Prevalence Estimates of Neurodevelopmental Disorders (NDD) in a South Indian Population

Abstract

Background

Neurodevelopmental disorders (NDDs) represent a significant public health concern globally, yet comprehensive prevalence data in India, a nation with 1.4 billion inhabitants, remains scarce. Limited systematic investigations have hindered effective public health planning.

Purpose

This study aims to evaluate the prevalence of NDDs within a local Panchayath population in Kottayam, Kerala, employing a community-based methodology.

Methods

A two-phase cross-sectional study was conducted. Phase I involved a door-to-door survey to screen for NDDs, targeting the entire Panchayath population. In Phase II, individuals screened as at-risk underwent detailed clinical assessments. The collected data were analysed to determine the overall and specific prevalence of various NDDs.

Results

The overall prevalence of NDDs in the surveyed population (n = 26,465) after Phase II was 0.80% (1 in 125), with a significantly higher prevalence of 1.38% (1 in 72) in children under 12 years. The prevalence rates of specific disorders included epilepsy (0.38%, 0.50% in children), specific learning disability (0.10%, 0.29% in children), attention deficit hyperactivity disorder (0.05%, 0.32% in children), developmental language disorder (0.026%, 0.029% in children), and autism spectrum disorder (ASD) (0.02%, 0.06% in children).

Conclusion

A key strength of the study was its inclusion of the entire Panchayath population in Phase I, filling a significant gap in the literature on NDD prevalence at the community level in India. As one of the first community-level estimates, it underscores the need for targeted public health strategies, particularly for children. The findings offer crucial data to guide policymakers and public health officials in planning interventions to reduce the burden of NDDs in the region.

Keywords

Attention deficit hyperactivity disorder autism spectrum disorder developmental language disorder epidemiology epilepsy India specific learning disorder prevalence

Introduction

Neurodevelopmental disorders (NDDs), which pose significant public health challenges, are driven by anatomical and neurochemical impairments of the nervous system. leading to deficits in behavioural and mental functioning. These disorders typically manifest in early childhood and lead to developmental disabilities (DD) that affect psychosocial functioning throughout life.¹ NDDs include conditions like intellectual disability (ID), autism spectrum disorder (ASD), attention deficit-hyperactivity disorder (ADHD), and specific learning disorders (SLD), motor disabilities, and others.^{2, 3} These disorders typically involve early disruptions in brain developmental processes, impacting social interaction, communication, emotional regulation, learning, and cognitive development. Consequently, individuals with NDDs face challenges in reaching age-appropriate developmental milestones across personal, academic, and occupational domains.

Accurate prevalence estimates are vital for effective public health policies and resource allocation, particularly for conditions with lifelong impacts, as high-income countries have established datasets to manage neurological conditions.⁴ From 2019 to 2021, DD prevalence in the USA increased from 7.40% to 8.56% in two years.⁵ According to comprehensive data from 195 countries, over 52.9 million children under five have DD globally, with South Asia showing the highest prevalence—about one in ten affected.⁶ India reports significant rates of epilepsy and ID, while neighbouring China shows high ASD prevalence, indicative of regional variations. Unfortunately, 66% of countries, mainly low- and middle-income, lack sufficient NDD data, hindering the understanding of global and local patterns.⁴

India, with nearly 18% of the global population, faces rising non-communicable diseases, particularly NDDs, straining its limited healthcare resources.^{7, 8} Around 11.4% of Indian children aged two to nine years are affected by NDDs, equating to one in every eight children.⁹ Given India’s cultural diversity, a unified nationwide survey on NDD is impractical. Community-based surveys and multiscale assessments are essential to understand NDD prevalence across States and Union Territories. Niti Aayog, under the Ministry of Health and Family Welfare, publishes health index scores to monitor health system performance and facilitate cross-learning, with the 2019–2020 data ranking Kerala as the top-performing state, achieving a score of 82.20.¹⁰ Some studies in Kerala have primarily focused on specific NDDs like ASD,^{11, 12} ADHD,¹³ and epilepsy,¹⁴ employing random sampling. A notable district-level study screened various NDDs,¹⁵ but it was limited to a narrow age range. This highlights the need for an inclusive research strategy to assess the prevalence and impact of NDDs across diverse populations in India, enabling tailored healthcare policies to address these concerns.

To address these gaps, we conducted a door-to-door epidemiological survey in Puthuppally Panchayath, representative of Kerala’s demographics, to assess the prevalence of NDDs by age and gender among its residents. Our findings can be extrapolated to similar regions in the state with a moderate tropical monsoon climate.¹⁶ We carried out comprehensive screenings and clinical assessment camps to validate neurological disorders and determine the accurate prevalence rates, focusing on conditions like developmental language disorder (DLD), SLD, ASD, ADHD, and epilepsy.

This study significantly enhances understanding of NDD prevalence in India, providing insights for public health policies and interventions. Systematic documentation of these disorders is essential to develop sustainable healthcare strategies for affected individuals and families. Ultimately, this work will deepen our understanding of NDD patterns and guide future resource allocation and research initiatives.

Methods

The Institutional Ethics Committee approved the study protocol (IUCBR-IEC/Certificate/2018-I, dated 16.02.2018). The study had two phases: Phase I screened the general population for NDDs via a door-to-door survey by the Data Collection Team (DCT). Phase II involved clinical assessments of individuals with suspected disabilities conducted by a medical team.

Design of Survey Questionnaire

We developed a comprehensive questionnaire by modifying an existing version from ICCONS, Palakkad, Kerala.¹¹ This included additional inquiries on ADHD¹⁷ and Parkinson’s disease¹⁸ from dedicated screening tools. The finalised questionnaire was printed in English (Supplementary file S1) and Malayalam and structured into five colour-coded units for user-friendly navigation and enhanced visual appeal.

Each questionnaire booklet had a unique alphanumeric serial number for easy tracking and data management. The first page featured a statement from the institute’s head explaining the survey’s purpose and encouraging cooperation, along with a receipt noting the survey date and the name of the DCT.

The questionnaire included the following sections: (a) Section A collected sociodemographic information (age, sex, marital status, education, occupation) for all family members. (b) Section B screened for disabilities and noncommunicable diseases, gathering details on sudden or early family deaths, while assessing knowledge, attitudes, and practices (KAP) regarding disabilities. (c) Section C focused on specific disabilities (DLD, SLD, ASD, epilepsy, hearing and visual impairments, motor disorders, and ADHD) and included maternal details for individuals under 21. (d) Section D addressed geriatric disabilities, like stroke, Parkinson’s disease and dementia. (e) Section E examined lifestyle factors, including dietary habits, exercise, alcohol consumption, and medicinal approaches. To enhance data consistency and facilitate analysis, we opted for closed-response questions with binary options (YES/NO) instead of open-ended questions, which can complicate data coding. Additionally, blank spaces were provided at the end of each section for participants to include further information or for the DCT to note critical observations.

Pilot Study

Surveys are better addressed through pilot studies and/or trial sessions.¹⁹ Accordingly, we conducted a pilot study with patients and bystanders at our Community Health Centre to identify issues with our survey, such as spelling errors and ambiguous questions. This trial helped us determine the average completion time and highlighted redundant or sensitive questions for exclusion. Importantly, data from the pilot study were not included in the final survey results, ensuring the integrity of our main study data.

Training of the DCT

The DCT consisted of researchers and graduate students from various fields. Door-to-door surveys usually have low nonresponse bias, but interviewer influence can affect responses.²⁰ To address potential bias in door-to-door surveys, we provided extensive training and supervision during data collection, including hands-on demos to clarify uncertainties among team members.

Consent to Participate

The survey, medical camps, and peripheral blood sample collections from participants were conducted after obtaining informed written consent.

Conduct of Survey

We surveyed Puthuppally (22.4 sq. km; 9° 34’ 5” N, 76° 33’ 58” E), a semi-urban panchayat of 18 wards in Kottayam district, Central Kerala, from January to November 2018. According to Kerala’s Local Self-Government Department, Puthuppally has 7,688 households and a population of 29,784, resulting in a density of 1,329.64 individuals/km². The survey design was developed in consultation with local health officials (District Medical Officer, District Program Manager, Chief Medical Officer) and panchayat leaders. Accredited Social Health Activist (ASHA) workers assisted during the survey, enhancing navigation and response rates. Each questionnaire took 30–60 minutes, with an adult household member as the respondent.

Survey Data Analysis

To assess percentage coverage, survey booklets were verified against the ward-wise list, and data entry operators uploaded the raw data with unique alphanumeric codes for reference. Family members were classified as asymptomatic or suspected cases based on neurological disorder responses in Sections C and D. ‘YES’ responses scored 1, whereas ‘NO’ responses scored 0, with three or more points indicating suspected NDDs. The booklets were then organised by ward for future reference.

Clinical Assessment

We contacted individuals suspected of having five NDDs (DLD, SLD, ASD, epilepsy, and ADHD) through telephone invitations to medical camps at IUCBR & SSH. Led by a medical team approved by Kottayam’s DMO, the camps included a paediatrician, psychiatrist, neurologist, clinical psychologist, and nursing staff. Transportation was arranged for attendees, and each participant received a one-on-one examination. The paediatrician assessed children under 18, while the neurologist evaluated those with neurological symptoms. Clinical psychologists used psychometric tools such as the Binet-Kamat Test of Intelligence (BKT) for IQ measurement²¹ and CARS-2 for ASD diagnosis. Clinical psychologists utilised a variety of psychometric assessment tools to screen for NDDs, subsequently followed by the psychiatrist, who made diagnoses and recommended interventions. Assessment tools included the Binet-Kamat Test of Intelligence (BKT) for measuring IQ and mental age,²² the Childhood Autism Rating Scale-2 (CARS-2) for ASD diagnosis, the Conners Rating Scale for ADHD,²³ and the Vineland Social Maturity Scale (VSMS)²⁴ for assessing social age and functioning. Following assessments in nine Phase II medical camps, confirmed cases were advised to maintain regular follow-ups for ongoing management. Peripheral blood samples were collected from medical camp participants for further research, allowing for biochemical, cytogenetic, and genomic investigations.

Analysis of Prevalence

Prevalence was calculated by determining existing disability cases relative to standardised population size, expressed as a percentage per 100 individuals, aiding in understanding the disability’s impact and enabling demographic comparisons.

Results

We surveyed 26,465 individuals (48.54% male; 51.46% female) from 6,771 households, ranging from newborns (five days) to adults (108 years), including six centenarians. Participants were grouped by age: children (up to 12 years: 12.84%), teenagers (13–19 years: 9.53%), adults (20–60 years: 56.8%), and elderly (above 60 years: 20.47%). Ninety-five participants did not disclose their age, creating an ‘Age not specified’ category. The female-to-male sex ratio was 1.06, below 1 for ages 0–12 and above 1 for all other age groups. Table 1 shows these data along with gender-specific details.

Table 1.

Analysis of Demographic Data of the Surveyed Population from Puthuppally Panchayath: Age and Sex-based Distribution.

Age Group	Male	Female	Total	Female: Male
Child (0–12 years)	1,749	1,649	3,398	0.94
Teenage (13–19 years)	1,234	1,289	2,523	1.04
Adult (20–60 years)	7,302	7,729	15,031	1.06
Elderly (above 60 years)	2,516	2,902	5,418	1.15
Age not specified	46	49	95	1.06
Total	12,847	13,618	26,465	1.06

In Phase I screening, we examined individuals suspected of having NDD (DLD, SLD, ASD, epilepsy, and ADHD) based on age and sex. Of 26,465 surveyed, 338 suspected individuals (1.28%) had one or more disabilities (Figure 1). Among them, 257 reported one disability, while 81 had multiple disabilities, totalling 470 reported disabilities. All suspected patients were contacted for Phase II assessments through medical camps (66 individuals) or psychological evaluations (258 individuals). We could not track 14 cases (4.14%) due to incorrect contact information or relocation.

Figure 1.

Structural Outline of NDD Screening Among the Surveyed General Population in Puthuppally Panchayath, Kottayam. Individuals Suspected of Having Various Clinical Features of NDD Were Evaluated Either Through Psychological Assessment and/or Medical Camp and then Categorised into DLD, SLD, ASD, ADHD, epilepsy, other NDDs and Non-NDDs. The Number of Individuals Involved in Each Stage is Specified in the Figure.

Of the 324 clinically assessed individuals, 212 (65.43%) showed one or more NDDs, including global developmental delay (GDD), stuttering, ID, and speech sound disorders, in addition to the five listed NDDs (Figure 1). Additionally, 58 individuals (17.90%) were diagnosed with other disabilities such as deafness, hearing impairment, cerebral palsy, and Down syndrome. The remaining 54 individuals (16.67%) exhibited typical development without any traits of DD.

Figure 1 presents the flowchart of the NDD screening process. Following the clinical assessment, a discrepancy was noted between suspected and confirmed disability cases, as shown in Table 2. Among confirmed cases, epilepsy had the highest concordance at 70.83%, followed by SLD at 33.33%, ADHD at 21.88%, ASD at 10.26%, and DLD at 5.04%. Table 2 indicates that ID, stuttering, GDD, and speech sound disorders were also diagnosed during Phase II screening.

Table 2.

Identified NDDs and Concordance Between Suspected and Confirmed Cases After Phase I and II NDD Screening Process.

Sl. No.	NDD Category	Suspected Cases Phase I (Male, Female)	Confirmed Cases Phase II (Male, Female)	Concordance (%) (Confirmed/Suspected)
1	Epilepsy	144 (79, 65)	102 (61, 41)	70.83
2	Specific learning disorder	84 (48, 36)	28 (21, 7)	33.33
3	Attention deficit hyperactivity disorder	64 (42, 22)	14 (14, 0)	21.88
4	Autism spectrum disorder	39 (22, 17)	4 (2, 2)	10.26
5	Developmental language disorder	139 (75, 64)	7 (4,3)	5.04
6	Intellectual disability	–	47 (25, 22)	–
7	Stuttering	–	8 (7, 1)	–
8	Speech sound disorder	–	1 (0, 1)	–
9	Global developmental delay	–	1 (1, 0)	–
	Total	470 (266, 204)	212 (135, 77)

Epilepsy was the most prevalent NDD, affecting 102 individuals, with an overall prevalence of 1 in 259 (0.38%) in the total population and 1 in 200 (0.50%) among children (Table 3). Figure 2 illustrates the distribution of epilepsy cases, which are idiopathic and with secondary causes, including excessive alcohol use, brain tumours, meningitis, and accidents. A five-year-old girl with a history of febrile seizures showed SLD, while two idiopathic epilepsy cases (one male and one female) displayed ID phenotypes (Table 4).

Table 3.

Information on the Prevalence of Neurodevelopmental Disorders (NDD) in the Total- or Child-population Population of Puthuppally Panchayath, with a Focus on Gender-wise Statistics.

Sl. No.	NDD Category	Prevalence in Total Population (%)			Prevalence in Child Population (%)
Sl. No.	NDD Category	Male	Female	Total	Male	Female	Total
1	Epilepsy	1 in 211(0.47)	1 in 332(0.30)	1 in 259(0.38)	1 in 175 (0.57)	1 in 236(0.42)	1 in 200(0.50)
2	Specific learning disability	1 in 612(0.16)	1 in 1945(0.05)	1 in 945(0.10)	1 in 350 (0.28)	1 in 330(0.30)	1 in 340(0.29)
3	Attention deficit hyperactivity disorder	1 in 918(0.11)	−	1 in 1890(0.05)	1 in 159(0.63)	−	1 in 309(0.32)
4	Autism spectrum disorder	1 in 6424(0.02)	1 in 6809(0.01)	1 in 6616(0.02)	1 in 1749(0.06)	1 in 1649(0.06)	1 in 1699(0.06)
5	Developmental language disorder	1 in 3212(0.03)	1 in 4539(0.02)	1 in 3781(0.03)	1 in 1749 (0.06)	−	1 in 3398(0.03)
6	Intellectual disability	1 in 514(0.19)	1 in 619(0.16)	1 in 563(0.18)	1 in 874(0.11)	1 in 550(0.18)	1 in 680(0.15)
7	Stuttering	1 in 1835(0.05)	1 in 13618(0.01)	1 in 3308(0.03)	1 in 1749(0.06)	−	1 in 3398 (0.03)
8	Speech sound disorder	−	1 in 13618(0.01)	1 in 26465(0.003)	−	−	−
9	Global developmental delay	1 in 12847(0.01)	−	1 in 26465(0.003)	−	−	−
Total		1 in 95 (1.05)	1 in 177 (0.57)	1 in 125 (0.80)	1 in 56 (1.77)	1 in 103 (0.97)	1 in 72 (1.38)

Figure 2.

The Graphical Distribution of Epilepsy Cases Based on Aetiology. Among 102 Patients with Confirmed Epilepsy After Clinical Evaluation, 80 had Idiopathic Epilepsy Cases, 16 had Febrile Seizures, Four had Epilepsy Cases Due to Secondary Reasons, and One had Iatrogenic and Gestational Epilepsy.

Figure 3.

Neurodevelopmental Disorder (NDD) Distribution by Age. The Prevalence of NDD in the Total Population is Indicated by the Red Dotted Line.

Table 4.

List of Comorbidities Associated with Various NDDs Identified in Puthuppally Panchayath.

Neurodevelopmental Disorders	Comorbidities	Individuals with the Comorbidity
Developmental language disorder	Learning disability	2
Developmental language disorder	Stuttering	1
Specific learning disorder	Epilepsy	2
	Inattention on the ADHD scale	1
	Tongue tie	1
Autism spectrum disorder	Intellectual disability features	1
Epilepsy	Learning disability at risk	1
	Intellectual disability features	2
	Muscle weakness	1
	Osteoporosis	1
Attention deficit hyperactivity disorder	Learning disability	3
	Learning disability and oppositional defiant disorder	1
	Learning disability and speech sound disorder	1
	Epilepsy and oppositional defiant disorder	2
	Oppositional defiant disorder	1
	Hydrocephalus	1
Intellectual disability	Epilepsy	11
	Autism spectrum disorder features	1
	Hemiparesis	1

Of the suspected SLD cases confirmed through clinical assessment, 28 individuals (33.3%) were confirmed, and 21 were males (Table 2). Prevalence rates were 1 in 945 (0.10%) in the general population and 1 in 340 (0.29%) among children (Table 3). Roughly 89% of confirmed cases were aged 2–19, primarily school-aged children, evaluated on their reading, writing, and math skills. Among male SLD cases, comorbid conditions included two cases of epilepsy and one of inattention as per the ADHD scale (Table 4).

All confirmed ADHD cases were males, with a prevalence of 1 in 1,890 (0.05%) in the population and 1 in 309 (0.32%) among children (Table 3). Approximately 57% of ADHD cases were comorbid with conditions like SLD, oppositional defiant disorder, speech sound disorder, and epilepsy (Table 4). The prevalence of ASD was lower than ADHD, found only in individuals under 19. It was 1 in 6,616 (0.02%) in the population and 1 in 1,699 (0.06%) among children (Table 3). One female teenager with ASD exhibited significant ID features.

The prevalence of DLD was 1 in 3,781 (0.026%) in the population, but only one male DLD phenotype was found in 3,398 children (Table 3). Among DLD cases, the comorbidity of learning disabilities and stuttering was observed. Other NDDs included ID (n = 47) and stuttering (n = 8), and single cases of speech sound disorder and GDD (Table 2).

In this study, the NDD burden was 1 in 56 (1.78%) after Phase I screening, reducing to 1 in 125 (0.80%) in the total population and 1 in 72 (1.38%) in children after Phase II (Table 3 and Figure 3), with affected males approximately twice that of females.

Discussion

This study employed a structured questionnaire to systematically survey the entire population of Puthuppally Panchayath in Kerala, identifying individuals with NDDs through a two-phase approach that combined community-based surveys with clinical assessments. Key findings include (a) high coverage of 88.07% among local households, which reinforced the reliability of our data; (b) a notable sex difference was observed, with a higher prevalence of NDDs in males; (c) the discrepancy between suspected and confirmed cases indicates underreporting of true prevalence; and (d) epilepsy emerged as the most prevalent NDD, followed by SLD.

The survey recorded a total NDD prevalence of 0.80% in the general population, escalating to 1.38% among children aged 12 years or younger. These figures are consistent with the 2011 Census of India, which reported NDD prevalences of 0.1% and 1.5% for the 0–4 and 5–9 age brackets, respectively.²⁵ Notably, while the overall incidence of NDD was higher in males (1.3:1), no female cases of ADHD were identified, possibly due to differences in behaviours that prompt recognition by parents and caregivers.

Of the 470 suspected NDD phenotypes identified, 144 were with epilepsy, 139 with DLD, 84 with SLD, 64 with ADHD, and 39 with ASD. A substantial 45.1% of the suspected NDDs (65.43% of NDD-suspected individuals) were only subsequently confirmed during the clinical assessment phase, aligning with findings from Nair et al.¹⁵ that underscore the merit of multi-phase assessments in prevalence studies. The observed incidence of ASD in our study aligns with previous research that reported a prevalence range of 0.23% to 1.8% among children in India,²⁶ as noted by Arora et al.⁹ It is important to highlight that their study utilised cluster random sampling from select regions, which may not represent the entire population. In contrast, our study did not limit its scope to a specific age group or focused explicitly on participants from special education schools or centres. Instead, it encompassed the entire local residential population, providing a more comprehensive assessment of ASD prevalence. Our study revealed a prevalence of 0.02% for the entire population and 0.06% specifically among children, with all identified cases classified as severe. This finding underscores the potential for underdiagnosis of milder and moderate cases.

The prevalence of ADHD exhibits significant variability across studies.²⁷ In our research, we did not identify any females exhibiting the ADHD phenotype. This may be attributed to the more pronounced tantrums and disruptive behaviours typically observed in males, which parents and caregivers readily notice. A previous school-based self-report study in Kerala reported prevalence rates of 1.8% for hyperactive ADHD, 1.4% for inattention, and 4.3% for combined ADHD, with males being more frequently affected in all subtypes.¹³ In contrast, our findings suggest prevalence rates of only 0.05% in the general population and 0.32% among children, significantly lower than the global pooled prevalence of 5.29%, suggesting significant underreporting.²⁷ Notably, our data collection relied on feedback from family members during door-to-door surveys, which may introduce bias if they are reluctant to disclose full details.

After the second screening phase, there was a significant decrease in suspected DLD cases, likely due to overlapping phenotypic features with other disorders like deafness. Most individuals suspected of having NDD were later identified as non-NDD or having ID, with 47 confirmed cases post-screening (M = 25, F = 22). Among 15 ID cases, comorbidities included epilepsy (11 cases), ASD traits and hemiparesis (one case each). A review indicates the prevalence of SLD in India varies from 2.16 to 30.77%, averaging 10.70% among children.²⁸ Notably, boys are more affected than girls, with comorbidities such as ADHD²⁹ and anxiety disorders observed.³⁰ This study found a lower SLD incidence of 0.29% in children than reported in other Indian studies,³¹ emphasising the variability across research due to methodological differences.^{9, 32}

Our study provides an overall account of the prevalence of various NDDs in a small local panchayath in Kottayam District in Kerala. The findings stress the necessity of addressing NDDs as a significant public health challenge for children, reinforcing the importance of initiatives such as Kerala’s ‘Our Responsibility to Children’ and the National Health Mission’s ‘Rashtriya Bal Swasthya Karyakram’ to enhance early identification and interventions.^{33, 34} Children are the pillars of any nation, and their physical and mental health is important for a nation’s economic and social development.

Conclusion

To our knowledge, this study represents the first nationwide, population-based analysis that examined the age- and sex-specific prevalence of NDDs within the general population without random sampling. Continued efforts in this area are vital for improving outcomes for children affected by NDDs and national socio-economic development. Understanding the age and sex-specific prevalence of various NDDs has significant implications for public health service planning, national policy formulation, and the effective delivery of healthcare services to the community. Such insights will aid in tailoring interventions and resource allocation to meet the specific needs of different demographic groups affected by these disorders.

Footnotes

Acknowledgement

The sincere support from the residents and the elected members of the local panchayath, District Program Manager-Kottayam, District Medical Officer-Kottayam, Asha workers and health workers during the survey, medical camps and assessment is gratefully acknowledged. The authors also thank the support from the student volunteers and the respective college authorities. Moreover, we thankfully acknowledge the assistance from administrative staffs, technicians and other research fellows of IUCBR & SSH for complete cooperation and helps during the study period. We are also grateful to the various funding (as given in the funding declaration) by Government of India agencies (ICMR, DST, CSIR, MHRD) and Government of Kerala agencies (DHE & DHFW) for supporting this research.

Authors’ Contribution

Conceptualization: UR, KPM.

Questionnaire preparation-Modification & Editing: KSN, VRR, KPM, UR.

Data curation-Survey: KSN, RLG, VRR, RPA, CVS, RRM, RAS, GC, KPM, UR.

Data curation-Clinical diagnosis & assessment: JN, RK, KKN, AT, KSN, RLG, VRR, RPA, CVS, RRM, RAS, DV, GC, KPM, UR.

Medical experts: JN, RK, KKN, AT.

Funding acquisition: UR, KPM.

Data analysis and validation: KSN, RLG, KPM, UR.

Supervision: UR, KPM, GC.

Writing-original draft: KSN, RLG, KPM, UR.

Writing-review and editing: KSN, RLG, VRR, RPA, CVS, RRM, RAS, JN, RK, KKN, AT, DV, GC, KPM, UR.

Manuscript Final Submission: KSN, RLG, KPM, UR.

KSN and RLG equally contributed to the study.

Consent to Participate

The survey and medical camps were conducted after obtaining a informed written consent from the participants. The questionnaire for the survey containing the consent page and the informed written consent documents is attached as supplementary files.

Consent for Publication

The consent for publication of the generated data was provided by the participants while providing informed written consent.

Data Availability Statement

Data that support the findings of this are available from the corresponding author, upon reasonable request.

Statement of Ethics

The study protocol was reviewed and approved by the Institutional Ethics Committee (IUCBR-IEC/Certificate/2018-I, dated 16.02.2018).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was partly supported by the grants from Departments of Higher Education (DHE), Department of Health & Family Welfare (DHFW), Government of Kerala and the Ministry of Human Resource Development, Government of India, under the MHRD-SPARC scheme sanctioned to UR, GC & KPM (No.: SPARC/2018-2019/P1215/SL dated 15.03.2019). The authors gratefully acknowledge the fellowship-awards to KSN (3/1/2/117/Neuro/2019-NCD-1), RLG (3/1/2/118/Neuro/2019-NCD-1), RRM (3/1/2/119/Neuro/2019-NCD-1) and DV (3/1/13/Neuro/139/2020-NCD-I) from Indian Council of Medical Research (ICMR), Government of India; to VRR (DST/WOS-B/2017/494-HFN) from Department of Science and Technology (DST), Government of India, and to CVS (09/1252(0001)/2019-EMR-I) from Council of Scientific and Industrial Research (CSIR), Government of India.

Supplemental Material

ORCID iDs

Chinthu V Saji

Deepthi Varughese

Kochupurackal P Mohanakumar

Usha Rajamma

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