Optimal Time to Diagnose Autism

Abstract

In this paper, the issues concerning the possible ways of early diagnosis of autistic spectrum disorder are discussed. Current research on autism and its early detection is impacted by the growing number of people with the disorder who might be underdiagnosed throughout their lifespan. The impairments in cognitive and language development, as well as the biochemical approach to diagnosis, are analyzed and discussed from the perspective of their application to practice. Concerning the prevailing rate of the illness, it is crucial to apply current research advances to stimulate earlier detection of the disorder to implement timely practices that would enhance the quality of patients’ lives.

Introduction

The issue of autism spectrum disorder occupies a significant place in the field of clinical psychology due to the prevalent nature of the illness. Affecting a wide range of individuals, autism is manifested through exaggerated behavioral reactions to different changes in the environment, sensitivity, dysfunctional socialization, and communication. For the last several decades, the number of cases of this disorder has increased significantly, which imposes the need for clinicians and researchers involved in the psychological realm to utilize better practices for early diagnosis of autism.

Thus, this paper aims at clarifying the optimal time for autistic spectrum disorder diagnosis. By now, the mean age when the disorder is detected in children is 4-5 years. Research in the field of autism spectrum disorder diagnosing is broad and presents a variety of methodologies and strategies. The most discussed ones are the detection of impairments in language and cognition and the diagnosis with the help of biomarkers. Therefore, it is critical to review the available research findings and evaluate them according to their applicability to practice.

Evaluation of Sources

The quality of autistic patients is predominantly dependent on the timely and correct diagnosis, which enables appropriate treatment and attitude. According to the latest findings, the number of individuals diagnosed with the disorder increases and now reaches the prevalence rate of approximately 1-1.5% (Zwaigenbaum & Penner, 2018). From this perspective, the study by Zwaigenbaum and Penner (2018) is highly relevant and timely since it addresses the newest advances in the psychological and clinical approaches to early diagnoses. Also, this source is credible and authoritative because the research is carried out by reputable clinicians professing in the field of psychology and pediatrics.

The research by Goodwin, Matthews, and Smith (2017) was conducted to validate the suggestion concerning the effects of language on the diagnosis of autism. Although the study findings are somewhat unclear, the implications of the results justify the use of more specific methods of diagnosis than language development in children. The source is credible; the authors are specialists with Southwest Autism Research and Resource Center (Goodwin, Matthews, & Smith, 2017). Overall, this study is an important step forward in the improvement of diagnosing methods.

Finally, the article by Howsmon, Kruger, Melnyk, James, and Hahn (2017) concentrates on strictly biological predictors of autism spectrum disorder and amplifies the validity of the diagnosing measures. The source’s credibility and authority are based on the wide range of literature used, quantitative analysis, and statistical data presented in the article. Thorough referencing and scientific explanation of the findings enhance the academic and clinical contribution made by the scholars. All three articles are based on credible scholarly sources; address a narrow audience specializing in the field of autism spectrum disorder diagnosis, and present clear points of view on the best practices.

Cognitive and Language Levels as Diagnosis Predictors

One of the leading symptoms of autism is patients’ dysfunctional social activity, their disability to integrate into society, and communicational failures. According to Zwaigenbaum and Penner (2018), “delayed language skills and atypical social, emotional responses” are commonly used as the signs indicating at-risk children (p. 2). Indeed, in the second year of the lifespan, individuals with a high risk of being diagnosed with autism demonstrate reduced language skills and impairments in communication. However, as the latest findings indicate, the development of cognitive and language skills cannot be used as the only reliable measure for the autism diagnosis.

As Goodwin et al. (2017) demonstrate by their research, autistic children with language delay and without it do not have significant differences in adaptive functioning. Also, the language skills of the two groups of individuals do not have any effect on the severity of symptoms. Therefore, this research proves that more accurate and more specific methods of diagnosis are needed to detect the indicators of autism in the early stages of a child’s development.

The Utilization of Biomarkers as the Way to Diagnose Autism

In terms of consistency with the findings concerning language development delay, it is relevant to discuss biological approaches to diagnosis, which are more accurate. According to Howsmon et al. (2016), the issue of lacking knowledge about the pathophysiology of autism spectrum disorder is the main problem of the current research on autism. The available data suggests that metabolite concentrations in blood are correlated with autism symptoms and is a valid method of predicting autism spectrum disorder even at an early age (Howsmon et al., 2016). The credibility of the method is validated by the detection accuracy rate of 91%, which demonstrates the prospects for further research in the field and the wide application of the approach to practice.

Application and Research Vision

The analysis of the literature on autism diagnosis allows for applying the best practices in the clinical setting. Since the discussed illness is a spectrum disorder, it is characterized by a variety of symptoms and affects a wide range of individuals. Therefore, it is important to use complex methodologies in early diagnosis to reach the highest level of results’ accuracy. Thus, the observation of particular cognitive and language development signs should be combined with biochemical tests for detecting metabolite concentrations in patients’ blood. Such a method will amplify the effectiveness of the process of diagnosis and cohort classification. Ultimately, timely identification of the problem will improve the quality of care for people with an autism spectrum disorder.

Conclusion

Autism is a severe psychological problem that impacts more and more children across the globe during recent decades. The issue of underdiagnosed cases imposes the need for research on better methods of detecting at-risk individuals at the early stages of the lifespan. The analyzed sources demonstrate that scholars work on the improvement of the available practices. More specifically, the issues of cognitive and language development delay are critically evaluated and recognized as non-fully reliable. Therefore, more accurate biochemical measures are being developed to identify metabolite concentrations specific to autism. However, the combination of multiple approaches will amplify the effectiveness of early diagnosis and allow for determining the probability of autism during the first year of life.

References

Goodwin, A., Matthews, N. L., & Smith, C. J. (2017). The effects of early language on age at diagnosis and functioning at school age in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 47, 2176-2188.

Howsmon, D. P., Kruger, U., Melnyk, S., James, S. J., & Hahn, J. (2017). Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation. PLoS Computational Biology, 13(3): e1005385, 1-15.

Zwaigenbaum, Z., & Penner, M. (2018). Autism spectrum disorder: Advances in diagnosis and evaluation. BMJ, 361:k1674, 1-16.