Studies vary on a range of factors that may moderate the association between negative interpretation and anxiety in children and adolescents. These factors can be grouped together as ‘Population’ factors, those that relate to the participants in the study (e.g. age, whether the focus of the study was a clinical or nonclinical sample); and ‘Procedural’ factors, those that relate to the way in which the study was designed and conducted (e.g. which task was used to assess interpretation bias and who the informant for the anxiety measure was). Careful consideration of moderators is important as it may explain some of the inconsistencies apparent in the literature and provide important insights for treatment. The following sections briefly outline the relevant population and procedural variables that will be assessed as moderators in this review.

To date, three narrative reviews have been conducted that examine the association between anxiety and interpretation bias in children and young people, covering literature up to 2008 (Blanchette & Richards, 2009 ; Castillo & Leandro, 2010 ; Muris & Field, 2008 ). Taken together these reviews tentatively conclude that anxious children and adolescents are likely to show a negative interpretation bias. They also highlight the inconsistency in findings across studies and several unanswered questions. No previous reviews have included a meta‐analysis or claimed to be systematic and none directly tackle the issue of moderators. Thus, the aim of the present paper is to conduct a systematic review and meta‐analysis of the association between anxiety and negative interpretation bias in children and adolescents, taking into account a range of potential moderators.

In cognitive models (e.g. Kendall, 1985 ), anxiety is viewed as an emotional, behavioural and cognitive state that is underpinned by threat‐related schemas. These schemas are activated and guide cognitive processing in response to threat or the potential for threat. When an individual has an overactive threat schema, negative cognitive biases result. Cognitive biases can occur at various stages of information processing including attention and interpretation (Muris & Field, 2008 ). This review focuses specifically on negative interpretation bias, that is, a tendency to interpret ambiguity in a threatening or negative way. This bias has been implicated in cognitive behavioural models of anxiety as having a predisposing (Bar‐Haim, Lamy, Pergamin, Bakermans‐Kranenburg, & van IJzendoorn, 2007 ; Eysenck, 1992 , 1997 ; Williams, Watts, MacLeod, & Mathews, 1988 , 1997 ), causal (Beck & Clark, 1997 ), and/or maintaining (Bar‐Haim et al., 2007 ; Eysenck, 1992 , 1997 ; Mogg & Bradley, 1998 ; Williams et al., 1988 , 1997 ) role.

Clinical studies vary in whether and how they deal with comorbid disorders; participants with comorbid diagnoses may be included, excluded or comorbidity may not be assessed. As negative interpretation bias has also been found in other common comorbid psychiatric disorders such as depression and externalising disorders (Mathews & MacLeod, 2005 ; Reid, Salmon, & Lovibond, 2006 ) (although see. for example, Epkins, 1996 ; Leung & Wong, 1998 ), inclusion of those with comorbid disorders may result in the association between negative interpretation bias and anxiety appearing stronger than it would in a ‘pure’ anxious group.

Participant age and sex also vary widely across studies, but the effect of these sample characteristics is unclear. Age is sometimes considered as a covariate or moderator in studies examining interpretation bias and anxiety with mixed results (e.g. Blossom et al., 2013 ; Waite, Codd, & Creswell, 2015 ; Waters, Zimmer‐Gembeck, & Farrell, 2012 ). To our knowledge, no study has assessed sex within the context of this association.

There are also inconsistent results depending on whether the focus is on trait/state anxiety. Although only a few studies have examined state anxiety, there is evidence that both trait and state anxiety may be associated with a negative interpretation bias. However, findings are inconsistent (e.g. Muris, Rapee, Meesters, Schouten, & Geers, 2003 ; Salemink & Wiers, 2012 ).

Studies with both community and clinical samples vary in terms of whether they look at general anxiety or a specific subtype of anxiety (e.g. social anxiety or total anxiety score). If negative interpretation bias is a feature of a specific type of anxiety then effect sizes will be stronger in some studies than others, dependent on the anxiety subtype considered.

Studies vary in whether they focus on community or clinical populations. Here, we include all studies examining the association between anxiety and interpretation bias, including those that focus on clinical samples and those that focus on community samples. Larger effect sizes may be expected in studies using a clinical versus control design than a high versus low community sample design given that the difference in anxiety levels between groups will typically be greater in the former. For the same reason, a larger effect size would be expected when a clinical group are compared to a screened ‘nonanxious’ control group as opposed to an unscreened community sample or a different clinical population (Bar‐Haim et al., 2007 ).

The individual providing information about the young person's anxiety also varies across studies: it may be a teacher, parent or the child/adolescent participant. This may affect the strength of the association between bias and anxiety, particularly given that studies differ on whether the same or different informants report on bias and anxiety.

According to the content specificity hypothesis, (Beck, 1976 ), the relationship between interpretation bias and anxiety is expected to be stronger when the interpretation content matches the anxiety subtype. The majority of studies examining interpretation bias and anxiety in young people do not examine content specificity. However, as outlined above, some ambiguous scenario tasks use specific types of scenario that align with specific subtypes of anxiety (e.g. social scenarios/social anxiety). To date there has been no systematic review of whether the bias–anxiety association is stronger when there is a content match than when there is not.

The ambiguous scenarios task also varies by the type of scenario assessed (e.g. social, nonsocial, physical or a response to a range of scenarios to create ‘general scenarios’). This is not typically true of lexical tasks as they are limited by the words available in the English language that possess the required properties (homograph/homophone).

Studies also vary in the dependent variable used to capture interpretation bias. For example, in an ambiguous scenarios task: threat interpretation, threat frequency, threat threshold or a composite of all three may be used (e.g. Muris, Merckelbach, & Damsma, 2000 ). It is possible that some measures better capture anxiety‐related interpretation biases than others, which could explain some variance in effect sizes reported across the literature.

Research assessing interpretation bias in children and young people typically uses one of two task formats. Ambiguous scenario tasks (Barrett, Rapee, Dadds, & Ryan, 1996 ) are the most commonly used. Here, participants are presented with ambiguous social and nonsocial vignettes (via written, auditory, pictorial or a combination, stimuli) and asked to either choose an ending for each vignette from a list or to generate their own. An alternative task is based on lexical knowledge. For example, homophones and/or homographs that have a threat and nonthreat interpretations such as berry/bury and sink (kitchen)/sink (boat) (i.e. Gifford, Reynolds, Bell, & Wilson, 2008 ) might be used. Typically in this type of task, interpretation is evaluated by asking participants to select an image that matches the word they heard or to use the word in a sentence. Even within the same study inconsistent results have been found between these different tasks (e.g. Waters, Wharton, Zimmer‐Gembeck, & Craske, 2008 ). The extent to which the nature of the task influences the association between anxiety and interpretation bias in children and young people remains unclear.

The overall aim of the present study is to provide a systematic quantitative assessment of the relationship between negative interpretation and anxiety in children and adolescents, and to evaluate potential moderators of this relationship. The review takes a broad scope with regard to anxiety and includes research that focuses on clinical anxiety as well as research focused on normal individual variation in anxiety levels, both trait and state. Data were drawn from studies with a range of methods including, but not limited to experimental, cross‐sectional, and longitudinal designs that adhered to our eligibility criteria.

Funnel and forest plots of effect sizes aggregated within studies (so that each study was represented by one effect size) were used to assess outliers, as well as Cooks distance where influence was assessed by checking whether dfbetas were greater than one (Viechtbauer & Cheung, 2010 ).

The models were fitted using R 3.2.4 (R Core Team, 2015 ) using the rma.mv()function in the metafor package (Viechtbauer, 2010 ), data processing was conducting using the reshape (Wickham, 2007 ) and car packages (Fox & Weisberg, 2011 ), and sensitivity analysis was conducted with the weight package (Coburn & Vevea, 2016 ). To be included as a level within a moderator analysis, at least two effect sizes had to be available.

Which states that effect size,, in studyare predicted from the mean effect size across studies, γ, study characteristics,, and their associated parameter estimates, γ… γ. The deviation of the effect in studyfrom the overall mean is reflected in the residual, μ, which is assumed to have a normal distribution with variance σ. The sampling error for studyis reflected in, which has a normal distribution with variance σ. When no moderators are included, this model reduces to:

Most studies yielded more than one effect size due to multiple outcome measures being used or the same outcome being taken at multiple time points. To account for the dependency this created among effect sizes within studies a multilevel approach was used, in which effect sizes (level 1) were nested within studies (level 2). Effect sizes were allowed to vary across studies as a random effect, and moderators were treated as fixed effects. The model fitted is described by:

Cohen's d was extracted for all papers included in the meta‐analysis. Where Cohen's d was not available for the association of interest, means and standard deviations were used to compute d . If these were also not available, t ‐statistics and degrees of freedom were used. Where studies reported a correlation r , this was converted to Cohen's d using the formula described by Rosenthal ( 1994 ) on p.239. Effect sizes were coded in the same direction so that a positive d always indicated that those with higher anxiety showed greater negative interpretation. Where correlations were included, positive correlation coefficients always indicated that as anxiety/fear scores increased so did negative interpretation scores prior to transformation to d .

Attempts were made to reduce risk of bias within the studies included in the meta‐analysis in two ways. Firstly, studies were only included if they adhered to our strict eligibility criteria regarding methods. Secondly, characteristics related to quality such as control group, measures used and whether the study was published or unpublished were included as moderators within the analysis to investigate whether these affected results.

Papers were coded for a range of sample characteristics and moderator variables. A detailed description of coding criteria for each characteristic and level of all moderators is provided in Table A1 in Appendix S1 . Where papers had investigated potential mediators or moderators of the association between negative interpretation and anxiety, the moderator/mediator of interest was coded along with the resultant associations with anxiety and negative interpretation separately.

To ensure reliability of the criteria for full paper screening, the same two coders both assessed 20 full texts against the eligibility criteria. Agreement between the two coders was found on 90% of the papers. 2 Any disagreements between coders at either stage of the screening were discussed with the first author to reach a consensus. The first coder then coded the remaining full texts. Once all the full texts had been screened, the first author then extracted the relevant statistics (effect sizes; sample sizes; means and standard deviations where effect sizes were not available; and demographic information including, mean age, and percentage of males in the sample) from the accepted full texts.

A postgraduate student piloted the eligibility criteria and search terms and eligibility criteria were altered accordingly (specifically the word ‘human’ was added to criteria 3 regarding age of participants to ensure only papers on human populations were accepted). After completion of the piloting two coders (both postgraduate students) checked the first 208 abstracts against the eligibility criteria. On the basis of these 208 abstracts a high level of inter‐rater reliability between coders was found for reject/accept decisions ( κ = .91, p < .001). The remaining abstracts were coded by the first coder.

Study selection procedures adhered to PRISMA guidelines (Liberati et al., 2009 ). To select studies, abstracts from all sources were first screened against the eligibility criteria, followed by full texts. A paper could be excluded at any stage of the screening process on the basis of a ‘no’ response to any of the eligibility criteria; the first criterion that was not met was recorded as the reason for rejection. Where criteria were coded as unclear (in the absence of any ‘no’ codes) at the abstract stage, papers went through to full text screening. Where particular criteria were not applicable they were not coded. Those papers that were accepted via the full paper screening were then coded according to the coding criteria (see below and Table A1 in Appendix S1 ), and appropriate data were extracted. Duplicates were removed at both the abstract and full paper screening stages. Full or partial overlap of data between published and unpublished data was checked for during this process and unpublished data excluded as a duplicate.

Two sets of search terms were used. One set of terms focused on interpretation bias and anxiety, including anxiety subtypes, while the second set specifically identified papers using cognitive bias modification (CBM) that may have been missed by the first search terms. The exact search terms used can be found in Appendix S2 .

Studies were identified through searches on the databases: PubMed, Psych Info/Psych Articles, Web of Science, Google Scholar, NHS Evidence database. The searches were conducted on all papers from 1990, when the first studies examining interpretation bias and child anxiety were published, to the present day. A check for papers prior to 1990 was conducted and no papers conforming to the age limit were identified. Searches were conducted on 6 August 2015. Additionally, the references of previous reviews (i.e. Blanchette & Richards, 2009 ; Castillo & Leandro, 2010 ; Muris & Field, 2008 ) and all accepted papers were checked for relevant papers. Finally, first authors and corresponding authors of accepted papers were contacted to request any relevant unpublished work.

The sensitivity method, trim and fill, indicated that 15 more studies would be required to satisfy symmetry (see Figure 4 B). If these extra studies were entered with a d of 0, the association between negative interpretation and anxiety would be only slightly smaller ( = .51, p < .001). Following Vevea and Woods ( 2005 ), a prespecified sensitivity analysis was conducted using a priori weight functions. The estimate from the overall meta‐analysis proved to be quite robust, suggesting publication bias is unlikely to be an important influence on the results (adjusted model estimates ranged from = .51−.61).

To reduce publication bias through the inclusion of unpublished works, 58 researchers were contacted (four could not be contacted) and 70% responded to our email request. Of these, 21 authors provided additional unpublished manuscripts or data resulting in 29 further studies assessed for eligibility, 24 were accepted (since this request 10 of these papers have been either published or are under review at the time of writing, as reflected in the references in Appendix S3 ).

As Table 2 indicates, variation among effect sizes was not significantly accounted for by the task used to assess interpretation of ambiguity, open versus forced choice responses, scenario type, the dependent variable assessed or anxiety measure informant. However, content specificity was a significant moderator (see Table 2 ); when the scenario content matched the anxiety subtype, the association between negative interpretation and anxiety was larger than when they did not match.

Variation among effect sizes was not significantly accounted for by the inclusion/exclusion of comorbidity with another anxiety disorder or comorbidity with another psychiatric disorder (Table 2 ). Furthermore, variation among effect sizes was not accounted for by anxiety subtype, (descriptors of all moderators and their respective levels can be found in Table A1 in Appendix S1 ). Nor was variance in effect sizes accounted for by sex, b = −.0003 [−.009, .009], p = .940. In contrast, age significantly predicted effect size magnitude, b = .06 [03, .10], p < .001; with increasing age, the association between negative interpretation and anxiety in children and adolescents also increases. To provide greater insight into the significant moderation by age, mean age per study was plotted against the study's corresponding aggregated effect size (see Figure 3 ).

There were not enough effect sizes ( k ≤ 1 for all levels except social anxiety and separation anxiety) available to conduct an analysis across clinical anxiety disorders. As there were enough effect sizes comparing social anxiety ( k = 21) and separation anxiety ( k = 2) and other anxiety disorders we included two additional levels in the planned overall control group analysis: ‘Not social anxiety’ and ‘Not separation anxiety’ respectively (see Table 2 ; for descriptions of these levels see Table A1 in Appendix S1 ). Variation was not found to be accounted for by control group. Given these results, the associations with ‘Not social anxiety’, the ‘Not separation anxiety’ or the ‘Clinical Externalising’ control groups were excluded from the remaining analyses to allow a clear picture of the association between negative interpretation and anxiety in children and adolescents (for descriptions of these levels see Table A1 in Appendix S1 ).

Table 2 shows all moderation analyses, and separate meta‐analyses for each level of the moderator, as well as their respective confidence intervals (see Appendix S1 for a list of all moderators and their definitions). The first level listed under the title of each moderator indicates the reference group used in the moderation analyses.

Following assessments, no studies yielded an effect size that was an outlier. Therefore, the total sample included 11,507 children and adolescents with an average age across studies of 11.19 years old ( SD = 1.28, min = 2, max = 22). Eighteen studies (16 samples) focused on anxiety and interpretation bias within clinical samples and 57 studies focused on anxiety and interpretation bias within community samples. Table 1 lists all studies included within the meta‐analysis and their characteristics. Aggregated effect sizes within each study, along with their confidence intervals, can be seen in Figure 2 . Note that the statistics in the following sections are from a multilevel model that factors in the dependency between effect sizes from the same study, whereas the overall effect size in Figure 2 is based on a model in which effect sizes within studies are aggregated so that each study contributes only one effect size.

Six authors were contacted as the information required to calculate an effect size was not available in the paper. Three authors were able to provide the necessary information and the studies were therefore included. After the complete selection process, a total of 77 studies representing 75 samples were included in the meta‐analysis, resulting in the inclusion of 345 effect sizes (see Figure 1 for flow chart of numbers screened and accepted at each stage of the selection procedure).

Discussion

Summary of evidence Our meta‐analysis indicated that there is a medium‐sized overall association between negative interpretation and anxiety in children and adolescents, and that this effect is robust across clinical and community samples as well as across comparison groups for clinical samples. There was significant heterogeneity across studies, which were partially accounted for by child/adolescent age and whether the content of the interpretation‐task matched the specific subtype being assessed. The overall findings are consistent with adult reviews on the association between interpretation bias and anxiety (Blanchette & Richards, 2009; Mobini, Reynolds, & MacKintosh, 2013) and the previous narrative reviews of the child and adolescent literature (Muris & Field, 2008). There is no equivalent meta‐analysis assessing the association between negative interpretation and anxiety in adults, therefore as yet the effect sizes cannot be compared. However, to give some context, the population effect size estimate of = .62 is larger than that found between anxiety and attention bias in children and adolescents ( = .21; Dudeney, Sharpe, & Hunt, 2015).

Evidence for an age effect The results indicated that as age increases the association between negative interpretation and anxiety increases in strength. Dudeney et al. (2015) also found age effects in their meta‐analysis of attention bias and anxiety in children and adolescents. Taken together, these findings indicate that age/development may moderate the association between anxiety and cognitive biases more broadly. The analysis presented in Figure 3 indicates a positive linear relationship between the magnitude of the effect size and age, however, it is important to note that the vast majority of studies included had a participant mean age above 8 years. There are very few effect sizes available for children below 8 years old (N studies = 4, k = 9), with none available for children between 6 and 8 years old. This limits the conclusions that can be drawn about interpretation bias and anxiety in young children which is a noteworthy omission as anxiety symptoms cause significant impairments in children as young as 3 years and anxiety disorders are as common in younger as older children (Egger & Angold, 2006). Developmental factors, such as the ability to inhibit attention to threat (inhibition hypothesis; Kindt & Van Den Hout, 2001) and regulatory control (Salemink & Wiers, 2012) may moderate the association between negative interpretation and anxiety in adolescents and underpin age effects (see Field & Lester, 2010a; 2010bb for a more detailed discussion of potential moderating developmental factors). We were only able to investigate age as a proxy for development as, to date, there is a paucity of studies investigating the influence of specific developmental factors on the association between negative interpretation and anxiety. Another consideration is that findings may reflect age‐related differences in task performance rather than information processing per se (Field & Lester, 2010a). If younger children have difficulty understanding and completing the task as intended, this will likely lead to underestimated associations between negative interpretation and anxiety. In order for results from tasks to be reliable, the skills necessary for task completion must be sufficiently developed (Brown et al., 2013). Moving forward, it will be important for interpretation bias tasks to be designed in a developmentally sensitive way with studies ideally including assessments of relevant developmental factors alongside interpretation bias and anxiety.

Evidence for content specificity The finding that there was a larger association between bias and anxiety when anxiety subtype and scenario content matched than when they did not match provides evidence for content specificity in children and adolescents. Such evidence is in line with the cognitive specificity hypothesis (Beck, 1976) and adult reviews that have concluded that there is an association between emotions and mood‐congruent interpretation biases (Blanchette & Richards, 2009). Our results extend this finding to children and adolescents. It is important to consider whether this finding relates to all anxiety disorders. Where studies had examined content specificity it was almost always for social anxiety with interpretation of social versus nonsocial scenarios. Therefore, it would be premature to suggest that this evidence for content specificity applies across anxiety disorders. Furthermore, this analysis is based upon primary anxiety diagnoses or anxiety symptoms and it is therefore unclear how the presence of comorbid anxiety disorders affect biases.

Clinical implications The moderate overall association between anxiety and negative interpretation confirms that it may be appropriate for anxiety treatments to include some focus on negative interpretation, at least in older children and adolescents. The finding that age significantly moderated the association between anxiety and negative interpretation suggests that, with age, the processing of ambiguity may become increasingly important as a focus within anxiety treatments and may be an important treatment target for adolescents with elevated anxiety. On the other hand, targeting negative interpretation may not be so central to the treatment of anxiety in younger children: for example, Thirlwall, Cooper, and Creswell (2017) found that for 7–12‐year‐olds undergoing parent‐guided cognitive‐behavioural therapy, child threat interpretation decreased from pre‐ to post‐treatment in both treated and wait list groups, and this change was not associated with recovery from primary anxiety diagnosis. It is possible that there are interactions between age and other moderating variables that would assist in elaborating on the clinical implications of the age effect. For example, age may interact with a match between scenario and anxiety type, whereby focusing on scenarios matching the child's anxiety in treatment may only be/be more appropriate for a particular age group. However, a lack of power in this study meant investigations of such interactions was not possible and would be an important consideration for future research. The moderation by a match between scenario and anxiety subtype suggests targeting interpretations related to the child/adolescent's specific anxiety diagnosis may prove most efficacious. However, three things should be noted when considering the clinical implications of results. First, while the meta‐analysis did find a larger association between interpretation bias and anxiety when there was a match between scenario content and anxiety subtype, an association was still present when there was no match. This suggests that the targeting of interpretations, regardless of whether they do or do not reflect the anxiety subtype, may still be appropriate in treatment. Second, it is unclear whether age/development influences content specificity; it may be that targeting interpretations related to anxiety subtype may be more appropriate for some ages than others. Finally, the present findings are entirely based on cross‐sectional data and it is important to keep in mind that the causal relationship between negative interpretation and anxiety has not been confirmed by the present results. While experimental studies were included, effects sizes were only taken from associations at a single time point, as per the focus of this review. As such, the effect sizes included in this review are subject to the same issues that apply to correlational designs: unobserved confounding variables might account for the associations. Whether interpretation bias and anxiety are causally related and whether associations are unidirectional or reciprocal remains unclear. Hallion and Ruscio (2011) and Van Bockstaele et al.'s (2013) both found evidence to suggest a modest causal relationship between cognitive biases and anxiety, going from the bias to anxiety, among adults. Some studies with children and adolescents have also shown that successful manipulation of interpretation (using Cognitive Bias Modification of Interpretation; CBM‐I) is associated with changes in anxiety and fear (Lau, Belli, & Chopra, 2013; Lau, Pettit, & Creswell, 2013; Vassilopoulos, Banerjee, & Prantzalou, 2009), consistent with a causal pathway. However, a recent meta‐analysis concluded that changes in interpretation bias caused by CBM paradigms did not significantly affect symptoms of anxiety in children (Cristea, Mogoașe, David, & Cuijpers, 2015). Thus, there is scope for further work to examine the exact interplay between biases and anxiety and the conditions under which a causal association is found. The association between interpretation and attention biases is also unclear, with the majority of cognitive bias research focusing on one or other of these biases. It is possible that both biases share the same processing mechanism (Williams et al., 1997) or that one may directly influence the other (Hirsch, Clark, & Mathews, 2006), for example, attention bias may have a cascading influence on interpretation bias (Daleiden & Vasey, 1997; Muris & Field, 2008; White, Suway, Pine, Bar‐Haim, & Fox, 2011). Future research capturing the interaction between attention and interpretation bias in child anxiety over time would be beneficial. Furthermore, extending cognitive bias research to consider other biases such as confirmation bias may be a useful avenue for future research with a recent study suggesting a possible reciprocal relationship between bias and anxiety in children (Remmerswaal, Huijding, Bouwmeester, Brouwer, & Muris, 2014).