case study on dyslexia child in india

Prominent dyslexia signs and symptoms in schoolchildren

• Letter names and sounds are difficult to remember.
• Problem with unpredictable and uneven spelling.
• Reversing the order of letters and figures.
• The arrangement of letters in words is sometimes misunderstood.
• When reading aloud, might read slowly or make mistakes.
• When reading, there are visual distortions.
• Answering questions effectively verbally but finding it difficult to write down the response.
• Difficulty following a set of instructions.
• Having difficulty learning sequences such as the days of the week or the alphabet.
• Writing speed is sluggish.
• Messy handwriting.

Dyslexia signs and symptoms in teenagers and adults

• Disorganized written work and lack of expression.
• Essays, letters, and reports can be tough to prepare and write.
• Preparing for exams and revising seems to be challenging.
• Avoiding to read and write
• Having trouble taking notes or copying.
• Incorrect spelling.
• Having trouble remembering things like a PIN or a phone number.

Class X and XII board results: How students battling dyslexia showed the way

About the Author

Sandeep Shrivastwa has been associated as a journalist with The Times of India since 2016 and working in the role of Assistant Editor, News - digital desk. He loves breaking news and simplifying puzzling and ambiguos news content to ensure an enjoyable read. Read More

Visual Stories

Roles Conceptualization, Writing – review & editing

Affiliation Kerala University of Health Sciences, Medical College PO, Thrissur, Kerala, India

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing

Affiliation Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India

Roles Formal analysis, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Data curation, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Supervision, Validation, Writing – review & editing

Affiliation Department of Paediatrics, Maulana Azad Medical College, New Delhi, India

Roles Formal analysis, Investigation, Supervision, Validation, Writing – review & editing

Affiliations Sangath, Bardez, Goa, India, Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America

Roles Data curation, Formal analysis, Writing – review & editing

Affiliation Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India

Roles Investigation, Supervision, Writing – review & editing

Affiliation Department of Community Medicine, Kalinga Institute of Medical Sciences, Bhubaneshwar, Odisha, India

Affiliation Sangath, Bardez, Goa, India

Roles Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Indian Institute of Public Health, Hyderabad, Telangana, India

Affiliation Himachal Foundation, Dharamshala, Kangra, Himachal Pradesh, India

Roles Investigation, Methodology, Validation, Writing – review & editing

Affiliation Department of Paediatrics, Lady Hardinge Medical College, New Delhi, India

Roles Methodology, Supervision, Validation, Writing – review & editing

Affiliation Centre for Applied Research and Education on Neurodevelopmental Impairments and Disability related Health Initiatives (CARENIDHI), New Delhi, India

Roles Methodology, Validation, Writing – review & editing

Affiliation Department of Paediatrics, Sir Ganga Ram Hospital, New Delhi, India

Roles Methodology, Supervision, Writing – review & editing

Affiliation Department of Social Medicine and Community Health, Jawaharlal Nehru University, New Delhi, India

Roles Project administration, Supervision, Validation, Writing – review & editing

Roles Supervision, Validation, Writing – review & editing

Roles Methodology, Writing – review & editing

Affiliation Department of Paediatrics, Stanford University School of Medicine and Lucile Packard Children’s Hospital, California, United States of America

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation Department of Population Health Sciences and Paediatrics, and Waisman Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America

Affiliation University of Pennsylvania School of Nursing and School of Medicine, Philadelphia, United States of America

Affiliation Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States of America

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – original draft

Affiliation Department of Psychiatry, All India Institute of Medical Science, New Delhi, India

Roles Conceptualization, Data curation, Methodology, Supervision, Writing – original draft

Affiliation Integral Institute of Medical Sciences & Research, Integral University, Lucknow, Uttar Pradesh, India

Roles Data curation, Investigation, Methodology, Writing – original draft

Affiliation Department of Psychology, Delhi University, New Delhi, India

Roles Conceptualization, Investigation, Methodology, Writing – original draft

Affiliation Department of Paediatrics, Seth GS Medical College & KEM Hospital, Mumbai, Maharashtra, India

Roles Conceptualization, Methodology, Writing – original draft

Affiliation Department of Neurology, Paras Hospital, Gurugram, Haryana, India

Affiliation Department of Ophthalmology, Lady Hardinge Medical College, New Delhi, India

Roles Conceptualization, Data curation, Investigation, Methodology, Supervision, Writing – original draft

Affiliation Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India

Affiliation Department of Paediatric Disciplines, Health City Hospital, Guwahati, Assam, India

Affiliation Department of Neurology, Institute of Human Behaviour and Allied Sciences & Department of Neurophysiology, Sir Ganga Ram Hospital, New Delhi, India

Affiliation School of Health Policy and Planning, Kerala University of Health Sciences, Thiruvananthapuram, Kerala, India

Affiliation Ali Yavar Jung National Institute of Speech and Hearing Disabilities, Department of Empowerment of Persons with Disabilities, Kasturba Niketan, New Delhi, India

Affiliation Department of ENT & Head Neck Surgery, Medanta Medicity, Gurugram, Haryana, India

Affiliation Department of Paediatrics, Indraprastha Apollo Hospital, New Delhi, India

Affiliation Department of Paediatric Neurology, Rainbow Children’s Hospital, Hyderabad, Telengana, India

Affiliation Department of Paediatrics, Mumbai Port Trust Hospital, Mumbai, Maharashtra, India

Affiliation Department of Paediatrics, King George Medical University, Lucknow, Uttar Pradesh, India

Affiliation Department of Neonatology, Post Graduate Institute of Medical Education and Research and Dr. Ram Manohar Lohia Hospital, Delhi, India

Affiliation Department of Community Medicine, Government Medical College, Srinagar, Kashmir, India

Roles Conceptualization, Methodology, Supervision, Writing – original draft

Affiliation National Trust, Department of Empowerment of Persons with Disabilities, Ministry of Social Justice & Empowerment, Government of India, Delhi, India

Affiliation Vidya Sagar (formerly The Spastics Society of India), Chennai, Tamil Nadu, India

Affiliation Department of Paediatrics, Government Medical College, Nagpur, Maharashtra, India

Roles Conceptualization, Funding acquisition, Methodology, Writing – original draft

Affiliation Social Welfare Department, Government of Bihar, Patna, India

Affiliation Department of Community Medicine, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India

Affiliation Department of Child & Adolescent Psychiatry and Facility for Children with Intellectual Disability, Christian Medical College, Vellore, Tamil Nadu, India

Roles Data curation, Methodology, Writing – original draft

Affiliation Rashtriya Bal Swasthya Karyakram, Ministry of Health and Family Welfare, Nirman Bhawan, New Delhi, India

Affiliation Department of Otorhinolaryngology and Head and Neck Surgery (ENT), Lady Hardinge Medical College, New Delhi, India

Affiliation Child Development Centre, Medical College Campus, Thiruvananthapuram, Kerala, India

Affiliation Samarth, Chennai, Tamil Nadu, India

Affiliation Department of Otolaryngology & Head-Neck Surgery, All India Institute of Medical Sciences, New Delhi, India

Affiliation Department of Pediatric Neurology, The Hospital for Sick Children (SickKids), The Peter Gilgan Centre for Research and Learning, Toronto, Ontario, Canada

Affiliation Department of Paediatrics, M.P. Shah Government Medical College & G.G. Hospital, Jamnagar, Gujarat, India

Roles Data curation, Methodology, Supervision, Writing – original draft

Roles Data curation, Supervision, Writing – original draft

Affiliation Department of ENT, Dr. Rajender Prasad Government Medical College, Kangra, Himachal Pradesh, India

Roles Data curation, Methodology, Project administration, Supervision, Writing – original draft

Affiliation RVM Institute of Medical Sciences and Research Center, Laxmakkapally, Telangana, India

Affiliation Department of Paediatrics, Goa Medical College, Bambolim, Goa, India

Roles Data curation, Project administration, Supervision, Writing – original draft

•  [ ... ],

Affiliations Sangath, Bardez, Goa, India, Department of Orthopedic Surgery, Goa Medical College, Bambolim, Goa, India

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• Narendra K. Arora, 
• M. K. C. Nair, 
• Sheffali Gulati, 
• Vaishali Deshmukh, 
• Archisman Mohapatra, 
• Devendra Mishra, 
• Vikram Patel, 
• Ravindra M. Pandey, 
• Bhagabati C. Das, 

• Published: July 24, 2018
• https://doi.org/10.1371/journal.pmed.1002615
• Reader Comments

Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden.

Methods and findings

We assessed 3,964 children (with almost equal number of boys and girls distributed in 2–<6 and 6–9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal ( N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra ( N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal ( N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad ( N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa ( N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6–9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2–<6 year olds ranged from 2.9% (95% CI 1.6–5.5) to 18.7% (95% CI 14.7–23.6), and for any of nine NDDs in the 6–9-year-old children, from 6.5% (95% CI 4.6–9.1) to 18.5% (95% CI 15.3–22.3). Two or more NDDs were present in 0.4% (95% CI 0.1–1.7) to 4.3% (95% CI 2.2–8.2) in the younger age category and 0.7% (95% CI 0.2–2.0) to 5.3% (95% CI 3.3–8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5–11.2) and 13.6% (95% CI 11.3–16.2) in children of 2–<6 and 6–9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6–9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population.

Conclusions

The study identifies NDDs in children aged 2–9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions.

Author summary

Why was this study done.

• Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels.
• Lack of robust evidence regarding burden and risk factors impedes policy and programmatic action for these conditions.
• Given the widespread prevalence of known risk factors, the anticipated burden of NDDs in children in India could be considerably high, but adequate information is not available.

What did the researchers do and find?

• In this population based study, the prevalence of NDDs among 2–9-year-olds was estimated across five geographically diverse sites in India: North-Central (Palwal), North (Kangra), East (Dhenkanal), West (North Goa), and South (Hyderabad).
• We assessed 3,964 chidren (2–<6 years: 2,057; 6–9 years: 1,907) for seven common NDDs: vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Two additional NDDs (attention deficit hyperactivity disorder [ADHD] and learning disorders [LDs]) were also assessed in 6–9-year-old children.
• Prevalence of NDDs varied between sites. Site-specific prevalence of any of seven NDDs in 2–<6year olds ranged between 2.9% and 18.7% and for any of nine NDDs in the 6–9-year-old children from 6.5% to 18.5%. About one-fifth of these children had two or more NDDs.
• HI and ID were the most common NDDs.
• The risk factors for childhood NDDs were as follows: children with history of delivery at home, delayed crying or difficult breathing at birth (perinatal asphyxia), neonatal illness requiring hospitalization, neurological/brain infections, low birth weight (LBW) (<2.5 kg) and/or birth before 37 weeks of gestation (prematurity), and stunting. NDDs were also likely to be more frequent in older children (6–9 year age category).

What do these findings mean?

• Almost one in eight children of the age 2–9 years have at least one of the nine NDDs; this is a conservative estimate, and actual burden might be higher due to limitations of the study.
• The data suggested that the NDD burden can be substantially reduced in India by addressing the risk factors which are amenable to public health interventions.

Citation: Arora NK, Nair MKC, Gulati S, Deshmukh V, Mohapatra A, Mishra D, et al. (2018) Neurodevelopmental disorders in children aged 2–9 years: Population-based burden estimates across five regions in India. PLoS Med 15(7): e1002615. https://doi.org/10.1371/journal.pmed.1002615

Academic Editor: Lars Åke Persson, London School of Hygiene and Tropical Medicine, UNITED KINGDOM

Received: January 15, 2018; Accepted: June 15, 2018; Published: July 24, 2018

Copyright: © 2018 Arora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files. For need of any additional data, please contact the INCLEN Data Access Committee (IDAC). The IDAC is headed by Brigadier (Retd.) VK Panday, Chief Operations Officer ( [email protected] ); other members in the committee are: Dr Ramesh Poluru, Senior Program Officer – Biostatistics ( [email protected] ), Dr Rupak Mukhopadhyay, Senior Program Officer- Demographic Surveillance ( [email protected] ) and Mr Neeraj K Kashyap, Program Officer- IT & Data Science ( [email protected] ).

Funding: This work was supported by The National Institutes of Health, USA [NIH 1R21–HD53057–01A1] through Fogarty International Center (FIH), Autism Speaks (USA), and The National Trust, Ministry of Social Justice and Empowerment, Government of India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ADHD, attention deficit hyperactivity disorder; AOR, adjusted odds ratio; ASD, autism spectrum disorder; CAB, child assessment booklet; CDD, childhood disintegrative disorder; CI, confidence interval; CP, celebral palsy; DSM-IV-TR, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; Epi, epilepsy; GLAD, Grade Level Assessment Device; HI, hearing impairment; ID, intellectual disability; INDT–ASD, INCLEN Diagnostic Tool for Autism Spectrum Disorders; INDT–EPI, INCLEN Diagnostic Tool for Epilepsy; INDT–NMI, INCLEN Diagnostic Tool for Neuro–Motor Impairments; IQ, intelligence quotient; LBW, low birth weight; LD, learning disorder; LMIC, low- and middle-income country; LPT, linguistic profile test; NDD, neurodevelopmental disorder; NFHS-3, National Family Health Survey-3; NMD, neuromuscular disorder; NMI-CP, neuromotor impairments including cerebral palsy; OAE, oto-acoustic emission; PAF, population attributable fraction; PDD-NOS, pervasive developmental disorder not otherwise specified; PPS, probability proportionate to population size; SC-ST, scheduled caste/tribe; SLI, Standard of Living Index; SQ, social quotient; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology; TAG, Technical Advisory Group; TQ, Ten Questions; UHC, Universal Health Care; VI, vision impairment; VSMS, Vineland Social Maturity Scale; WMA, World Medical Association

Introduction

“Neurodevelopment is a dynamic inter-relationship between genetic, brain, cognitive, emotional and behavioural processes across the developmental lifespan. Significant and persistent disruption to this dynamic process through environmental and genetic risk can lead to neurodevelopmental disorders and disability” [ 1 ]. Low-income communities and children living in poverty are disproportionately affected by NDDs [ 2 ]. Communities most vulnerable to NDDs often lack disease burden estimates to formulate policy decisions and implement programs to address NDDs [ 3 ]. To better understand the spectrum of childhood NDDs, there is a need to utilize valid and practical screening methodologies based on globally accepted disease definitions [ 4 ]. To date, global health policy makers have relied on national census disability data, even though such an approach grossly underestimates disability prevalence in children [ 5 ]. Censuses usually restrict themselves to the identification of gross and visible disabilities only and utilize nonspecialized assessors and diagnostic tools [ 5 , 6 ]. Global and societal leaders have urged that nations promote awareness of children with disabilities and advocate for their healthcare services [ 3 ]. The United Nations General Assembly [ 7 ] and Agenda for Sustainable Development [ 8 ] consider childhood disability an integral part of the global development agenda and promote the use of evidences that address national and regional contexts and are disaggregated by gender and age.

India has the world’s largest birth cohort (about 26 million) and is experiencing dynamic improvements in both infant and child survival [ 9 , 10 ]. The neonatal, infant and under-five mortality rates in India have shown significant decline during the last decade; improved survival of children and infants with high risk for NDDs is likely to result in higher community prevalence, lest interventions are instituted concomitantly [ 11 – 13 ]. To address the challenge of inadequate data, we convened a series of working groups to design and conduct a study of population-level prevalence of childhood NDDs in India through a transdisciplinary approach. Our aim was to determine the prevalence of NDDs among children aged 2–9 years in India and identify potential demographic and individual risk factors that could be applied at the national level. We decided to assess these children for nine common NDDs: vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), intellectual disability (ID), attention deficit hyperactivity disorder (ADHD), and learning disorders (LDs).

We have reported this study as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines ( S1 STROBE Checklist ).

Study design

We conducted a cross-sectional survey at five sites in India.Within each site, we used probability proportionate to population size (PPS) cluster sampling to select households for the survey.

Investigator group

A Technical Advisory Group (TAG) comprising 55 transdisciplinary experts (51 from India and four from the United States of America) provided guidance and oversight throughout the project. The TAG included experts in paediatrics (paediatric neurology, developmental paediatrics, general paediatrics), epidemiology, public health, social science, biostatistics, child psychology, oto-rhino-laryngology, ophthalmology, and child psychiatry from 18 institutions. The central coordinating office for the study was located at the INCLEN Executive Office in New Delhi.

Considering the major geographic (North, South, East, West), demographic, sociocultural and topographic (hilly terrain, plains, coastal regions) population heterogeneity in India, we selected one district each from five states across the country with the following characteristics: Kangra in Himachal Pradesh (Northern India; Himalayan terrain; altitude ranges from 427 to 6,401 metres; population: 94.3% rural; literacy: 85.7%); Palwal in Haryana (North-Central India; plains; population: 77.3% rural; literacy: 69.3%); Dhenkanal in Odisha (Eastern India; plains; population: 14.0% tribal, 90.0% rural; literacy: 70.6%); Hyderabad in erstwhile Andhra Pradesh (now Telangana) (Southern India; plains; population: 100.0% urban; literacy: 83.3%); and North Goa (West India; coastal; population: 59.9% urban; literacy: 90.7%) (demographic details taken from Census of India, 2011) [ 14 ]. The technical capacity of the potential collaborators and availability of infrastructural facilities and logistical support also contributed to decisions regarding site selection. Villages and municipal wards as listed in the Census register (Census of India, 2011) were the primary sampling units to select clusters of rural and urban areas, respectively. Fifty clusters were identified at three sites. At Hyderabad and North Goa sites, only 25 clusters were drawn due to limited finance availability. In total (five sites pooled), we surveyed 200 clusters (42 urban and 158 rural). The data was collected between 5 December 2011 and 27 September 2012.

Participants and recruitment

An “eligible participant” was defined as a child in the age range of 2–9 years (24–119 months) in the household. We requested the parent(s)/legal guardian(s) to provide written informed consent for participation in the study and to visit the study clinic for the diagnostic work-up of the child. We recruited 20 participants—five boys and five girls each from the age categories 2–<6 years and 6–9 years—in every cluster. A landmark located centrally in the cluster (e.g., temple, mosque, church, market place, panchayat office, central place) was identified with the help of local people. The clusters were virtually divided into two halves, for enrolling boys from one half and girls from the other. The direction for enumeration and choice of the first household of each half of the cluster was decided through a random number. For households with more than one eligible child, we recruited the eldest child in the even-numbered clusters and the youngest child in odd-numbered clusters. One of the parents of the child was the preferred respondent. Children who dropped out from diagnostic assessment after initial enrolment were replaced from the same cluster (matched for age category and gender) by continuing enumeration of consecutive households.

Two research teams were engaged for data collection at each site: (a) the field team comprising one physician and two social scientists, and (b) the diagnostic team comprising one physician, one audiologist/speech therapist, and two psychologists. The field teams identified the eligible participants, obtained consent, collected information on demographic characteristics including socioeconomic variables and risk factor variables at the participants’ residence, and thereafter mobilized them to the partner institution (tertiary care hospital) study clinic for detailed NDD work-up. At the hospital, under the supervision of the site investigators, members of the diagnostic team conducted the following: (i) physician assessments for VI, Epi, and NMI-CP; (ii) audiologist/speech therapist assessment for HI and speech and language disorders; and (iii) psychologist assessment for ASD, ID, ADHD, and LD.

Training of the research teams

The TAG prepared standard operating procedures and training modules for the research teams. Three-day structured training programs were conducted by a team of four multidisciplinary TAG members at partner hospitals for both field and diagnostic teams; 50% training time was allocated for hands-on activities in the field. Thereafter, one quality assurance visit per site by the TAG members was organized (all within eight weeks of starting the field work) to ensure adherence to the assessment protocols. Additionally, the central coordinating site conducted teleconferences with the site teams each time they completed data collection from two clusters.

Study variables and measurement

Personal details and information on sociodemographic variables, asset variables for Standard of Living Index (SLI), and biological and nutritional “risk factor” variables relevant for NDDs were obtained for study participants. At the study clinics, every participant was assessed for NDDs as per the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) guidelines. Prior validation, reported use among Indian participants, and feasibility of application in community settings were the criteria used by the TAG for zeroing in on the instruments used in the study ( Table 1 ). As diagnostic tools for Epi, NMI-CP, ASD, and ADHD satisfying the above criteria were not available, the INCLEN investigator team developed culturally adapted and context-relevant diagnostic tools for these four conditions and validated them against globally established guidelines and tools [ 15 – 18 ]. The instruments and examination formats for the NDDs were compiled into a child assessment booklet (CAB) and applied consistently on all participants across the study sites. NDD evaluations followed a specific algorithm to determine the clinical diagnosis. Since ADHD and LD could be assessed only in older children (6–9-year-olds), the younger study participants (2–<6-year-olds) were assessed for seven NDDs, and children aged 6–9 years were assessed for all nine NDDs. A subcommittee of the TAG reviewed the data for resolution of diagnostic ambiguity.

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https://doi.org/10.1371/journal.pmed.1002615.t001

Sample size

Based on previous NDD prevalence reports from South Asian countries [ 28 ], we assumed that about 10% of children aged 2–9 years could have at least one NDD. For an admissible absolute error of ±2% at 95% confidence level, we arrived at the sample size of 864 participants per site. Taking into account potential nonresponses and operational feasibility issues, 1,000 children were enrolled per site. Due to paucity of resources and personnel, we reduced the sample size to half at two sites (Hyderabad and North Goa); this increased the admissible absolute error to ±2.65% for these sites.

For analysis, we used Microsoft Excel 2010 and STATA v12.0 [ 29 ]. Our analysis was guided by a prospectively written statistical analysis plan at the time of preparation of this proposal for funding ( S1 Text ). Nutritional status of participants (somatic measurements) was adjusted for WHO Z-scores using WHO AnthroPlus software [ 30 ].We computed the SLI score for every participant household, assigning scores for assets and household characteristics as per National Family Health Survey-3 (NFHS-3) 2005–2006 (Demographic and Health Survey for India–3 rd Round) [ 31 ] and, using NFHS-3 state-specific cutoffs, assigned quintile status to each household. Thus, we generated both SLI scores and quintile statuses for the participants. The study population was stratified according to age category (2–<6-year-old and 6-9-year-old), gender (boy/girl), place of residence (rural/urban), and religion (Hindu/non-Hindu) and standardized for these variables as per Census of India 2011 to arrive at respective district level estimates. The data from the five sites was pooled and weighed against Census 2011 population figures aggregated from respective districts for age, gender, place of residence (rural/urban), and religion (Hindu/non-Hindu). As Census counts were not available according to nutritional status of individuals, standardizing for stunting and low birth weight (LBW) could not be done.

The complex survey module of STATA (SVYSET and SVY) was used for estimating the prevalence and risk factors.

Risk factor analysis.

To identify risk factors associated with presence of NDDs, we performed multivariable logistic regression modelling on the background of SVY command of STATA. The independent variables for the regression were adjusted into the model using subject knowledge. These were history of consanguinity, mental or neurological illness in the family, medical complications during pregnancy, chorioamnionitis in the mother at the time of index pregnancy, birth order (≥3 versus <3), multiple pregnancies, place of delivery (institutional versus noninstitutional delivery), history of perinatal asphyxia, serious neonatal illness (requiring hospitalization), traumatic brain injury, postnatal neurological infections (e.g., meningitis, encephalitis), LBW and/or prematurity, and stunting. Gender (boy/girl), place of residence (rural/urban), education status of the respondent (“never been to school”/“ever been to school”), religion (Hindu/non-Hindu), caste (SC-ST/rest), SLI score (continuous variable), and age category (6–9-year-old versus 2–<6-year-old) were also adjusted into the model. We estimated the population attributable fraction (PAF) [ 32 , 33 ] for the final multivariable logistic model.

Data cleaning and processing

All CABs were scrutinized at two levels: by the research teams and the site investigators for consistency and completeness of diagnostic assignment and again at the central coordinating office. Discrepancies discovered at central coordinating office were resolved through communication with the site investigators (weekly teleconference) and TAG review (quality assurance site visits and reviewing of CAB by TAG subcommittee). TAG members rechecked the diagnostic assignment in 10% of CABs of participants (chosen randomly) with NDD and an equal number without NDD and did complete review of all participants labelled as “indeterminate.”

Informed written consent was solicited from parents/guardian of the children prior to inclusion in the study.

Ethics considerations

Ethics approval was obtained from the institutional review boards of India-CLEN (Indian Network of INCLEN) and the participating sites. The study was conducted in compliance with the World Medical Association (WMA) Declaration of Helsinki [ 34 ]. Children identified with “any NDD” and/ or other medical conditions during the course of the study were referred to the nearest appropriate tertiary care centre for management and followed up with due counselling.

Table 2 illustrates the study recruitment profile for each site. Overall, the field teams approached families of 4,739 children, of which 739 (15.6%) refused to participate. After enrolling the targeted 4,000 children, 181 (4.5%) refused to complete diagnostic assessment, of which 158 (4.0%) could be replaced with age category and gender-matched children from the same cluster. Post hoc analyses revealed that dropouts and their replacements also matched for anthropometric measurements and sociodemographic characteristics ( S1 Table ). The NDD assessment could be done in 3,977 children (83.9% of the total approached; 99.4% of those enrolled).

https://doi.org/10.1371/journal.pmed.1002615.t002

During data review at the central coordinating office, errors in score computation done at field sites were corrected in 358 out of 3,977 records (9%); 179 (4.5%) CABs were returned to the primary site for completion, and all were obtained back. For quality assurance, the TAG reviewed 50 random CABs per site for cases labelled with or without NDD, respectively, and found no disagreements. The TAG reviewed an additional 174 CABs labelled as “indeterminate”; of these, a definitive diagnosis was made in 161, and the remaining 13 were excluded (due to incomplete information) from the study analysis, leaving a final number of 3,964 participants (2,006 boys and 1,958 girls) for analysis.The background characteristics of study participants are summarized in Table 3 .

https://doi.org/10.1371/journal.pmed.1002615.t003

Prevalence of NDDs

Overall, we found 475 of 3,964 children (between the ages of 2 and 9 years) had at least one NDD (12.0% [95% CI 11.0%–13.0%]). Among children with NDDs, 21.7% (95% CI 18.1%–25.7%) had two or more NDDs; children with ASD (79.6%), NMI-CP (74.2%), ID (56.9%), and Epi (55.1%) most frequently had coexisting NDD(s) ( S2 Table ). The district level prevalence estimates for NDDs across the five sites, upon weighing for age category, gender, place of residence (rural/urban), and religion (Hindu/non-Hindu) as per Census 2011, is provided in Table 4 . There was site-specific variation in the prevalence: Dhenkanal had the lowest and Palwal the highest prevalence of NDDs. HI, ID, speech and language disorders, Epi, and LDs were among the most common NDDs across the sites.

https://doi.org/10.1371/journal.pmed.1002615.t004

There were no significant differences in the all-sites-pooled data for gender, place of residence, and religion weighed as per Census 2011: prevalence among boys 12.4% (95% CI 10.2%–15.0%) versus 10.2% (95% CI 8.4%–12.2%) in girls ( p = 0.146); rural areas 12.6% (95% CI 11.4%–13.9%) versus 10.1% (95% CI 7.9%–12.8%) from urban areas ( p = 0.085); and in non-Hindu families 11.7% (95% CI 8.5%–15.9%) versus those from Hindu families 11.0% (95% CI 9.7%–12.5%) ( p = 0.727).

Clinical profile of NDDs.

Of the 62 cases of NMI-CP, 46.8% had spastic cerebral palsy (CP), 22.6% had neuromuscular disorder (NMD), and 30.6% had other NMIs. ID ( N = 144) was assessed as mild (27%), moderate (13.8%), or severe and profound (3.5%); in the remaining 55.6% (80/144), severity could not be determined, primarily due to associated comorbidities (62/80; 77.5%). Out of 44 cases of ASD, 52.3% were diagnosed as autism, 2.3% as Asperger, 38.0% as pervasive developmental disorder not otherwise specified (PDD-NOS), and 7.1% were diagnosed to have childhood disintegrative disorders (CDDs). In 27 cases of ADHD, 44.4% had inattention, 11.1% had hyperactivity/impulsivity, and the remainder (44.4%) were of mixed type. The assessment tool for Epi was not designed to determine the type of Epi.

Risk factors analysis

S3 Table enlists risk factor information for the study participants with and without NDDs. Table 5 provides the independent risk factors in the multivariable regression model: noninstitutional delivery, history of perinatal asphyxia, history of neonatal illness, postnatal neurological (brain) infections, stunting, LBW (<2.5 kg)/prematurity (gestation <37 weeks), and older (6–9 years) age category were statistically significantly associated with “any NDD.” The PAF was 36.8% (95% CI 27.2%–45.1%) for modifiable and statistically significant risk factors. We also undertook multivariable analysis for identifying risk factors for specific NDDs ( S4 Table ). The risk factors for “any NDD” were variably present with specific NDDs as well.

https://doi.org/10.1371/journal.pmed.1002615.t005

The study reports the prevalence of NDDs in children aged 2 to 9 years obtained through a population-based, multisite survey across five regions in India. The prevalence of NDDs varied across the five study sites despite using the same diagnostic tools with application of consistent methodology and training for the assessors. NDD prevalence might truly vary across regions, particularly in a large country like India, due to heterogenous distribution of risk factors and biological factors, if any. Dhenkanal, situated in central Odisha, with a sizeable tribal population, has high under-five mortality rate (80 per 1,000 live births) [ 35 ] and is endemic for hemoglobinopathies [ 36 ] and cerebral malaria (cerebral malaria in Odisha has up to six times higher risk of mortality among children) [ 37 , 38 ]. It has been reported that the risk of mortality in children with NDDs may be high in environments of poor economic development, weak health systems, and high child mortality [ 4 , 39 ]. The higher prevalence of HI in Palwal contributed to a higher overall prevalence of NDD in this site over others. According to an earlier study, repeated respiratory infections and high rates of chronic suppurative otitis media were associated with a high rate of deafness in 5–15-year-old children in the area [ 40 ].

Literature suggests that the prevalence of NDDs and their profiles vary considerably within and between geographies [ 4 , 41 – 45 ]; this has commonly been attributed to methodological variability [ 4 ] but can also be seen in studies that used common diagnostic tools and criteria and common data collection methods across sites. For example, based on parent-reported diagnosis, Boyle and colleagues [ 46 ] reported the prevalence of ASD in the United States of America to be 0.7%, but when systematic community-based assessment was done at 11 sites by the Autism and Developmental Disabilities Monitoring Network, overall prevalence of ASD was almost twice that of the previous study (1.5%) [ 47 ]. The Ten Questions (TQ) screen for disability was uniformly applied in a study of 18 low- and middle-income countries (LMICs); the prevalence of children aged 2–9 years at high risk for disability ranged between 3% (Uzbekistan) and 48% (Central African Republic), with a median prevalence of 23% [ 48 ]. In the two-phased studies with TQ in the similar age group, the prevalence of moderate to severe forms of disability varied between 0.5% and 9.4% in several LMICs [ 49 – 51 ].

In the Indian census form, there are four questions for collecting information on eight severe and obviously visible disabilities, including NDDs. According to Census of India 2011 data, the prevalence of all disabilities in the age category 0–4 years was 1.1%, and in the age category 5–9 years, it was 1.5% [ 52 ]. These figures were almost 10 times less than what we reported in the present study after a systematic, comprehensive, community-based assessment exclusively for NDDs; the study findings were likely to present a more factual picture of the population-based prevalence of NDDs in different parts of India.

The cumulative prevalence of NDDs was higher in the older age category (6–9 years) as compared to the younger age category (2–<6 years). According to published literature, observation of higher prevalence of NDDs with increasing age in childhood might be due to the following: (a) accumulation of childhood-onset causes and exposures, such as infections, injuries, and nutritional deficiencies; and (b) increased ability to recognize and diagnose conditions such as CP, ID, and behavioral disorders as age advanced [ 53 ]. In the regression model in which we fitted age as a categorical variable (6–9-year-old versus 2–<6-year-old, as a reference), it emerged as a statistically significant predictor of NDD ( Table 5 ). We could have also possibly arrived at this observation because participants in the older age category were assessed for two additional NDDs (i.e., ADHD and LD).

Almost one-fifth of the children with NDDs had another comorbid NDD. Similarly, there was clustering of risk factors in the same individuals. We looked for the various modifiable risk factors significantly associated with occurrence of any or a cluster of NDDs in the same individual. The odds of any NDD among children with two or more risk factors and three or more risk factors were 2.1 (95% CI 1.5–2.9) and 2.6 (95% CI 1.7–4.0), respectively. Walker and colleagues have suggested that cumulative exposure to risks starting from the prenatal period affects the developmental trajectories and lays the foundation for NDDs; these risk factors therefore require early identification and integrated interventions [ 54 ]. The frequent coexistence of ASD, NMI-CP, ID and/or Epi in ( S2 Table ) could be suggestive of common precursor events [ 55 ].

The results of the study have to be interpreted in the light of its limitations; 15.6% refusal to participate might be reflective of sociobehaviroal complexities associated with a child with NDD in the family (e.g., disability, fear, denial, guilt, blame, different dimensions of stigma and discrimination) that could in turn influence participation in such diagnostic evaluations [ 56 , 57 ]. The study sample was not representative of India ( S5 Table ); particularly, the sample was underrepresentative of stunting and LBW, and that could be contributing to underestimation of the true NDD burden in the study population. The stunting and LBW status of the children were not available in the Census data and therefore could not be adjusted together with other variables like age, gender, place of residence, and religion. The national rates (NFHS-4, 2015–2016) of stunting (38.4%) and LBW (18.2%) were available, and when these were applied separately to all-site-pooled estimates, prevalence of NDD in the 2–<6 year age category increased from 9.2% to 10.9% and 12.3%, respectively. Similarly, in the 6–9 year age group, weighing for these conditions led to increase in the estimates from 13.6% to 16.8% and 14.0%, respectively. The study was not designed to determine the social bias of “gender-selective treatments” on child survival, growth, and development [ 58 ]. The group has now validated a revised version of the Epi instrument to ascertain the types of Epi, including some of the ones not detected with the present tool [ 59 ].

Our study provided the first population-based multisite data on the burden of NDDs in 2–9-year-old children from India. We developed four new diagnostic tools that were culturally adapted and validated against international guidelines that use global normative data [ 15 – 18 ]; the tools were applied in the field by trained professionals with stringent quality control frameworks. While measurement of clinical conditions such as lack of vision or Epi is fairly comparable to other international measures (and prevalence rates are thus directly comparable), the same does not apply to the assessment of cognitive, attention, and learning skills, in which disability estimates would likely be substantially different if high-income population norms would be applied instead of an Indian reference population.

The study highlighted NDDs in children as an important public health challenge of considerable significance with substantial within-country variations. Most of the significant risk factors identified through the study were modifiable and could potentially be addressed by investments in public health to improve maternal/newborn care and child nutrition. Several emerging economies have been able to address these risk factors effectively [ 60 ]. The findings were important in the context of the Universal Health Care (UHC) agenda of the Government of India and the recently launched national child health program (“Rashtriya Bal Swasthya Karyakram”) [ 61 ]. These data will have relevance for India’s response to the needs of the children who are most vulnerable and their families and might also inform the policies of other countries that share similar socioeconomic milieu and healthcare situations, particularly in South Asia [ 54 , 62 ].

Supporting information

S1 strobe checklist..

https://doi.org/10.1371/journal.pmed.1002615.s001

S1 Table. Comparison of “Drop-out” ( N = 181) and “Replacement” ( N = 158) participants in the study sites.

https://doi.org/10.1371/journal.pmed.1002615.s002

S2 Table. Coexisting NDDs (by percentage) in study participants with at least one NDD.

NDD, neurodevelopmental disorder.

https://doi.org/10.1371/journal.pmed.1002615.s003

S3 Table. Distribution of the risk factors among children with and without NDDs.

https://doi.org/10.1371/journal.pmed.1002615.s004

S4 Table. Multivariable logistic regression analysis for risk factors for specific NDDs in children aged 2–9 years.

https://doi.org/10.1371/journal.pmed.1002615.s005

S5 Table. Comparison of demographic features of the participant population with national databases.

https://doi.org/10.1371/journal.pmed.1002615.s006

S6 Table. Summary description of four diagnostic tools developed and validated by INCLEN team for the study.

https://doi.org/10.1371/journal.pmed.1002615.s007

S1 Text. Prospectively written statistical analysis plan.

https://doi.org/10.1371/journal.pmed.1002615.s008

S1 Data. Palwal.

https://doi.org/10.1371/journal.pmed.1002615.s009

S2 Data. Kangra.

https://doi.org/10.1371/journal.pmed.1002615.s010

S3 Data. Dhenkanal.

https://doi.org/10.1371/journal.pmed.1002615.s011

S4 Data. Hyderabad.

https://doi.org/10.1371/journal.pmed.1002615.s012

S5 Data. Goa.

https://doi.org/10.1371/journal.pmed.1002615.s013

S6 Data. All.

https://doi.org/10.1371/journal.pmed.1002615.s014

S7 Data. Analysis file.

https://doi.org/10.1371/journal.pmed.1002615.s015

Acknowledgments

The authors acknowledge the partner institutions for participating in the study. We are deeply indebted to the communities and families who participated in the study.

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The following are some case studies of dyslexics with whom we have worked over the past years. In each story, we provide background information, the course of therapy that integrates the individual's strengths and interests, and the outcomes—all of which are positive.

Case Studies for using strengths and interests

Case Study One:

Grace has a diagnosis of dyslexia. She has trouble with visual scanning, processing, and working memory. She also has difficulties with spelling and sequencing for problem solving. She has strong verbal skills and is artistic abilities. She learns well with color and when her hands are occupied.

Grace struggled with note taking because of her difficulties with spelling and visual scanning (looking from the board to her paper). Furthermore, she could not keep up and got "lost" in the lecture (particularly for subjects that were already difficult for her). Grace’s teachers thought that she was not putting forth the effort, because they often saw her daydreaming in class. When the therapist asked Grace about this, she admitted that sometimes she would daydream because she did not know where they were in the lecture. She also desperately wanted to blend in with her peers, so she looked to them to see what she was supposed to be doing. However, when she was permitted to follow along with a book that she could highlight in and make her own doodles and notes in the margins during the lecture, she was able to focus her energy on the teacher and have notes that she could refer back to later with all of the main points highlighted. Using Grace's kinesthetic learning style and preference for color, she was able to participate with her peers, decrease her anxiety in class, and develop a skill that will help her to learn better across the curriculum.

Due to her difficulties with sequencing, working memory, and reading, Grace struggled with numerical operations and story problems in math. Her problem solving skills were good when she could leverage her strengths: connecting abstract ideas and thinking at the macro level. Hence, when she could connect a concept to a real life problem, she could inevitably come up with a creative solution and grasp the concept; however, her poor numerical operations skills were still holding her back. The therapist remembered Grace's interest in color and tactile learning style and introduced her to a number of "hands-on" ways of solving the problem: calculating probability with colored marbles, using her fingers for multiplication, and solving equations with objects to represent the variables. In this manner, Grace not only grasped the concept that was presented at the macro-level, but using her love of color and keeping her hands moving she could reliably solve for the answer. Employing colored pencils for numbering steps or placing hash marks in multi-step directions helped Grace stay on point and not skip steps in complex problems. These strategies were incorporated into her 504 Plan and were communicated to her math teacher.

Case Study Two:

Amy has a diagnosis of dyslexia. She enjoys creative writing, fashion, and art. She is extremely bright and has a strong memory. She benefits from rule-based instruction. If you tell her a rule once, she will be able to recite it to you the next time you see her. She delights in being able to be the teacher and teach the rules herself or correct others’ errors.

Amy’s stories often jumped around without any cohesion or plot. The clinician suggested that Amy work on her stories on a daily basis. Amy drafted her stories about glamorous people and enjoyed illustrating their wardrobes. Her clinician helped her to expand and revise her story using a multi-sensory tool to teach her the parts of story grammar. She was able to revise her own story, by adding the components of a good plot (characters, setting, initiating event, internal response, plan, and resolution). With several revisions, she produced a well-developed story and colorful illustration that was framed and displayed. The combination of using Amy’s interests, learning style, and a powerful reinforcement (framing and displaying the finished product) lead Amy to become proficient in telling stories and in revising her own work.

Case Study Three:

Ryan has a diagnosis of PDD-NOS that affects his language, social, and literacy skills. He also struggles with anxiety. He has a number of interests including: pirates and treasure, cooking, watching his favorite TV shows, and drama. Ryan has a strong memory and conveys a great deal of social knowledge when he is acting or drawing.

Due to Ryan’s anxiety associated with reading and writing, he often protested and completely shut down when presented with something to read or write. Ryan watched a number of shows that taught lessons about friendship or had a “moral to the story.” He was able to take some of those themes and stories and modify them, inserting kids from his school as the characters, and adding himself as a character and narrator. Given his interest in drawing, he illustrated his story, and made it into a short book.

The clinician wanted to incorporate his interest in writing and illustrating stories to improve his social skills. The therapist suggested that Ryan make his story into a play, and that he could be the director. Through a series of role-plays, Ryan was able to overcome his social anxiety and invite a peer to act in his play. Numerous social skills were targeted: greetings, turn-taking, active listening, problem solving, and flexibility for handling unforeseen circumstances. Ryan has now directed four plays, and has written countless others. To date, five of his peers have come and acted in his plays. (It has become a “cool” thing to do in Ryan’s social circle). He has gained a great deal of confidence in relating to his peers and in his strength of writing and directing plays.

In addition to social skills, Ryan has struggled with reading and following directions, asking for clarification, and comprehending and using abstract vocabulary. These areas were addressed using his interests in cooking and treasure hunts. Ryan participated in a number of baking projects that required him to locate the directions on the package, sequence and follow each step in a sequence, and determine the meaning of new vocabulary. Since this was in a context that he enjoyed, his attention was high and his anxiety was non-existent. Furthermore, Ryan had the opportunity to learn a new recipe and build on his strength for baking. Since his learning was in context, he was able to remember the meanings of abstract vocabulary. Ryan’s social skills were targeted when he went to the various offices in the building and offered his baked treats. He inevitably received positive social feedback.

Another motivating context for boosting Ryan’s reading for directions and vocabulary skills was participating in scavenger hunts around the building. He enjoyed the challenge of complex directions because there was an element of surprise and adventure. There was a notable consequence if he incorrectly followed the directions. This created the opportunity for Ryan to ask for directions or seek clarification. Since his learning was in context (i.e., he was looking at a fire extinguisher when he was reading the word for the first time), it was memorable. Many conjunctions (but, therefore, so, if) and sequence words (when, at the same time, before, after, next) were targeted multiple times, which led to mastery. This multi-sensory activity was enjoyable for both Ryan and the clinician. For Ryan, it resulted in greater participation, gains, and retention than traditional teaching approaches.

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The 100 Top-Cited Studies on Dyslexia Research: A Bibliometric Analysis

Shijie zhang.

1 Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China

Yonggang Zhang

2 Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China

3 Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China

Associated Data

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Background: Citation analysis is a type of quantitative and bibliometric analytic method designed to rank papers based on their citation counts. Over the last few decades, the research on dyslexia has made some progress which helps us to assess this disease, but a citation analysis on dyslexia that reflects these advances is lacking.

Methods: A retrospective bibliometric analysis was performed using the Web of Science Core Collection database. The 100 top-cited studies on dyslexia were retrieved after reviewing abstracts or full-texts to May 20th, 2021. Data from the 100 top-cited studies were subsequently extracted and analyzed.

Results: The 100 top-cited studies on dyslexia were cited between 245 to 1,456 times, with a median citation count of 345. These studies were published in 50 different journals, with the “Proceedings of the National Academy of Sciences of the United States of America” having published the most ( n = 10). The studies were published between 1973 and 2012 and the most prolific year in terms of number of publications was 2000. Eleven countries contributed to the 100 top-cited studies, and nearly 75% articles were either from the USA ( n = 53) or United Kingdom ( n = 21). Eighteen researchers published at least two different studies of the 100 top-cited list as the first author. Furthermore, 71 studies were published as an original research article, 28 studies were review articles, and one study was published as an editorial material. Finally, “Psychology” was the most frequent study category.

Conclusions: This analysis provides a better understanding on dyslexia and may help doctors, researchers, and stakeholders to achieve a more comprehensive understanding of classic studies, new discoveries, and trends regarding this research field, thus promoting ideas for future investigation.

Introduction

Dyslexia is a common learning disorder that affects between 4 and 8% of children ( 1 – 3 ), and often persists into adulthood ( 4 , 5 ). This neurodevelopmental disorder is characterized by reading and spelling impairments that develop in a context of normal intelligence, educational opportunities, and perceptual abilities ( 4 , 6 ). Reading and spelling abilities can be affected together or separately. The learning abilities of children with dyslexia are significantly lower than those of their unaffected pairs of the same age. Generally, difficulties begin to show during the early school years. Dyslexia is a complex multifactorial disorder whose etiology has not been fully elucidated, and it has caused great social and economic burdens. Over the last few decades, the research on dyslexia has made some progress. For example, some studies have shown that dyslexia has a strong genetic background that can affect brain anatomy ( 7 , 8 ) and function ( 9 , 10 ). But a citation analysis on dyslexia that reflects these advances is lacking.

The publication of study results in scientific journals is the most effective strategy to disseminate new research findings. A high number of citations can indicate the potential of a paper to influence the research community and to generate meaningful changes in clinical practice ( 11 ). Citation analysis is a type of quantitative and bibliometric analytic method designed to rank papers based on their citation counts. The latest and up-to-date research findings on dyslexia are well-reflected in recent scientific papers ( 12 ), particularly in the most cited ones ( 13 , 14 ). By analyzing the most cited studies, especially the 100 top-cited studies, we can gain better insight into the most significant advances made in the field of dyslexia research over the course of the past several decades ( 15 ). This retrospective bibliometric approach has been used for many other diseases, such as diabetes ( 16 ), endodontics ( 17 ), cancer ( 18 ). However, to date, no bibliometric analyses have been conducted in the field of dyslexia. Therefore, the aim of the present study was to analyze the 100 top-cited studies in the field of dyslexia.

Materials and Methods

Search method and inclusion criteria.

This retrospective bibliometric analysis was conducted using the Web of Science Core Collection database. The Web of Science Core Collection is a multidisciplinary database with searchable authors and abstracts covering a vast science journal literature ( 19 ). It indexes the major journals of more than 170 subject categories, providing access to retrospective data between 1945 and the present ( 20 ). On May 20th, 2021, we conducted an exhaustive literature retrieval, regardless of the country of origin, publication year, and language. The only search term used was “dyslexia” and the search results were sorted by the number of citations.

Article Selection

Two authors independently screened the abstracts or full-texts to identify the 100 top-cited articles about dyslexia. Disagreements were resolved through discussion. Only studies that focused on dyslexia were included in subsequent analyses. Studies that only mentioned dyslexia in passing were excluded.

Data Extraction

The final list of the 100 top-cited studies on dyslexia was determined by total article citation counts. We extracted the following data for each article: title, authors, journal, language, total citation count, publication year, country, journal impact factor, type of article, and Web of Science subject category. If the reprint author had two or more affiliations from different countries, we used the first affiliation as the country of origin. If one article was listed in more than one subject category, the first category was selected. If one article had more than one author, we selected the first-ranked author as the first author and the last-ranked author as the last-author.

Data Analysis

SPSS 11.0 (Chicago, IL, USA) was used to count the frequency. We analyzed the following data: citation count, year of publication, country, the first author, journal, language, type of study, and Web of Science subject category.

Citation Analysis

The 100 top-cited studies on dyslexia based on total citations are listed in Table 1 . The total citation count for these 100 articles combined was 42,222. The total citation count of per study ranged from 245 to 1,456 times, with a median citation count of 345. Only 3 studies were cited more than 1,000 times, and the rest of the studies were cited between 100 and 1,000 times. The title of the top-cited study, which also had the largest mean citation per year count ( n = 91), was “Reading acquisition, developmental dyslexia, and skilled reading across languages: a psycholinguistic grain size theory,” which was published by Ziegler et al. in Psychological Bulletin in 2005 ( 21 ). The second top-cited study, which also had the second-highest mean citation per year count ( n = 80), was published by Vellutino et al. ( 22 ). In addition, we also identified the 100 top-cited studies on dyslexia based on mean citation per year, whose results were shown in Supplementary Table 1 .

The 100 top-cited studies on dyslexia based on total citations.

USA, the United States of America; UK, the United Kingdom.

The different journals of the 100 top-cited studies on dyslexia and their associated impact factors are listed in Table 2 . The 100 top-cited studies on dyslexia were published in 50 different journals, with the top three in frequency being “Proceedings of the National Academy of Sciences of the United States of America” ( n = 10), “Brain” ( n = 6), and “Journal of Educational Psychology” ( n = 6).

Journals of the 100 top-cited studies on dyslexia.

The journal with the highest total citation count was “Proceedings of the National Academy of Sciences of the United States of America.” However, the highest average citation count per study belonged to the journal “Psychological Bulletin.” The journal impact factors of the 100 top-cited studies on dyslexia ranged from 1.315 to 74.699. Of the 100 top-cited studies, 29 were published in a journal with an impact factor greater than 10. The standard “CNS” journals, with the exception of “Cell,” “Nature,” and “Science” published 2 and 3 studies, respectively. Regarding the top four medical journals, while the “New England Journal of Medicine” and “Lancet” published 2 studies each, no top-cited study was published by the “Journal of the American Medical Association” or the “British Medical Journal.”

Language and Year of Publication

The 100 top-cited studies on dyslexia were all published in English and were published between 1973 [by Boder et al. ( 23 )] and 2012 [by Norton et al. ( 24 ) and Peterson et al. ( 25 )] ( Table 3 ). The most productive years were 2000, 2001 and 2003, with 9, 8 and 8 published articles, respectively. The year of 2003 had the most total citations with a total count of 3,788 and an average citation count per study of 474.

Publication year of the 100 top-cited studies on dyslexia.

Countries and Authors

Eleven countries contributed articles to the 100 top-cited studies on dyslexia ( Table 4 ). Most of the articles were from the USA ( n = 53), United Kingdom ( n = 21), Canada ( n = 7), and France ( n = 6). In addition, the USA had the highest total citation count (23,129), and Italy had the highest average citation count per study (665).

Countries of the 100 top-cited studies on dyslexia.

As shown in Table 5 , there were 18 first-authors and 13 last-authors who published more than one of the 100 top-cited studies on dyslexia. Among them, Shaywitz SE published the most top 100 articles ( n = 7) on dyslexia as the first author, followed by Galaburda AM ( n = 3) and Pugh KR ( n = 3). And for the last author, 8 studies of the 100 top-cited studies on dyslexia research were published by Shaywitz BA who was the most productive.

Authors with at least two first-author or last-author publications in the 100 top-cited studies on dyslexia.

Publication Type and Web of Science Subject Categories

As shown in Table 6 , there were 71 studies in the form of an original research article, 28 studies in the form of a review article, and one study in the form of an editorial material publication. The total citation counts for each publication type were 27,812, 13,899, and 511, respectively. Although the type of original research article had the highest total citation count, it had the lowest average citation count per study. In addition, a total of 12 Web of Science subject categories were extracted. Among them, “Psychology” was the most frequent category associated with studies [35], followed by “Clinical Neurology” [15], and “Multidisciplinary Sciences” [15], “Neurosciences” [12], and “Education” [6]. Consistent with the number of studies, the subject categories of “Psychology” and “Clinical Neurology” also had the highest total citation counts (15,683 and 6,427, respectively). The “Behavioral Sciences” subject category had the highest average citation count.

Type of study and subject categories for the 100 top-cited studies on dyslexia.

Although retrospective bibliometric approach has been conducted in many other diseases, to our knowledge, no citation analyses have examined publications on dyslexia. Therefore, this study is the first comprehensive analysis summarizing several features of the most influential studies on dyslexia. It has been suggested that a highly cited study can be considered as a milestone study in a related field and has the potential to generate meaningful changes in clinical practice ( 26 ). We believe that the present analysis of the 100 top-cited studies on dyslexia may be beneficial to the research community for the following reasons. First, the present study not only provides a historical projection of the scientific progress with regards to dyslexia research, but it also shows associated research trends and gaps in the field ( 27 ). Second, our findings provide critical quantitative information about how both the classic studies and recent advancements in the field have improved our understanding of dyslexia ( 28 ). Third, the present analysis may help journal editors, funding agencies, and reviewers critically evaluate studies and funding applications ( 28 ).

Our analysis discovered that the 100 top-cited studies on dyslexia were published in 50 different journals. This may reflect the fact that the 100 top-cited studies on dyslexia were very multidisciplinary in nature, unlike the top studies of other fields (e.g., psoriatic arthritis) where there is a more inherent researcher bias for journal selection ( 29 ). Of the 100 top-cited studies, 29 were published in a journal with an impact factor >10, and 62 studies were published in journal with an impact factor >5. However, there were only five studies published in the standard “CNS” journals and only four published in the top four medical journals, which suggests that most dyslexia researchers are more inclined to choose the most influential journals in their respective professional fields when submitting articles ( 30 ). This is in marked contrast with some other fields (e.g. vaccines), where the majority of top-cited articles are published in either the standard “CNS” journals or in the top four medical journals ( 15 ). Several other factors, such as the review turnaround time, likelihood of manuscript acceptance, publication costs, journal publication frequency, will all invariably also affect a researcher's journal selection ( 13 , 20 ).

According to the results of our analysis, nearly 80% of the 100 top-cited studies on dyslexia were published between 1990 and 2005, and the years of 2000 was found to have the most publications. The increase of landmark publications between 1990 and 2005 might reflect an increase in the interest in dyslexia research or that researchers had made some important scientific breakthroughs during this time period. All the top-cited studies on dyslexia were published in English, likely because English is the most commonly used language for knowledge dissemination in the world.

The top countries with regards to total citation count and number of papers in the top 100 list were the USA ( n = 53) and United Kingdom ( n = 21), which accounted for ~75% of the 100 top-cited studies. The USA published the most studies from the list, and this is probably because some of the world's top research centers are located in the USA and likely also the USA receives more research funding ( 31 ). Furthermore, the most prolific first-author (Shaywitz SE) and last-author (Shaywitz BA) were also from the USA. It is also worth mentioning that China had two studies on the top 100 list, which attests to the improvement of our national scientific research community with regards to knowledge dissemination.

In the present study, there were more original research articles ( n = 71) than review articles ( n = 28), but the latter had higher average citation counts per study. These results indicate that even though researchers pay significant attention to new findings on dyslexia, they regularly use information from review articles to convey relevant points in their own papers. We found that “Psychology” was the most frequent subject category associated with the top 100 articles, which indicates that researchers have been working to find effective treatments for people with dyslexia and that research in this field will continue to progress.

Like with other bibliometric analyses, there are some study limitations that should be highlighted. First, the 100 top-cited studies were extracted from the Web of Science Core Collection, which might have excluded some top-cited studies from other databases, such as Scopus and Google Scholar. Second, there was no citation data for recently published studies. Third, self-citations might have substantially influenced the results of the citation analysis. Moreover, this was a cross-sectional study, which implies that the identified 100 top-cited studies could change in the future. Despite these limitations, this descriptive bibliometric study could contribute new information about the scientific interest in dyslexia.

In conclusion, the present analysis is the first analysis to recognize the 100 top-cited studies in the field of dyslexia. This analysis provides a better understanding on dyslexia and may help doctors, researchers, and stakeholders to achieve a more comprehensive understanding of classic studies, new discoveries, and trends regarding this research field. As new data continue to emerge, this bibliometric analysis will become an important quantitative instrument to ascertain the overall direction of a given field, thus promoting ideas for future investigation.

Data Availability Statement

Author contributions.

YZ and HF designed the study. SZ and YZ acquired the data and performed statistical analyses. SZ, YZ, and HF drafted the manuscript. All authors critically revised the article and approved the final version of the manuscript.

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.

Funding. This study was partly supported by National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2018B016).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2021.714627/full#supplementary-material

A systematic procedure for identifying and classifying children with dyscalculia among primary school children in India

Affiliation.

• 1 Regional Institute of Education, Mysore, India. [email protected]
• PMID: 12067188
• DOI: 10.1002/dys.214

This paper describes the procedures adopted by two independent studies in India for identifying and classifying children with dyscalculia in primary schools. For determining the presence of dyscalculia both inclusionary and exclusionary criteria were used. When other possible causes of arithmetic failure had been excluded, figures for dyscalculia came out as 5.98% (15 cases out of 251) in one study and 5.54% (78 out of 1408) in the second. It was found in the latter study that 40 out of the 78 (51.27%) also had reading and writing problems. The findings are discussed in the light of previous studies.

• Child Development
• Cognition Disorders / diagnosis*
• Cognition Disorders / epidemiology
• Cross-Sectional Studies
• Dyslexia / epidemiology
• Educational Measurement
• India / epidemiology
• Learning Disabilities / diagnosis*
• Learning Disabilities / epidemiology
• Mathematics*
• Sampling Studies