ability was the Academic Rating Scale: Language and Literacy Subscale (ARS).The Adapted Peabody Picture Vocabulary Test-III (PPVT) is a test of receptive vocabulary designed for the LSAC study . The AdaThe Academic Rating Scale: Language and Literacy Subscale (ARS) is a teacher-report measure of literacy ability that has been demonstrated to validly assess developmental skill levels consistent with theories of early literacy acquisition . The ARSIn this study we wanted to examine the relationship between language and literacy over time. However, previous work by Taylor et al  has estaTo age standardise, we created age-groups of approximately equal size within the 4-year, 6-year, 8-year, and 10-year age-groups see . ChildreFinally, as we were interested in stability and change in receptive vocabulary and literacy, we identified the lowest 15% (‘low’) within each age-group. This enabled us to examine the extent to which being low-performing in receptive vocabulary, relative to their peers at ages 4, 6 and 8 years, is predictive of their onward teacher-rated language and literacy at 10 years. Taking a categorical approach to language and literacy is complementary to growth curve modelling . It allo4) possible combinations of language development across time. What These groupings of language development can be split into six broad patterns: 2 patterns of stability and 4 patterns of change. Broadly, children can either be stable in their language (i.e. the same position at each age) or they can change (i.e. they change position at least once between ages). The two stable patterns were a stable middle-high pattern  and a stable low pattern . The most common developmental pattern was the stable middle-high pattern, 69% of the children were middle-high at all 4 ages. One of the least common developmental patterns was a stable low pattern. Only 26 children  were persistently in the low group at ages 4, 6, 8 and 10.Change patterns are also evident in The candidate predictor measures used in this paper have been extensively described elsewhere .Briefly, a bioecological model of child development , 33 guidIn addition to measures of PPVT at ages 4, 6 and 8, another 28 candidate predictor variables were used, giving a total of 31 predictors. These were grouped into child, maternal, and family and home environment characteristics. All candidate predictor variables were measured when the child was 4 years of age. Candidate predictors were modelled as risk variables with the lowest level of risk as the reference category see . The anaThe child characteristics in our models were: Gender, ethnicity, birthweight, 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 descent and were coded to distinguish them from those who were not. Primary carers were asked to report their child’s birthweight which was subsequently coded into those children who were born with low birthweight  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?(WAI) [Who Am I? score with high quintiles representing higher levels of performance.Each study child was directly assessed at 4 years using the  I?(WAI) . The 11  I?(WAI) ). It has I?(WAI) . There i I?(WAI) . In thisChild temperament was measured at 4 years with the Short Temperament Scale for Children (STSC) . The STSThe 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 with the vast majority of mothers (94.8%) 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. Study children’s mothers were asked about tobacco use and also categorised as either current smokers (21.2%) or not current smokers.In this study, we used the Kessler-6 (K6) 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 –40 usingIn 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 or less (35.8%), 12 years (32.8%), and those who had completed more than 12 years (i.e. University education) (31.4%).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 (40.3%) or working part time (46.4%) with the remainder (13.3%) 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 . We use The characteristics of the family home environment in our models were: maternal non-English speaking background (NESB), family structure, sibship size, 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 NESB as a general indicator for language other than English spoken in the household at 4 years. About 12% 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 10.4% of the study children were living in single mother families at 4 years. The majority had one or two siblings (50.2% and 27.4% respectively) or were singletons (10.5%) 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. 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 18.3% 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 family 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 summarises 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 . These dSeveral 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 88 (2.6%) of parents reported not reading to the child at all, 598 (17.9%) reported reading 1 or 2 days a week, 1,018 (30.5%) reported reading to the child 3–5 days a week, and 1,630 (48.9%) reported reading to the child daily. Mothers were asked if their child had attended a playgroup in the period 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 199 (6.2%) attended 8 or less hours a week, 2,136 (66.2%) attended 9–20 hours a week, 633 (19.6%) attended 21–30 hours a week, and 258 (8.0%) attended 31+ hours a week.The data were analysed using logistic regression in SAS 9.3 . The surOur analysis proceeded in four steps. First, we used logistic regressions to estimate initial unadjusted associations and effect sizes between our individual candidate predictors and ARS group  at 10 years. Second, we grouped the candidate predictors by their unadjusted estimated effect size. Third, we set a criterion for selecting from these unadjusted effect sizes the final predictors to use in estimating 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 size for the selection of multivariate predictors in our final model we used the odds ratios see  as estabOf the 361 children who scored in the low PPVT group at age 4, 31.6% were in the low ARS group at age 10, while of the 2,663 children tested in the middle-high PPVT group at age 4 only 11.7% were in the low ARS group at age 10. This corresponds to an odds ratio for the low PPVT group, 3.4 times greater than that for children in the middle-high PPVT group. This latter observation is important. It illustrates that while low PPVT at age 4 carries with it an increased risk of low ARS at age 10, the majority of children with low PPVT at age 4 go on to achieve middle-high performances at age 10.Of the 31 initial unadjusted candidate predictors of low literacy , fifteenIn order of increasing magnitude of effect size these were: Mother smoker (2.06), socio-economic area disadvantage (2.34), single-mother family (2.41), health care card (2.46), high reactivity (2.73), low PPVT score at 6 years (3.07), low maternal parenting consistency (3.25), low maternal education (3.25), low PPVT score at 4 years (3.43), child not read to at home (3.48), low persistence (3.59), low PPVT score at 8 years (3.75), low family income (4.10), Aboriginal and/or Torres Strait Islander status (4.92), and low WAI school readiness (11.16).Nine candidate predictors of low literacy were statistically significant in the bivariate model, but fell below the OR 2.0 cut-off: 1 sibling vs. only child (0.58), problematic alcohol use (1.46), maternal hostility (1.56), ear infections (1.69), SC male (1.75), maternal mental health distress (1.81), mother not in work force (1.86), and financial hardship (1.87).Eight candidate predictors of low literacy were not statistically significant in the bivariate model: daily use of non-parental child care, teenage mother status, low birthweight, not attending playgroup, low maternal inductive reasoning, low sociability, low maternal warmth and mother NESB.The fifteen predictors of low literacy meeting the cut-off criterion of an odds ratio of 2.00 or higher in the unadjusted analyses were subsequently included in the multivariate model see .In the multivariate model three predictors exhibited substantial associations with subsequent low ARS at age 10: Low school readiness (WAI), Aboriginal and/or Torres Strait Islander status, and low PPVT at 8 years. Children in the lowest quintile for school readiness were at 7.35 the odds of children in the highest quintile of school readiness for low ARS at 10 years. Aboriginal and/or Torres Strait Islander children were at 2.94 the odds of non-Indigenous children for low ARS at age 10. Finally, children with low PPVT at 8 years were at 2.20 the odds for low ARS at 10 years, relative to children with middle-high PPVT at 8 years.Moderate effects on low ARS at 10 years were observed with three more predictors: High temperamental reactivity, low PPVT at 4 years, and low PPVT at 6 years. Children in the most reactive quintile for temperamental reactivity at 4 years were at 1.69 the odds of children in the lowest quintile of temperamental reactivity for low ARS at 10 years. Children with low PPVT at 4 years were at 1.55 the odds for low ARS at 10 years, relative to children with middle-high PPVT at 4 years. Finally, children with low PPVT at 6 years were at 1.42 the odds for low ARS at 10 years, relative to children with middle-high PPVT at 6 years.The remaining nine risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and neighbourhood disadvantage.Odds ratios and statistically significant associations do not necessarily lead to clear predictive relationships. The fit of logistic regression models can be estimated via the area under the Receiver Operating Curve (ROC) statistic; Hanley and McNeil  recommenSingle binary predictors in a logistic regression model permit the calculation of sensitivity, specificity, positive predictive value and negative predictive value based on the presence or absence of that predictor. That is, when considering a single binary predictor, there is single binary cut-point for predicting cases from non-cases. When considering low PPVT at age 4 as a predictor of low ARS at age 10 as in , childreIn However, when considering a multivariate model, information from different predictors can be combined and a variety of cut-points can be chosen. Thus, to select a cut-point requires judgements that concern the utility of the model. These judgements result in trade-offs between sensitivity and specificity and between positive predictive value and negative predictive value depending on the gravity  of misclassification. A cut-off threshold of probability for predicting whether the study child will have low ARS at age 10 must be set by the investigator.In the absence of clear utility guidelines for setting a criterion cut-off for our model, we examined the ROC curve see  and set There were 2316 children in the model at age 4 . At the Conversely, by the time the 2316 children were assessed at age 10, 2013 were actually found to have middle-high ARS. In line with our criterion cut-off, the model predicted that 1438 of these children would have middle-high ARS (sensitivity = 71.4%). This level of specificity also carried with it the classification of another 91 children as not having low ARS group at age 10 when, in fact, these children had low ARS on examination at age 10. This model thus produces a negative predictive value of 94.0% (1438/2316).Other criterion could be chosen. For example, by choosing a stricter cut-off such as 0.05, sensitivity increases to 92.7% but decreases the specificity to 36.6%. Or, by relaxing the cut-off to 0.18 the sensitivity of our model decreases to 59.1% but specificity to increases to 81.6%. However, these adjustments all entail varying levels of positive and negative predictive power.Understanding factors in early childhood that promote and impede children’s progress along the oral-literate continuum at school has long been a primary concern of educational policy. What typically happens to children as they age from 4 to 10 years in their progression from early and rapid vocabulary acquisition to their onward and early literacy performance? The aims of this study were to identify developmental patterns of vocabulary and literacy development in this age period, to assess associated predictors of early literacy in this age range, and estimate their predictive utility. The strength of this study resides in a well characterized sample with measures selected, calibrated and independently collected longitudinally on the same children from multiple informants.Six patterns of vocabulary development to onward early literacy were longitudinally observed across 4 biennial assessments from age 4 to 10. The most common pattern was a stable developmental pattern in which children commenced with, and then maintained, middle-high vocabulary ability that progressed to an outcome of middle-high literacy ability. This pattern fit 69% of children.Collectively, another 30% of the children exhibited four change patterns. These comprised: 1) an improving pattern of vocabulary development towards onward literacy for 8% of children; 2) a declining pattern for another 10% of children; 3) a fluctuating low pattern for 2% of children; and, 4) a fluctuating middle-high group for 10% of the children.One of the least common developmental patterns was a stable pattern comprising initial and subsequent persistent low vocabulary progressing to an outcome of low literacy abilities. Less than 1% (26/2792) of children in our study fit this pattern.Our findings allow an early and rare look at the transition point from early vocabulary development to onward early school literacy. We demonstrate considerable positional instability in the vocabulary development of this cohort and model its relationship to literacy performance at age 10. The onward literacy performances of these children will become apparent as successive waves of data are gathered. At this point though, our findings are similar to those of Verhoeven et al who reported the association between early vocabulary and word decoding to be consistently significant but only weak .In addressing this initial instability of literacy development, previous research in very young children has focussed principally upon the growth of literacy. Using both cross-sectional and longitudinal samples of young children and measuring their oral and reading vocabulary, these studies have demonstrated very rapid developmental growth of literacy in the epoch from pre-school to Year 4 –56. In lThere is some research however that has highlighted developmental instability of literacy with longitudinal samples. Leppänen et al (2004) specifically investigated the variability in reading performances of pre-schoolers as they moved into early primary school . They deWhile scant, other examples of developmental instability in literacy performance exist. Using a small sample of 187 children followed from Year 1 to Year 6 Phillips et al., 2002 . In a siIn examining early predictors for low literacy ability at age 10 we identified three substantial contributors with adjusted effects meeting our criterion: Low school readiness at age 4, Aboriginal and/or Torres Strait Islander status and low vocabulary ability at age 8. High child temperamental reactivity had a moderate predictive relationship with low literacy at age 10 as did low Vocabulary at ages 4 and 6.Who Am I? at age 4. The finding that pre-academic early copying and writing skills substantially overshadows low vocabulary ability as a risk factor for low literacy at age 10 is consistent with other studies that have shown that explicitly taught pre-academic skills that children bring to school make a major contribution to their academic progress at school [The highest risk for low literacy ability at age 10 was low school readiness as measured by the t school . In the taught  their children about reading and printing words were uncorrelated and showed distinct pathways into language and literacy: “…storybook reading was related to children’s receptive language development, whereas parents’ reports of teaching were related to children’s early literacy skills (p. 455)”. Our findings that a child’s pre-academic early copying and writing skills at age 4 substantially predict onward literacy at age 10 parallels these findings. It is also notable that in our model, neither maternal education nor income was predictive of onward low literacy. We would speculate that in the presence of a more proximal measure of direct inputs to the child’s early abilities (as measured by the WAI) both maternal education and family income—which are proxies for human capital and material resources—become non-significant.First, Sénéchal and LeFevre  have shoany one domain of the EDI at age 5 increased the risk of scoring in the bottom 20% of these standardised national measures of Reading in Grades 3, 5 and 7. This provides necessary evidence of the multidimensionality of school readiness.Second, school readiness is a multidimensional construct. It comprises physical health and wellbeing, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge. The WAI measures more multiple dimensions of school readiness relative to the PPVT, which measures one dimension . In total population studies of Australian 5 year olds using the Australian Early Development Index (EDI), Brinkman et al.  demonstrOur study shows that measuring multiple dimensions of school readiness with the WAI at age 4 in the year before the start of formal school, and one year earlier than EDI, identifies vulnerability for literacy measured 6 years later when the children are age 10. Children’s literacy development begins long before the start of formal school and the magnitude of risk associated with low school readiness at age 4 for low literacy at age 10 provides strong evidence of the importance of the preschool period for reducing inequalities in literacy acquisition at school through the provision of developmentally enriched opportunities and programs.The second substantial risk for low literacy performance was carried by children of Aboriginal and Torres Strait Islander descent. At the outset it is important to distinguish these children from the Australian population of Indigenous children: The LSAC sample excluded children living in remote areas of Australia and was not designed to representatively sample Indigenous children nor procure sufficient numbers of Indigenous children from which to derive stable population estimates . The WaDifferential attrition has affected this subsample over the four waves. One hundred children of Aboriginal and Torres Strait Islander descent (n = 100) were available for our analysis. In this sample, 98% of the mothers reported that English was their primary language. This leaves 2 of our Aboriginal and Torres Strait Islander children effectively coded as having a “Non-English speaking mother at Wave 1.” The higher risk for poor performance on the ARS estimated for Aboriginal and Torres Strait Islander status in our sample is not likely to be attributable in large part to second language use within the home.What then more plausibly explains the higher risk for poor onward literacy among the Aboriginal and Torres Strait Islander children in this sample? The challenging and confronting circumstances that have beset politicians, administrators and practitioners in improving the life prospects and capabilities of Australia’s Indigenous peoples are well described , 67. OurThe third substantial predictor of literacy at age 10 was vocabulary ability at age 8. While the age 4 and age 6 measures of vocabulary showed moderate and progressively weaker associations with literacy relative to the more proximal age 8 measure of vocabulary, all retained significance in the multivariate model.In line with Sénéchal and LeFevre’s (2002)  observatWe have previously documented the high variability in rates of children’s vocabulary growth and the volatility in their positional change over time , 22. OurThe final remaining predictor of any significance was temperamental reactivity. In our model early temperament predicts onward low literacy—but only in so far as this pertains to reactivity  rather than persistence (focusing and working on one thing). The effect of temperament on language development has a mixed career with some researchers finding higher rates of temperamental problems in children with language delay –72 and oThe findings of low predictive utility between vocabulary development, an important marker of language development, and onward literacy ability reinforce a general view that the early selection and exclusive targeting of children on the basis of risk indicators will miss a large proportion of children who are not performing low at the point of selection but who will go on to sustain low outcomes at later points. Such predictions will at the same time, deliver into the initial selection of low performing children, a substantial number who will developmentally improve without intervention.This means that although we identified substantial risks with adjusted odds ratios of greater than 2.0, the analysis of predictive utility  indicates that these risk factors do not function well to predict who will and will not have low literacy ability at age 10. We would note that researchers often conflate odds ratios with predictive utility, in that interventions are discussed in light of odds ratios similar to those in this paper, without giving consideration to the utility of the predictive relationship. We would encourage other researchers in the field to consider this distinction more carefully.We acknowledge that our models use a statistical, not a diagnostic or clinical definition, of low vocabulary and low literacy status. With this said though, our presentation of the estimates of poor predictive utility does not carry with it the intention of dismissing the importance of demonstrated significant predictors of literacy nor a desire to undermine the pursuit and implementation of interventions to address literacy in young children. Low early school readiness, Indigenous status, and low prior vocabulary development heighten the risks of progression towards low literacy competency.However, what our estimates of clinical utility do illustrate is “about” how hard it is to predict the literacy status of 10 year olds from a reasonable set of well measured variables selected with the theoretical and practical intent to enable this. The findings show that ‘late’ identification is inevitable for children whose low performance is not evident earlier in development. In this study, 10.2% of children showed a declining pattern of development. That is, they shifted from adequate performance at an earlier age to low performance at a subsequent age. Furthermore, 53% (202/381) of children with low literacy at age 10 were not in the low performance groups for language at ages 4, 6, and 8. For these children, identification at any earlier age was not possible. This reflects a developmental truism rather than failure of the monitoring system to detect low performance at an earlier age. This is an important message for policy makers, practitioners and parents. These observations are relevant for prevention scientists and practitioners alike.For prevention scientists seeking to implement randomised trials, there are clear hazards in both selection and trial assignment and in obtaining the necessary power to make estimates of effects. Where children are selected at an early point on the basis of low performance and assigned to treatment and no-treatment conditions the observed and typical variation as seen here may in all likelihood contribute to a pattern of mixed results across studies and weak effects in controlled trials .Where unselected samples are procured for intervention the high levels of developmental variability seen here will require careful scientific consideration of how to appropriately power and gear the sample size in order to differentiate the treatment effect of interest from the existing and high level of typical developmental variability. In intervention terms, the treatment “signal” will need to overcome a substantial level of typical developmental “noise” in order to be detected. For intervention scientists, the design choices here typically move across interacting fronts: The selection of sensitive child development measures that are calibrated with precision and, the specification of sample sizes with the power to detect the effect of the intervention. Our study serves as a reminder that if variability is part of the natural history of childhood language development, intervention scientists need to ensure they allow adequate sample to detect change. In our observational longitudinal study the measures used are suitable for use in large unselected samples. They have known and reasonable properties and are also used in school and health settings. Even with the presence of a large sample, and reasonable measures, our findings point to the challenges that intervention scientists face in the design of studies that test intervention efficacy.For those in policy and practice there are several pragmatic implications from these findings on the poor predictive utility of the multivariate and the change patterns that were observed. How do services  deliver preventive and treatment opportunities that take account of the risk profile of children in the face of their poor predictive utility?The findings here continue to encourage the adoption of methods of delivering services that are proportionately universal. This however, requires developing a policy platform of universal services available to all children irrespective of their risk status while incorporating the capacity to select and reach the more vulnerable. This is more than “repackaging” existing services. The service arrangement must allow for better documenting of a child’s developmental growth: In other words, an observation at a single point in time will not be useful relative to small annual or biennial measures in this period. Service arrangements will need to provide a relatively “light touch”, but universal capacity to measure children and document their growth over time to more appropriately detect those children that maintain stable low patterns, fluctuating low patters, and declining low patterns. This would permit better precision in targeting developmental prevention and intervention efforts to those that are demonstrating need .As well as this though, a particular tension in the implementation of policies and practices based on proportionate universalism rests with the propensity of the targeting component to focus on treatment (early or otherwise) to the exclusion of more robust efforts at altering barriers and reaching participants for prevention opportunities in the first place. Treatment will certainly be needed for those identified in need of it—but the gap in the targeted component tends to be in the failure to alter and address barriers for inclusion of higher risk children in prevention opportunities. How do we deliver effective developmental opportunities to those more in need? This remains a vexing and persistent challenge and intensification of this opportunity represents the “proportionate” effort required in policy and service implementation.In summary, for those wishing to reduce the risks for low literacy in the early preschool period, our findings suggest interventions in the preschool periods should provide broad-based developmental opportunities for children with interventions that improve school readiness—this multidimensional construct includes self-regulation  and general language enrichment. This should be programmatically delivered with multiple ‘touches’ across development and ‘light touches’ early in development because of the inevitability of over-servicing and be based on proportionate universalism that pays special attention to reducing barriers to participation for vulnerable groups .Our study has limitations.First, we lack a measure of non-verbal ability. There are strong arguments in favour of having such a measure . While In this study, we modelled the effect of multivariately adjusted single risk exposures  in early childhood on low literacy in middle childhood and discussed the implications of this approach to multi-level prevention and intervention policies and practice. In a subsequent study, we will investigate the effect of multiple risk exposures in early childhood on low literacy in late childhood. This will allow us to identify children with disproportionate exposure to multiple risks in early childhood and to determine the predictive utility of a multiple risk index for low literacy in late childhood. To achieve the necessary statistical power to conduct these sub-group analyses, we will expand the current analytic sample to include the the younger LSAC age cohort, that was out of scope for the current study. Combining the full LSAC sample of 10,000 children will be necessary to have adequate power to investigate risk and protective factors for declining and improving change patterns.In the future, we will be able to assess trajectories in language and literacy development in the LSAC using a direct assessment measure of children’s literacy. In Australia, the National Assessment Program—Language and Literacy (NAPLAN)  assesses


National Health Service (NHS) hospitals in the UK use a system of coding for patient episodes. The coding system used is the International Classification of Disease (ICD-10). There are ICD-10 codes which may be associated with adverse drug reactions (ADRs) and there is a possibility of using these codes for ADR surveillance. This study aimed to determine whether ADRs prospectively identified in children admitted to a paediatric hospital were coded appropriately using ICD-10.The electronic admission abstract for each patient with at least one ADR was reviewed. A record was made of whether the ADR(s) had been coded using ICD-10.Of 241 ADRs, 76 (31.5%) were coded using at least one ICD-10 ADR code. Of the oncology ADRs, 70/115 (61%) were coded using an ICD-10 ADR code compared with 6/126 (4.8%) non-oncology ADRs .The majority of ADRs detected in a prospective study at a paediatric centre would not have been identified if the study had relied on ICD-10 codes as a single means of detection. Data derived from administrative healthcare databases are not reliable for identifying ADRs by themselves, but may complement other methods of detection.The online version of this article (doi:10.1186/2050-6511-15-72) contains supplementary material, which is available to authorized users. National Health Service (NHS) hospitals in the UK use a system of coding alongside the length of hospital stay to determine the chargeable cost of care for each patient. The coding system used is the International Classification of Disease (ICD-10). The process of coding is by case note review, undertaken by trained coders. It relies on diagnoses, procedures and other events being written down by the clinical team caring for the patient. The data are submitted to become part of the national hospital episode statistics (HES) for the NHS in England and are used for research and planning.There are ICD-10 codes which may be associated with adverse drug reactions (ADRs). Although these codes can also be associated with overdose and medication errors, there is a possibility of using ICD-10 codes for ADR surveillance. Previous studies in the adult population have identified ADRs or ADEs either by retrospective case note review or by prospective monitoring, and then reviewed the ICD-10 codes for these cases; these studies showed that, in the majority of cases, ADRs and ADEs had not been coded properly . Other sIn this study, we aimed to determine whether prospectively identified ADRs in a paediatric population could have been detected via review of ICD-10 codes.The electronic records of children in whom a total of 241 ADRs had been prospectively identified in an earlier study, were reviewed. The aim was to determine whether an ICD-10 code related to the ADR had subsequently been added to their electronic admission abstract.The methodology of the prospective study in which the 241 ADRs were initially detected has been described previously , here a For each ADR, the following information was obtained from the prospective study dataset to meet the specific objectives of this retrospective study: patient identification, suspected drug(s), ADR description , ADR type, severity and causality. ADR type was defined as either Type A (augmented) resulting from an exaggeration of a drug’s normal pharmacological actions or Type B (bizarre) which are novel responses that are not expected from the known pharmacological actions of the drug . ADR sevThe electronic admission abstract for each patient identified through the earlier prospective study as having experienced at least one ADR was examined and a record was made of whether the ADR(s) and/or their signs and symptoms had been coded using ICD-10 and if so, which code(s) had been used.‘Y42 Hormones ’  for example, )’ Table  and codediseases . We refeA Chi-square test for difference in proportions was used to determine whether there were any differences in the characteristics of coded and uncoded ADRs.This study used routinely collected clinical data in an anonymized format. The Chair of the Liverpool Paediatric LREC informed us that this study did not require individual patient consent or review by an Ethics Committee.Y53.1 Other antacids and anti-gastric-secretion drugs’.Of the 241 ADRs evaluated in this retrospective study, 76 (31.5%) were coded using at least one ICD-10 ADR code, two reactions had two codes (Table Y43.3 other antineoplastic drugs’ was used to indicate adverse effects which had occurred secondary to the therapeutic use of oncology drugs.A large proportion of ADRs in the prospective study  occurredThe signs and symptoms of 212/241 (88%) ADRs were acknowledged by ICD-10 sign and symptom code(s). These 212 ADRs cases were made up of 107 oncology cases of which 70 also had an ADR code and 104 non-oncology cases of which 4 also had an ADR code. There were a variety of ICD-10 codes used for the same reaction type . Coded ADRs were not more likely to be of severity 1, 2 and 3 than those of severity 4 and 5 . Finally, coded ADRs were more likely to be definite and probable ADRs than possible ADRs .This retrospective study of electronic patient records demonstrates that the majority of ADRs detected in a prospective cohort study at a paediatric tertiary care centre would not have been identified if the study had relied on ICD-10 codes as a single means of detection. The use of ICD10 ADR codes was infrequent. The use of ICD-10 sign and symptom codes was more frequent but a wide variety of codes were used for the same ADR type. When undertaking research which uses data derived from administrative healthcare databases it is essential to first consider the reliability of those data.The limitations of this study relate to those of the prospective cohort study in which the ADR cases were identified. The recording of a medication history for each participant relied on parents and/or patients recalling and communicating accurately all medicines administered prior to admission. Clearly there was scope for errors and omissions in this process. The detection of suspected ADRs by the study team relied on two things: a) signs and symptoms associated with the ADR being recorded by the clinical team looking after the patient; and b) the study team suspecting a link between signs and symptoms recorded and the medicines administered before admission. Where signs and symptoms were not recorded or the study team missed the link, the ADR was not highlighted and an evaluation of how it had been coded could not be undertaken.In previous studies of ICD-10 coding for ADRs and ADEs in the adult population, the reported rate of coding was 2.0-6.8% [As a result of the system by which coding is undertaken, there are reasons why ADRs may not be coded using an ICD-10 ADR code. An ADR cannot be coded if it is not identified and subsequently recorded by the clinical team; this relies on many factors including the ability of the clinical team to identify an ADR and record it as such. It is interesting to note that ADRs which were classified as probable or definite in this study were more likely to have been coded using an appropriate ICD-10 ADR code. This suggests that a more confident causality assessment by the clinician is more likely to lead to a case-note entry which is picked up by coders.Many ADRs which were not coded using an ICD-10 ADR code had their signs and symptoms coded