Luisa Schonhaut
Andres Maturana
Olenkha Cepeda3- 1Departamento de Pediatría, Clínica Alemana, Santiago, Chile
- 2Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
- 3Departamento de Desarrollo Académico e Investigación, Clínica Alemana, Santiago, Chile
- 4Departamento Medicina Interna y Centro de excelencia CIGES, Universidad de La Frontera, Temuco, Chile
Context: Parent/caregiver completing developmental screening questionnaires (DSQs) for children before 5 years of age is currently recommended. The DSQs recommended by the American Academy of Pediatrics (AAP) are the Ages and Stages Questionnaires (ASQ), Parents' Evaluation of Developmental Status (PEDS), and the Survey of Well-being of Young Children (SWYC). Nevertheless, their predictive validity has not been well-established.
Objective: To assess in the current literature, the value of AAP-recommended DSQs (ASQ, PEDS, SWYC) administered between 0 and 5 years of age, for predicting long-term cognitive achievement and/or school performance (CA/SP), after 1 year or more of evaluation and at/or after age 5 years, in the general population.
Data Sources: Cochrane, MEDLINE PubMed, CINAHL, EMBASE, Web of Science, Scielo, and Scopus databases (until March 2021).
Study Selection: Two authors selected the studies. Forward and backward citation follow-up was done; authors of DSQ were contacted to identify additional studies.
Data Extraction: Cohorts were identified, and authors of selected studies were contacted to corroborate and complete extracted data.
Results: Thirty-two publications, corresponding to 10 cohorts, were included. All cohorts used ASQ. Only cohort using PEDS was identified but did not meet the inclusion criteria. No cohorts conducted with SWYC were identified. Associations between ASQ and CA/SP were extracted for eight cohorts. The odds ratios were >3, and the area under the curve was 0.66–0.87. A trade-off between sensitivity and specificity was observed.
Limitations: Heterogeneity in population characteristics and in DSQ adaptations.
Conclusions: A positive association between ASQ and later CA/SP was found in different social, cultural, and economic settings. Additional studies are necessary to determine the impact factors in the predictive capacity of DSQs.
Systematic Review Registration: PROSPERO, identifier: CRD42020183883.
Highlights
- ASQ is the most widely used developmental screening questionnaire in follow-up cohorts of young children.
- Positive association between ASQ and later cognitive and/or school performance was found.
- Trade-off between sensitivity and specificity could be explained by different scoring methods.
Introduction
It is estimated that one in six children has a developmental disability, defined by problems in cognitive, behavioral, language, learning, or physical performance, which are often more prevalent in children with biological risk factors such as prematurity (1–3). Considering that development is a continuum and that the first 5 years of life are recognized as a critical period for subsequent cognitive performance and school success, it is accepted that these disabilities begin in early childhood, under the definition of developmental delay (DD) (4). Early detection of DD allows for timely and effective interventions (5, 6). For this reason, early screening and referral of developmental difficulties are a critical element in the routine health supervision of children to guarantee that children have adequate conditions for optimal learning (7, 8).
Considering that the accuracy of healthcare providers for detecting DD is low when they rely on judgment or surveillance alone (9, 10), the current recommendation is to use standardized, valid, and reliable tools for screening at specific ages (7). The new guidelines from the American Academy of Pediatrics (AAP) focus on parent/caregiver-completed developmental screening questionnaires (DSQs) for children before 5 years of age (11). If screening results suggest delayed development or if parents have concerns, the child should be referred to a comprehensive developmental evaluation, which includes the application of a developmental diagnostic assessment (7). The Bayley Scale of Infant and Toddler Development is currently one of the most used tools with this purpose.
The DSQs recommended in the updated clinical report of the AAP are the Ages and Stages Questionnaires (ASQ), subsequently updated as ASQ-3 (12); Parents' Evaluation of Developmental Status (PEDS) and its complement Developmental Milestones (PEDS:DM) (13, 14); and the Survey of Well-being of Young Children (SWYC) (15). These questionnaires report values of sensitivity and specificity levels of 70–80%, thresholds recommended by the AAP statement in developmental screening tests (7, 11). The use of DSQs has increased in recent years because of their acceptable psychometric properties, versatility, cost-effectiveness, and parent empowerment (16–19). These questionnaires have been validated in a range of cultural and linguistic contexts and are widely used around the world in general populations and clinical samples (20–23).
In a recent study, Sheldrick et al., compared the three recommended DSQs, reporting adequate specificity and sensitivity for detecting concurrent severe DD (>70%) but low sensitivity to mild delays (24–62%) among children aged 9 months to 5 years, with no one questionnaire emerging superior (24). Despite numerous DSQ studies that analyzed concurrent validity (25–28), the predictive validity of these questionnaires has not been well-established, probably due to its complexity (29).
As background information, there are systematic literature reviews that analyze the predictive validity of developmental screening tools and developmental diagnostic assessment. In an extremely premature population, Wong et al., (30) reported a global sensitivity of 55% and a specificity of 84% of developmental assessments for identifying those children who will have cognitive problems later at school age. Luttikhuizen dos Santos et al., (31) reported that the mental coefficient of the Bayley test correlated significantly with subsequent cognitive functioning, r = 0.61.
In the general population, Sim et al., (32) demonstrated robust predictive validity of later disorders of language and socioemotional functioning, particularly when parent-report tools were used. In a recent publication, Cairney et al., (33) analyzed the predictive value of preschool developmental assessment on later educational outcomes in high-income countries, showing a consistent association between relatively poor early child development and later educational difficulties. They report ASQ as having the best correlation despite including only one study using ASQ in their review (33). Although these studies suggest robust predictive ability of the DSQs, none of the published studies analyzed the DSQs as a whole. We are not aware of any other publication to date that systematically reviews studies exploring an association between DSQ and later cognitive or educational performance.
The objective of this review is to assess in the current literature the value of AAP recommended DSQs (ASQ, PEDS, SWYC) administered between 0 and 5 years of age, for predicting long-term cognitive achievement and/or school performance after 1 year or more of evaluation and at/or after age 5 years, in the general population.
Methods
Protocol and Registration
Our systematic review protocol was registered in advance with PROSPERO (International Prospective Register of Systematic Reviews) on July 5, 2020 (registration no. CRD42020183883).
Eligibility Criteria
Included studies were in English and Spanish languages from peer-reviewed articles of cohort studies, which included two or more serial developmental evaluations with at least one DSQ before 5 years of age and at least one evaluation of intelligence or academic performance during school age (at 5 years of age or later and with at least 1 year between evaluations). In the first selection, we included three types of studies: those with an early developmental assessment, based on DSQ administered before 5 years of age; studies that conducted a developmental assessment at school age with intelligence or academic performance assessments in cohorts previously assessed with any DSQ; and finally, those that described the association between DSQ and school age assessment.
We included cohorts assessed with DSQ (ASQ, PEDS, and SWYC) applied in general populations, in any condition (whether completed by parents, education professionals, with or without assistance in completing it). We accepted those cases in which adjustments of the original test have been made to local conditions (including language translations, sociocultural adaptations, and/or validation process).
We excluded studies in which the developmental screening was performed after 5 years of age; studies that included concurrent evaluations or with <1-year difference between the screening test and the learning/intelligence evaluation; studies focused on children with known conditions or disease that severely affects development and cognition, such as genetic and/or metabolic diseases. We excluded prevalence and case–control studies, because of potentially overestimating the properties of the test, and case series (34).
Data Sources
A systematic search was carried out in Cochrane, MEDLINE PubMed, CINAHL, EMBASE, Web of Science, Scielo, and Scopus databases (until March 13, 2021) to identify the literature published. For the systematic search, we used the following terms: “infant,” “child, preschool” for population identification. The index tests were identified using the terms: “surveys and questionnaires,” “developmental screening,” “Ages and Stages,” “Parents Evaluation of Developmental Status,” “Survey of Well-being of Young Children,” “parents' evaluation.” Finally, the terms used to identify the reference test were “intelligence test”, “developmental disabilities,” “intellectual disability,” “intelligence,” “academic performance,” “intellectual quotient.”
To complete the search, the authors of the DSQ were contacted to identify additional studies that met the inclusion criteria.
Study Selection
A multiple-stage process was used to identify the studies and the cohorts behind them. First, two authors screened the titles and abstracts of studies retrieved from the electronic search for possible inclusion based on the predefined inclusion criteria. Second, forward and backward citation follow-up for each of the previously identified studies was done using Google Scholar–related references. The full text of all relevant studies identified was evaluated to select studies for final inclusion.
To identify and match the cohorts in the different publications reported separately, authors, site, and characteristics of the studied populations were considered. Although each cohort could have several published studies, only those that contributed data for either early developmental assessment with DSQ and/or academic or cognitive tests were included in the review.
Data Extraction
All information included was either published or extracted from published cohorts with the help of the authors. A data extraction form was completed for each cohort. The authors of the different cohorts were contacted to verify the cohorts and to corroborate the information extracted and to request additional information necessary to complete the data: author, study design, site, population, sampling method, sample size, age at DSQ, and cognitive/academic assessment and scoring method.
When children had more than one evaluation, each DSQ assessment was considered as a separate point for the analysis. When there was more than one simultaneous assessment of academic or cognitive performance, the cognitive assessment was considered as the most objective.
Evaluation of Risk of Bias
Two reviewers independently evaluated the risk of bias in each study using the Quality of Diagnostic Accuracy Studies version 2 (QUADAS-2) checklist. Each study was given a grade of “low,” “high,” or “unclear” for risk of bias and concerns regarding their applicability (35). Any disagreement between reviewers was resolved by consensus.
Data Synthesis
A qualitative analysis of the results was performed and summarized. The population characteristics, type of reference standard, index test, and reported comparison measures were summarized for each cohort [area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, odds ratio (OR), correlation coefficients]. When necessary, the findings from the comparison measures were recalculated based on the more exact information provided by the corresponding authors. Based on sensitivity, specificity, and predictive values, 2 × 2 tables were constructed, and the summary receiver operating characteristic and forest plot were calculated using RevMan 5.0. Because of significant heterogeneity, no summary measures were calculated.
Results
The literature search yielded 2,277 citations after excluding duplicates. A total of 396 studies were selected for full text review, selecting 74 studies. Of these, 32 publications met the inclusion criteria, corresponding to 10 cohort studies (Figure 1). The cohorts studied came from South America: Chile (19, 27, 36, 37) and Colombia (26, 38, 39); Europe: Catalonia, Spain (40–43), the Netherlands (28, 44–47); France Poitiers and Nancy (48) and France Loire (49–51) and Norway (52–54); Asia: South Korea (55–57) and North India (58–62); Oceania: Australia (63). Authors from seven of the 10 included cohorts reviewed and completed the data extraction form.
Figure 1. PRISMA flow diagram.
All the 10 cohorts included ASQ assessments. Only one prospective study using PEDS was identified but did not meet the inclusion criteria for this review because of the age of the children at first assessment (64). No cohorts conducted with SWYC were identified.
Two of the cohorts used abbreviated forms of ASQ, including only some domains (Norway and Australia) and one, an extended form of the test (Colombia). Except for the cohorts from Spain, France, and South Korea that used the official translated ASQ versions, the rest used locally translated and adapted versions. This information could not be obtained for the Australian cohort. All relevant characteristics are presented (Table 1).
Table 1. Qualitative summary of included cohorts.
Comparison measures between ASQ and cognitive/academic performance assessments in school age were extracted for eight of the cohorts (Table 2). In the five cohorts that report results based on the entire ASQ, a positive association was shown. Using the extended ASQ, the Colombian cohort reported a low global correlation at 6–8 years of age, with higher correlations for the Problem Solving and Communication domains, whereas in the Chilean cohort, all domains independently were significant predictors of long-term cognitive difficulties, except for personal–social. In studies that analyzed abbreviated forms of ASQ, positive associations were found for communication trajectories in Norway, and for the gross motor trajectories but not for fine motor trajectories in Australia, no other domains were analyzed.
Table 2. Comparison measures and main results of included cohorts.
The extracted or calculated AUC ranged between 0.66 and 0.87, and the ORs were all >3 (Table 2). In five cohorts, a 2 × 2 table was constructed, allowing the calculation of sensitivity and specificity, showing a trade-off between them (Figures 2, 3).
Figure 2. Forest plots of the estimated sensitivity and specificity of early developmental assessments for identifying the presence any cognitive impairment or academic difficulty. FN, false-negative; FP, false-positive; TN, true-negative; TP, true-positive; France PN, France Poitiers and Nancy; France L, France Loire.
Figure 3. Summary receiver operating characteristic curve of early developmental screening questionnaires for identifying any cognitive impairment or academic difficulty. Each marker displays the study and is scaled according to the sample size; the line represents the confidence interval. France PN, France Poitiers & Nancy; France L, France Loire.
Risk-Of-Bias Assessment
The assessment of each of the cohort studies for each dimension of the QUADAS is detailed in Table 3. The risk of bias in patient selection for most cohorts was low. However, external validity was limited, because special inclusion criteria based on gestational age or socioeconomic status were used in some cohorts. In relation to index test interpretation and applicability, we found some issues of concern due to differences in the scoring method and adaptations of the test. Another source of bias was due to significant dropout rate or follow-up of specific subgroups.
Table 3. Risk of bias and concerns about applicability.
Discussion
Our search identified 10 cohorts including children from early age who were all assessed with ASQ and followed to school age. Eight of these cohorts describe comparison measures showing adequate capacity to predict later cognitive achievement/school performance. The ORs reported were >3, and the AUC was high (0.66–0.87), showing trade-offs in sensitivity and specificity, which could be explained by the different scoring methods and thresholds used (28, 36, 42, 48, 50); the optimal cutoff point, for a screening test, is the one that yields sensitivity and specificity values >70% (7).
This review is in line with the results reported in both the reviews by Cairney et al., (33) and Sim et al., (32) that showed a consistent association between different developmental screening assessment tools and later educational performance. They reported better predictive capacity especially when using a parent-reported assessment than direct child assessment.
Our review expands these results by including studies using adapted/translated versions of ASQ, which increases the evidence supporting its widespread applicability. Some groups have adapted the form of application of the test, such as the “Home Procedure” model in India, abbreviated form in Norway and Australia, and extended ASQ in Colombia (26, 53, 58, 63). All these modifications could potentially impact the psychometric properties of the test, as shown by Velikonja et al., (25) in the analysis of ASQ concurrent validity studies. The heterogeneity regarding age at evaluation could also impact the results. In only two cohorts, a trend to improved predictive properties of the tests with assessment age was observed (37, 50). The heterogeneity among the studies did not allow conclusions in the domain analysis, as only some of the cohorts were included analysis by domain, and two cohorts used abbreviated forms of the ASQ, including only specific domains of the test (36, 52, 63).
The cohorts emerge from different socioeconomic, clinical, and cultural backgrounds. Some cohorts were population-based, whereas others corresponded to samples with specific socioeconomic or biological characteristics, which could compromise external validity of this data. It has been shown that the prevalence of DD increases with biological and psychosocial adversity (22, 65). In extremely premature infants, the predictive validity of developmental diagnostic tests has been well-established (30, 31). These variables can modify the developmental trajectories of children and, consequently, the predictive capacity of the questionnaires (44, 66).
Another factor that could alter developmental trajectories is the interventions carried out in children, data not reported by any of the cohorts. Only in the study from Catalonia was there evidence of a lack of association between the evaluation carried out at the age of 4 years and referral to support programs in development (42). It is described that in real world, referral rates for early intervention among children with positive screens ranged from 10–86% (67, 68).
Strengths and Limitations
The limitations of this review include great heterogeneity in population characteristics and in the way DSQ was used, such as thresholds considered and special adaptation of the questionnaires. Therefore, any summary result resulting from meta-analysis would be uninterpretable and will not allow any subgroup analysis. In addition, the variability of both initial and outcome assessments makes the mathematical synthesis of results difficult. In addition, several current ongoing cohorts are being studied and will need to be included in the future. There are currently no published studies of cohorts using SWYC and PEDS:DM as they are relatively new. Only one prospective study using PEDS was identified but did not meet the inclusion criteria for this review (64). Other studies analyzed the predictive validity of some DSQ for adaptive skills and behavior or social–emotional problems at school age. Although this is outside the purpose of this review, they contribute to understanding the scope of developmental screening in early stages of life (69, 70).
One of the key strengths of this review is the systematic and comprehensive literature search that is highly sensitive in capturing all available data relevant to the research question in different social, cultural, and economic settings. The presented analysis was based in cohorts and not individual studies with potentially overlapping populations with the additional advantage of having contacted a significant number of authors to corroborate and better extract data.
Conclusions
ASQ is the most widely used DSQ in follow-up cohorts. Associations between early ASQ assessment and later cognitive achievement/school performance have been established, suggesting it is a promising tool in early child assessment in different social, cultural, and economic settings. Additional studies are needed to determine the impact of different settings, prematurity, developmental interventions, age at assessment, and test adaptations in the predictive capacity of DSQ.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author Contributions
LS: conceptualized and designed the study, designed the data collection instruments and search strategy, collected and reviewed the data, carried out the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript. AM: designed the study, reviewed the data collection instruments, collected and reviewed the data, reviewed, and revised the manuscript. OC: conceptualized, designed and performed the search strategy and reviewed and revised the manuscript. PS: reviewed and revised the data collection instruments and the final manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
Our sincere acknowledgment to Xavier Demestre, Silvia Martinez-Nadal, and Paula Rojas, for reading the manuscript and giving their feedback. LS is a Ph.D. candidate in Methodology of Biomedical Research and Public Health program, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
Abbreviations
DSQ, developmental screening questionnaires; ASQ, Ages and Stages Questionnaires; DD, developmental delay; AAP, American Academy of Pediatrics; PDS, Parents’ Evaluation of Developmental Status, ; PEDS, Parents’ Evaluation of Developmental Status, ; DM, Developmental Milestones; SWYC, Survey of Well-being of Young Children; QUADAS-2, Quality of Diagnostic Accuracy Studies; AUC, area under the curve; OR, Odds Ratio.
References
1. Boyle CA, Boulet S, Schieve LA, Cohen RA, Blumberg SJ, Yeargin-Allsopp M, et al. Trends in the prevalence of developmental disabilities in US children, 1997-2008. Pediatrics. (2011) 127:1034–42. doi: 10.1542/peds.2010-2989
2. Rosenberg SA, Zhang D, Robinson CC. Prevalence of developmental delays and participation in early intervention services for young children. Pediatrics. (2008) 121:e1503–9. doi: 10.1542/peds.2007-1680
3. Allotey J, Zamora J, Cheong-See F, Kalidindi M, Arroyo-Manzano D, Asztalos E, et al. Cognitive, motor, behavioural and academic performances of children born preterm: a meta-analysis and systematic review involving 64 061 children. BJOG. (2018) 125:16–25. doi: 10.1111/1471-0528.14832
4. Shevell M. Global developmental delay and mental retardation or intellectual disability: conceptualization, evaluation, and etiology. Pediatr Clin North Am. (2008) 55:1071–84. doi: 10.1016/j.pcl.2008.07.010
5. Nordhov SM, Rønning JA, Dahl LB, Ulvund SE, Tunby J, Kaaresen PI. Early intervention improves cognitive outcomes for preterm infants: randomized controlled trial. Pediatrics. (2010) 126:e1088–94. doi: 10.1542/peds.2010-0778
6. Rao N, Sun J, Chen EE, Ip P. Effectiveness of early childhood interventions in promoting cognitive development in developing countries: a systematic review and meta-analysis. HK J Paediatr. (2017) 22:14–25. Available online at: http://www.hkjpaed.org/details.asp?id=1103&show=1234
7. Council on Children With Disabilities Section on Developmental Behavioral Pediatrics Bright Futures Steering Committee Medical Home Initiatives for Children With Special Needs Project Advisory Committee. Identifying infants and young children with developmental disorders in the medical home: an algorithm for developmental surveillance and screening. Pediatrics. (2006) 118:405–20. doi: 10.1542/peds.2006-1231
8. Williams PG, Okamoto J, Lieser D, Del Conte B, Donoghue E, Earls M, et al. The pediatrician's role in optimizing school readiness. Pediatrics. (2016) 138:e20162293. doi: 10.1542/peds.2016-2293
9. Sheldrick RC, Merchant S, Perrin EC. Identification of developmental-behavioral problems in primary care: a systematic review. Pediatrics. (2011) 128:356–63. doi: 10.1542/peds.2010-3261
10. Thomas RE, Spragins W, Mazloum G, Cronkhite M, Maru G. Rates of detection of developmental problems at the 18-month well-baby visit by family physicians' using four evidence-based screening tools compared to usual care: a randomized controlled trial. Child Care Health Dev. (2016) 42:382–93. doi: 10.1111/cch.12333
11. Lipkin PH, Macias MM. Promoting optimal development: identifying infants and young children with developmental disorders through developmental surveillance and screening. Pediatrics. (2020) 145:e20193449. doi: 10.1542/peds.2019-3449
12. Squires J, Bricker D. Ages and Stages Questionnaires User's Guide. 3rd ed. Baltimore, USA: Paul H Brookes Publishing Co (2009).
13. Glascoe FP. The value of “parents” evaluations of developmental status' in detecting and addressing children's developmental and behavioral problems. Diagnostique. (1998) 23:185–203. doi: 10.1177/073724779802300401
14. Brothers KB, Page Glascoe F, Robertshaw NS. PEDS: developmental milestones - an accurate brief tool for surveillance and screening. Clin Pediatr. (2008) 47:271–9. doi: 10.1177/0009922807309419
15. Sheldrick RC, Perrin EC. Evidence-based milestones for surveillance of cognitive, language, and motor development. Acad Pediatr. (2013) 13:577–86. doi: 10.1016/j.acap.2013.07.001
16. Lipkin PH, Macias MM, Chen B, Coury D, Gottschlich EA. Trends in pediatricians ' developmental screening : 2002–2016. Pediatrics. (2020) 145:e20190851. doi: 10.1542/peds.2019-0851
17. Schonwald A, Horan K, Huntington N. Developmental screening: is there enough time? Clin Pediatr. (2009) 48:648–55. doi: 10.1177/0009922809334350
18. Schonwald A, Huntington N, Chan E, Risko W, Bridgemohan C. Routine developmental screening implemented in urban primary care settings: more evidence of feasibility and effectiveness. Pediatrics. (2009) 123:660–8. doi: 10.1542/peds.2007-2798
19. Armijo I, Schonhaut L, Cordero M. Validation of the chilean version of the ages and stages questionnaire (ASQ-CL) in community health settings. Early Hum Dev. (2015) 91:671–6. doi: 10.1016/j.earlhumdev.2015.10.001
20. Small JW, Hix-Small H, Vargas-Baron E, Marks KP. Comparative use of the ages and stages questionnaires in low- and middle-income countries. Dev Med Child Neurol. (2019) 61:431–43. doi: 10.1111/dmcn.13938
21. Marks KP, Madsen Sjö N, Wilson P. Comparative use of the ages and stages questionnaires in the USA and Scandinavia: a systematic review. Dev Med Child Neurol. (2019) 61:419–30. doi: 10.1111/dmcn.14044
22. Woolfenden S, Eapen V, Williams K, Hayen A, Spencer N, Kemp L, et al. Systematic review of the prevalence of parental concerns measured by the parents' evaluation of developmental status (PEDS) indicating developmental risk. BMC Pediatr. (2014) 14:1–13. doi: 10.1186/1471-2431-14-231
23. Rousseau M, Dionne C, Savard RT, Schonhaut L, Londono M. Translation and cultural adaptation of the ages and stages questionnaires (ASQ) worldwide. J Dev Behav Pediatr. (2021) 42:490–501. doi: 10.1097/DBP.0000000000000940
24. Sheldrick RC, Marakovitz S, Garfinkel D, Carter AS, Perrin EC. Comparative accuracy of developmental screening questionnaires. JAMA Pediatr. (2020) 174:366–74. doi: 10.1001/jamapediatrics.2019.6000
25. Velikonja T, Calderon A, Sleed M, Deighton J. The psychometric properties of the ages & stages questionnaires for ages 2-2. 5: a systematic review. Child Care Health Dev. (2017) 43:1–17. doi: 10.1111/cch.12397
26. Rubio-Codina M, Araujo MC, Attanasio O, Mu P. Concurrent validity and feasibility of short tests currently used to measure early childhood development in large scale studies. PLoS ONE. (2016) 11:e0160962. doi: 10.1371/journal.pone.0160962
27. Schonhaut L, Armijo I, Schönstedt M, Alvarez J, Cordero M. Validity of the ages and stages questionnaires in term and preterm infants. Pediatrics. (2013) 131:e1468–74. doi: 10.1542/peds.2012-3313
28. Kerstjens JM, Bos AF. ten Vergert EMJ, de Meer G, Butcher PR, Reijneveld SA. Support for the global feasibility of the ages and stages questionnaire as developmental screener. Early Hum Dev. (2009) 85:443–7. doi: 10.1016/j.earlhumdev.2009.03.001
29. Marks K, Glascoe FP, Aylward GP, Shevell MI, Lipkin PH, Squires JK. The thorny nature of predictive validity studies on screening tests for developmental-behavioral problems. Pediatrics. (2008) 122:866–8. doi: 10.1542/peds.2007-3142
30. Wong HS, Santhakumaran S, Cowan FM, Modi N. Developmental assessments in preterm children: a meta-analysis. Pediatrics. (2016) 138:e20160251. doi: 10.1542/peds.2016-0251
31. Luttikhuizen dos Santos ES, de Kieviet JF, Königs M, van Elburg RM, Oosterlaan J. Predictive value of the bayley scales of infant development on development of very preterm/very low birth weight children: a meta-analysis. Early Hum Dev. (2013) 89:487–96. doi: 10.1016/j.earlhumdev.2013.03.008
32. Sim F, Thompson L, Marryat L, Ramparsad N, Wilson P. Predictive validity of preschool screening tools for language and behavioural difficulties: a PRISMA systematic review. PLoS ONE. (2019) 14:e0211409. doi: 10.1371/journal.pone.0211409
33. Cairney DG, Kazmi A, Delahunty LM. The predictive value of universal preschool developmental assessment in identifying children with later educational difficulties : a systematic review. PLoS ONE. (2021) 16:e0247299. doi: 10.1371/journal.pone.0247299
34. Bossuyt PM, Leeflang MM. Chapter 6: developing criteria for including studies. In: Bossuyt P, editor. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4 [updated September 2008]. The Cochrane Collaboration (2008). Available online at: https://methods.cochrane.org/sdt/handbook-dta-reviews
35. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. (2011) 155:529–38. doi: 10.7326/0003-4819-155-8-201110180-00009
36. Schonhaut L, Pérez M, Armijo I, Maturana A. Comparison between ages & stages questionanaire and bayley scales, to predict cognitive delay in school age. Early Hum Dev. (2020) 141:104933. doi: 10.1016/j.earlhumdev.2019.104933
37. Schonhaut BL, Pérez RM, Castilla FAM, Castro MS, Salinas AP, Armijo RI. Predictive value of ages & stages questionnaires for cognitive performance during early child education. Rev Chil Pediatr. (2017) 88:35–40. doi: 10.1016/j.rchipe.2016.08.008
38. Rubio-Codina M, Grantham-McGregor S. Predictive validity in middle childhood of short tests of early childhood development used in large scale studies compared to the Bayley-III, the family care indicators, height-for-age, and stunting: a longitudinal study in Bogota, Colombia. PLoS ONE. (2020) 15:e0231317. doi: 10.1371/journal.pone.0231317
39. Rubio-Codina M, Araujo MC, Attanasio O, Grantham-McGregor S. Validez concurrente y viabilidad de pruebas cortas comúnmente usadas para medir el desarrollo infantil temprano en estudios a gran escala: Metodología y resultados. Banco Interamericano del Desarrollo Division de protección social y salud (2016). Available online at: https://publications.iadb.org/bitstream/handle/11319/7823/Validez-concurrente-y-viabilidad-de-pruebas-cortas-comunmente-usadas-para-medir-el-desarrollo-infantil-temprano-en-estudios-a-gran-escala-metodologia-y-resultados.pdf (accessed April 18, 2021).
40. Demestre X, Schonhaut L, Morillas J, Martínez-Nadal S, Vila C, Raspall F, et al. Development deficit risks in the late premature newborn: evaluation at 48 months using the ages stages questionnaires®. An Pediatr. (2016) 84:39–45. doi: 10.1016/j.anpede.2015.09.023
41. Schonhaut L, Martinez-Nadal S, Armijo I, Demestre X. Reliability and agreement of ages and stages questionnaires®: results in late preterm and term-born infants at 24 and 48 months. Early Hum Dev. (2019) 128:55–61. doi: 10.1016/j.earlhumdev.2018.11.008
42. Martínez-Nadal S, Schonhaut L, Armijo I, Demestre X. Predictive value of the ages and stages questionnaire® for school performance and school intervention in late preterm and term-born children. Child Care Health Dev. (2021) 47:103–111. doi: 10.1111/cch.12814
43. Martínez-Nadal S, Demestre X, Schonhaut L, Muñoz SR, Sala P. Impact of neonatal morbidity on the risk of developmental delay in late preterm infants. Early Hum Dev. (2018) 116:40–6. doi: 10.1016/j.earlhumdev.2017.11.001
44. Hornman J, de Winter AF, Kerstjens JM, Bos AF. Stability of developmental problems after school entry. J Pediatr. (2017) 187:73–9. doi: 10.1016/j.jpeds.2017.05.022
45. Kerstjens JM, de Winter AF, Bocca-Tjeertes IF, ten Vergert EMJ, Reijneveld SA, Bos AF. Developmental delay in moderately preterm-born children at school entry. J Pediatr. (2011) 159:92–8. doi: 10.1016/j.jpeds.2010.12.041
46. Cserjesi R, van Braeckel KNJA, Butcher PR, Kerstjens JM, Reijneveld SA, Bouma A, et al. Functioning of 7-year-old children born at 32 to 35 weeks' gestational age. Pediatrics. (2012) 130:e838–46. doi: 10.1542/peds.2011-2079
47. Dotinga BM, Eshuis MS, Bocca-Tjeertes IF, Kerstjens JM, van Braeckel KNJA, Reijneveld SA, et al. Longitudinal growth and neuropsychological functioning at age 7 in moderate and late preterms. Pediatrics. (2016) 138:e20153638. doi: 10.1542/peds.2015-3638
48. Charkaluk M, Rousseau J, Calderon J, Bernard JY. Ages and stages questionnaire at 3 years for predicting IQ at 5–6 years. Pediatrics. (2017) 139:e20162798. doi: 10.1542/peds.2016-2798
49. Flamant C, Branger B, Tich SNT, de la Rochebrochard E, Savagner C, Berlie I, et al. Parent-completed developmental screening in premature children: a valid tool for follow-up programs. PLoS ONE. (2011) 6:e20004. doi: 10.1371/journal.pone.0020004
50. Halbwachs M, Muller JB, Tich SNT, Gascoin G, Chauty-Frondas A, Branger B, et al. Predictive value of the parent-completed ASQ for school difficulties in preterm-born children <35 weeks' GA at five years of age. Neonatology. (2014) 106:311–6. doi: 10.1159/000363216
51. Halbwachs M Muller JB The Tich SN de La Rochebrochard E Gascoin G Branger B . Usefulness of parent-completed ASQ for neurodevelopmental screening of preterm children at five years of age. PLoS ONE. (2013) 8:e71925. doi: 10.1371/journal.pone.0071925
52. Fufen J, Synnve S, Mari VW, Patricia E, Ragnhild BN, Espen R, et al. Predicting literacy skills at 8 years from preschool language trajectories: a population-based cohort study. J Speech Lang Hear Res. (2020) 63:2752–62. doi: 10.1044/2020_JSLHR-19-00286
53. Wood ME, Frazier JA, Nordeng HME, Lapane KL. Longitudinal changes in neurodevelopmental outcomes between 18 and 36 months in children with prenatal triptan exposure: findings from the norwegian mother and child cohort study. BMJ Open. (2016) 6:e011971. doi: 10.1136/bmjopen-2016-011971
54. Schjølberg S, Psychol C, Eadie P, Zachrisson HD, Øyen A-S, Prior M. Predicting language development at age 18 months: data from the norwegian mother and child cohort study. J Dev Behav Pediatr. (2011) 32:375–83. doi: 10.1097/DBP.0b013e31821bd1dd
55. Bhang SY, Ha E, Park H, Ha M, Hong YC, Kim BN, et al. Maternal stress and depressive symptoms and infant development at six months: the mothers and children's environmental health (MOCEH) prospective study. Korean Med Sci. (2016) 31:843–51. doi: 10.3346/jkms.2016.31.6.843
56. Shah S, Jeong KS, Park H, Hong YC, Kim Y, Kim B, et al. Environmental pollutants affecting children's growth and development: collective results from the MOCEH study, a multi-centric prospective birth cohort in Korea. Environ Int. (2020) 137:105547. doi: 10.1016/j.envint.2020.105547
57. Jeong KS, Park H, Ha E, Shin J, Hong YC, Ha M, et al. High maternal blood mercury level is associated with low verbal IQ in children. J Korean Med Sci. (2017) 32:1097–104. doi: 10.3346/jkms.2017.32.7.1097
58. Kvestad I, Taneja S, Kumar T, Bhandari N, Strand TA, Hysing M. The assessment of developmental status using the ages and Stages questionnaire-3 in nutritional research in north Indian young children. Nutr J. (2013) 12:1–11. doi: 10.1186/1475-2891-12-50
59. Kvestad I, Hysing M, Shrestha M, Ulak M, Thorne-Lyman AL, Henjum S, et al. Vitamin B-12 status in infancy is positively associated with development and cognitive functioning 5 y later in Nepalese children. Am J Clin Nutr. (2017) 105:1122–31. doi: 10.3945/ajcn.116.144931
60. Kvestad I, Taneja S, Upadhyay RP, Hysing M, Bhandari N, Strand TA. Vitamin B12, folate, and cognition in 6- To 9-year-olds: a randomized controlled trial. Pediatrics. (2020) 145:e20192316. doi: 10.1542/peds.2019-2316
61. Chowdhury R, Taneja S, Kvestad I, Hysing M, Bhandari N, Strand TA. Vitamin D status in early childhood is not associated with cognitive development and linear growth at 6 – 9 years of age in North Indian children : a cohort study. Nutr J. (2020) 19:1–9. doi: 10.1186/s12937-020-00530-2
62. Chowdhury R, Taneja S, Bhandari N, Kvestad I, Strand TA, Bhan MK. Vitamin-D status and neurodevelopment and growth in young north Indian children: a secondary data analysis. Nutr J. (2017) 16:1–8. doi: 10.1186/s12937-017-0285-y
63. Piek JP, Dawson L, Smith LM, Gasson N. The role of early fine and gross motor development on later motor and cognitive ability. Hum Mov Sci. (2008) 27:668–81. doi: 10.1016/j.humov.2007.11.002
64. Wake M, Gerner B, Gallagher S. Does parents' evaluation of developmental status at school entry predict language, achievement, and quality of life 2 years later? Ambul Pediatr. (2005) 5:143–9. doi: 10.1367/A04-162R.1
65. Potijk MR, Kerstjens JM, Bos AF, Reijneveld S, de Winter AF. Developmental delay in moderately preterm-born children with low socioeconomic status: risks multiply. J Pediatr. (2013) 163:1289–95. doi: 10.1016/j.jpeds.2013.07.001
66. Aylward GP. Developmental screening and assessment: what are we thinking? J Dev Behav Pediatr. (2009) 30:169–73. doi: 10.1097/DBP.0b013e31819f1c3e
67. Sheldrick RC, Breuer DJ, Hassan R, Chan K, Polk DE, Benneyan J, et al. System dynamics model of clinical decision thresholds for the detection of developmental-behavioral disorders. Implement Sci. (2016) 11:156. doi: 10.1186/s13012-016-0517-0
68. Wallis KE, Rivera LBD, Guthrie W, Bennett AE, Mandell DS, Miller JS. Provider responses to positive developmental screening: disparities in referral practices? J Dev Behav Pediatr. (2021) 42:23–31. doi: 10.1097/DBP.0000000000000855
69. Sananes R, Goldberg CS, Newburger JW, Hu C, Trachtenberg F, Gaynor JW, et al. Six-year neurodevelopmental outcomes for children with single-ventricle physiology. Pediatrics. (2021) 147:e2020014589. doi: 10.1542/peds.2020-014589
Keywords: screening tools, developmental screening questionnaires, cognition, educational difficulties, Ages and Stages Questionnaires (ASQ)
Citation: Schonhaut L, Maturana A, Cepeda O and Serón P (2021) Predictive Validity of Developmental Screening Questionnaires for Identifying Children With Later Cognitive or Educational Difficulties: A Systematic Review. Front. Pediatr. 9:698549. doi: 10.3389/fped.2021.698549
Received: 21 April 2021; Accepted: 12 October 2021;
Published: 24 November 2021.
Edited by:
Marjolein Verhoeven, Utrecht University, NetherlandsReviewed by:
Achyut Kumar Pandey, Banaras Hindu University, IndiaAnneloes Van Baar, Utrecht University, Netherlands
Copyright © 2021 Schonhaut, Maturana, Cepeda and Serón. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Luisa Schonhaut, lschonhaut@alemana.cl