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. 2021 Jan 23;11(2):192–199. doi: 10.1016/j.jobcr.2021.01.011

Prevalence of malocclusion among 8–15 years old children, India – A systematic review and meta-analysis

Parvathy Balachandran 1, Chandrashekar Janakiram 1,∗
PMCID: PMC7896144  PMID: 33659178

Abstract

Objective

We designed this review to assess the prevalence of malocclusion among 8–15 years old Indian children.

Methodology

The review protocol was registered in PROSPERO data with register number CRD42020214211. We employed the standard methodological procedures according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Electronic search was done in PubMed database and other sources in 2020 to identify studies. Only studies published in English after January 1, 2000 that assessed prevalence of malocclusion using Dental aesthetic Index (DAI) or Angle’s classification of malocclusion were considered for screening. Selection of articles, data extraction and validity assessment were done independently by the two reviewers.

Results

Pooled prevalence of malocclusion is 35.40% (CI:35.37–35.43, 54 studies, 97959 participants). Males had higher proportion of malocclusion (36.20%, CI: 36.12–36.28,33 studies, 40456 participants). 13 years had higher prevalence of malocclusion (33.50%, CI:33.34–33.66, 11 studies, 3366 participants).Prevalence of malocclusion was higher among urban population (32.78%, CI:32.71 32.85,11 studies, 18313 participants). South India showed higher prevalence of malocclusion (39.58%, CI:39.54–39.62, 41 studies, 58645 participants). Prevalence of malocclusion as assessed by mean DAI score was 21.23 (CI:21.14–21.33,11 studies, 12345 participants).

Conclusions

The pooled prevalence of malocclusion among 8–15 years children in India is 35.40% (CI:35.37–35.43,54 studies, 97959 participants).Included studies were heterogeneous in their methods of assessment of malocclusion.

Keywords: Prevalence, Malocclusion, Children, Dental aesthetic index, Angle’s classification of malocclusion, India

Abbreviations: DAI, Dental Aesthetic Index; I, Confidence Interval; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; CJ, Chandrashekar Janakiram; PB, Parvathy Balachandran; RV, Ramanarayanan Venkitachalam

1. Introduction

The World Health Organization, in 1987, defined malocclusion under the heading handicapping dentofacial anomaly, as “an anomaly which causes disfigurement or which impedes function, and requires treatment if the disfigurement or functional defect was likely to be an obstacle to the patient’s physical or emotional well-being”.1Angle defines malocclusion as “any deviation from the normal occlusion of teeth”.2Often mistaken for a disease, malocclusion is a developmental condition that has a profound impact on the self-esteem and social acceptance of an individual.

The need for early detection and treatment of malocclusion is highlighted by its role in the development of periodontitis, dental caries, temporo-mandibular disorders and trauma. Moreover, malocclusion adversely affects oral functions like mastication, swallowing and speech.3Though reports suggest that malocclusion is the second most common dental disorder affecting schoolchildren, there is inadequate implementation of preventive oral healthcare programs.4 Adverse implication of malocclusion in the quality of life, social interactions and psychological development emphasize its early diagnosis and prompt treatment.

Lack of early interception of malocclusion pose challenges to a vast and densely populated country like India. An insight into the struggles of Indian oral healthcare system in combating conditions like malocclusion reflects insufficiency of necessary data. Appropriate epidemiological data is imperative for a multifactorial condition like malocclusion in a diverse country like India. Thus documentation of the prevalence and distribution of malocclusion enables determination of the size of the problem, problem analysis and plan future actions.5

Though various studies have assessed the prevalence of malocclusion among children, they were region specific. Compilation of these studies and quantifying the malocclusion among children in a meaningful manner is essential to understand the national scenario. Therefore, this systematic review was designed to generate nationally representative data on the burden of malocclusion among 8–15 years old children in India from previously published point prevalence studies.

2. Methodology

This systematic review was conducted in agreement with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Search method to identify relevant studies using focus question is:

Patients: Indian children of 8–15 years age

Intervention: Not applicable

Determinant: Prevalence

Outcome: Malocclusion

Focused question: What is the prevalence of malocclusion among 8–15 years old Indian Children?

The review protocol was registered in PROSPERO data with register number CRD42020214211.

2.1. Eligibility criteria

This systematic review was limited to cross sectional studies published in English language since 2000. In the included studies, the prevalence of malocclusion was assessed using Dental Aesthetic Index (DAI) or Angle’s classification of malocclusion (including Dewey’s modification). Studies that assessed the prevalence of malocclusion in children with special healthcare needs (e.g., visually challenged) or those with medically compromised subjects (e.g., children with haemophilia) were excluded. Also, studies that assessed the orthodontic treatment needs rather than prevalence of malocclusion were not included.

2.2. Outcome assessed

In this systematic review we considered the term “malocclusion” as any deviation from ideal occlusion. We considered the studies that assessed malocclusion either by Dental Aesthetic Index (DAI) or Angle’s classification of malocclusion (including Dewey’s modification comprising of further subdivisions of Angle’s class I and III).

2.3. Information sources and search

This review was undertaken in 2020. An electronic search was conducted using PubMed database using the search strategy consisting of MeSH terms: “prevalence” [All fields] AND “orthodontic”[All fields] OR “Dental Aesthetic Index” [All fields] AND “India” [All fields] AND “children” [All fields] and keywords like malocclusion,Angle’s classification of malocclusion. Bibliographies of retrieved studies were also reviewed. Studies published from January 1, 2000 till September 30, 2020 were included. The two authors (PB and CJ) independently reviewed all the titles. Duplicate studies from the obtained results were removed and remaining studies were examined by title and abstract. Subsequently, full texts were obtained and analysed for further inclusion/exclusion. Studies that did not meet the inclusion criteria were removed. Disagreement between the two reviewers was resolved by third party adjudication. Full text of the included studies was reviewed thereafter.

2.4. Data collection process and data items

Piloting of data extraction was done by one of the authors (PB) on two articles. Both authors (PB and CJ) agreed on the design of the data extraction form. A Microsoft Excel spreadsheet was used to include the following data – study authors, year of publication, journal in which the study is published, year in which the study was conducted, place of study, demographic characteristics of participants, sample size and sampling technique used, sample size in terms of gender, age range of the studied population, index used for assessment of prevalence of malocclusion, overall age and gender wise prevalence and severity of malocclusion. Weighted proportion difference was calculated for the outcomes (measured by different scales/indices) of each study. Based on the level of heterogeneity, random-effects model was used to calculate a pooled estimate of malocclusion and its 95% confidence intervals (CIs).

2.5. Quality assessment of included studies

The risk of bias assessment was done using the quality assessment checklist for prevalence studies (adapted by Hoy et al.). All included studies were assessed independently by the review authors. The risk of bias assessment tool evaluates nine specific domains – representation of the national population by the study population in relation to variables like age and gender, sampling frame and sampling method used in the study, randomisation in sampling process, data collection method, uniformity in data collection, presence of an acceptable case definition, reliability and validity of the instrument used for measurement of prevalence of malocclusion and the appropriateness of numerator and denominator used in the study. Each response had a value of 0 (for low risk) and 1(for high risk). Resultant values of each response were added and thus the overall risk of bias for each study was assessed. Summary of the overall risk of individual studies was evaluated as follows- low risk (0–3), moderate risk(4- 6) and high risk (7- 9).Review authors were not blinded to author information and source institution. Disagreement between the reviewers regarding quality assessment was resolved by discussion with a methodological expert (RV).

2.6. Synthesis of findings

The decision to pool studies was based on the absence of significant methodological heterogeneity in terms of patient demographic characteristics and the quality of studies. Subgroup analyses were performed for age, gender, place of the study (region wise and urban-rural areas), method of prevalence assessment and quality of study variables. Meta-analysis was performed using Meta XL software developed for use with Microsoft Excel. Pooled prevalence was calculated with 95% Confidence Interval (CI) based on the total sample size and number affected. Subgroup analysis was done with respect to gender (males and females), age group (12 years, 13 years, 14 years, 15 years), population sub-group (rural, urban), study quality (moderate and low), region (North, West and South India), method used for assessment of prevalence of malocclusion (DAI and Angle’s classification of malocclusion) and gender wise comparison of ages, urban-rural areas.

3. Results

3.1. Study selection

Electronic search from all sources retrieved 162 citations (Fig. 1.). After removal of duplicates, 154 records were left for screening. Screening the titles and abstracts yielded 58 studies (96 studies were excluded). From the 58 studies, four studies were excluded after full text screening, as they had missing values of outcome (prevalence of malocclusion).

Fig. 1.

Fig. 1

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Search strategy.

3.2. Study description

Selected characteristics of the included studies are summarized in Table 1. Meta-analysis was performed for 54 studies comprising of 97,959 study participants. Out of 97,959 participants, 40,456 were males and 36,938 were females. We included only those studies that were published in English language from the year 2000 onwards. Majority of participants were in 12 years and 15 years age groups. Highest number of studies were from South India - 41 studies. Based on the risk of bias assessment, 47 studies were found to be of low risk and seven studies were of moderate risk. 11 studies assessed malocclusion in urban areas and 14 studies assessed malocclusion in rural areas. Prevalence of malocclusion was assessed using DAI score and Angle’s classification of malocclusion in 42 studies and 11 studies, respectively. However, one study did not report the method used for the assessment of malocclusion. The pooled estimate of prevalence of malocclusion was estimated from the 54 studies (97959 participants) (Table 2; Fig. 2).

Table 1.

Characteristics of included studies.

Author, year Year of Publicatio-n G. zone Location State Total sample size Age group (years) Index used to measure malocclusion Quality assessme-nt
Singh 201916 2019 E Patna Bihar 902 12–15 DAI Low
Nagalekshmi 201717 2017 S Namakkal Tamilnadu 1078 12–15 DAI Low
Sivakumar 201618 2016 S Bylakuppe Karnataka 319 11–13 DAI Low
Jha 201419 2014 N Lucknow Uttar Pradesh 697 12–15 DAI Low
Sanadhya 20143 2014 W Kutch coast Gujarat 947 12–15 DAI Low
Damle 201420 2014 N Ambala Haryana 1322 12,15 DAI Low
Tak 201313 2013 N Udaipur Rajasthan 887 12–15 DAI Low
Chauhan 201321 2013 N Chamba etc Himachal Pradesh 1188 9,12 DAI Low
Anita 201322 2013 S Telangana Telangana 1000 12–14 DAI Low
Chandrashekar 201311 2013 S Nalgonda Andhra Pradesh 1268 15 DAI Low
Ahammed 201323 2013 165 12–15 DAI Moderate
Baskaradoss 201324 2013 S Kanyakumari Tamilnadu 1800 11–15 DAI Low
Singh A 20117 2011 S Udupi Karnataka 927 12 DAI Low
Nagarajan 201025 2010 S Bangalore Karnataka 1618 14–15 DAI Low
Shivakumar 201026 2010 S Davangere Karnataka 1800 12–15 DAI Low
Shailee 201327 2013 N Shimla Himachal Pradesh 963 12,15 DAI Low
Hegde 201028 2010 S Mangalore Karnataka 188 15 DAI Moderate
Pruthi 201329 2013 N Shimla Himachal Pradesh 961 12,15 DAI Low
John 201130 2011 S Chennai Tamil Nadu 613 12 DAI Low
Sundareswaran 201931 2019 S Calicut Kerala 1554 13–15 ACM Low
Prasad MG 201632 2016 S West Godavari Andhra Pradesh 2519 11–14 ACM Low
Prabhakar 20146 2014 S Chennai Tamil Nadu 532 8–13 ACM Moderate
Das 200833 2008 S Bangalore Karnataka 745 8–12 ACM Low
Sushanth 201534 2015 S Chennai Tamil Nadu 1800 12–13 DAI Low
Dhar 200735 2007 N Udaipur Rajasthan 1399 8–14 DAI Low
Singh M 201136 2011 N Barabanki Uttar Pradesh 598 8–14 DAI Low
Paphe 201237 2012 S Bagalkot Karnataka 1000 12–14 ACM Low
Pankaj 201038 2010 S Belgaum Karnataka 1600 12–15 DAI Low
Das 200939 2009 S Bangalore Karnataka 201 12 DAI Moderate
Sureshbabu 200540 2005 S Davangere Karnataka 300 13–15 DAI Low
Jose and Joseph 200341 2003 S Vadavucode Kerala 1068 12–15 Not reported Moderate
Mahajan 201742 2017 N Jammu Jammu Kashmir 696 13–14 ACM Low
Reddy 201943 2019 S Khammam Telangana 2550 10–12 ACM Low
Kumar 201744 2017 S Hyderabad Telangana 4732 10–12 ACM Low
Narayanan 201645 2016 S Kozhikode Kerala 2366 10–12 ACM Low
Shetty 201846 2018 S Mangalore Karnataka 1001 15 DAI Low
Disha 201747 2017 S Davangere Karnataka 800 8–9 ACM Low
Sharma 201548 2015 N Muradnagar Uttar Pradesh 1012 12–15 DAI Moderate
Aggarwal 201549 2015 N Ambala Haryana 1932 12–15 DAI Low
Atulkar 201750 2017 W Nagpur Maharashtra 1050 11–15 DAI Low
Prasad S N 201651 2016 S Bangalore Karnataka 450 10–12 DAI Moderate
NOHS -FM 2002–200312 2004 38,559 12,15 DAI Low
Hemapriya 201352 2013 S Kancheepuram Tamil Nadu 1000 12,15 DAI Low
Reddy 201153 2011 S Chennai Tamil Nadu 300 12–15 DAI Low
Athuluru 201654 2016 S Nellore Andhra Pradesh 200 12 DAI Low
Kaipa 201355 2013 S Nellore Andhra Pradesh 2126 13–15 DAI Low
Reddy 201056 2010 S Chennai Tamil Nadu 613 12 DAI Low
Thakur 201757 2017 S Udupi Karnataka 525 12 DAI Low
Handa 201658 2016 N Gurgaon Haryana 324 12,15 DAI Low
Mallick 201759 2017 S Davangere Karnataka 710 15 DAI Low
Gupta 201560 2015 C Bhopal Madhya Pradesh 549 13–15 DAI Low
Suma 201161 2011 S Nalgonda Andhra Pradesh 1794 15 DAI Low
Sultan and Ain 201862 2018 N Srinagar Kashmir 1600 12 ACM Low
Raina R 201763 2017 S Bangalore Karnataka 1111 13–15 DAI Low
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NOHS-FM: National Oral Health Survey and Fluoride Mapping, E – Eastern India, W – Western India,

S – Southern India, N – Northern India,ACM – Angle’s classification of malocclusion,G.Zone – Geographic zone.

Table 2.

Overall prevalence of malocclusion.

Characteristics Variables No. of studies Total sample (N) No. of participants with malocclusion (n) Pooled Prevalence 95% Confidence Interval
Lower Limit Upper Limit
Overall Total 54 97959 34674 35.40% 35.37% 35.43%
Age (in years) 12 24 29916 8869 29.65% 29.59% 29.70%
13 11 3366 1128 33.50% 33.34% 33.66%
14 11 3420 1132 33.10% 32.94% 33.26%
15 21 29132 7527 25.84% 25.79% 25.89%
Gender Male 33 40456 14645 36.20% 36.12% 36.28%
Female 33 36938 11813 31.98% 31.93% 32.03%
Population Urban 11 18313 6003 32.78% 32.71% 32.85%
Rural 14 36237 9447 26.07% 26.03% 26.12%
Regions North India 21 24683 8376 33.94% 33.88% 34.00%
South India 41 58645 23213 39.58% 39.54% 39.62%
West India 5 9949 2410 24.23% 24.14% 24.31%
Studies using Dental Aesthetic Index (DAI) Total 42 77797 20763 26.69% 26.66% 26.72%
Males 23 32746 8771 26.78% 26.74% 26.83%
Females 23 29942 6889 23.01% 22.96% 23.06%
Studies using Angle’s classification of malocclusion Total 11 19094 13495 70.68% 70.61% 70.74%
Males 8 6886 5603 81.37% 81.27% 81.46%
Females 8 6139 4708 76.69% 76.59% 76.80%
Risk of bias Low 47 94343 33404 35.41% 35.37% 35.44%
Moderate 7 3616 1270 35.12% 34.97% 35.28%
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Fig. 2.

Fig. 2

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Forest plot of included studies (n ​= ​54).

3.3. Synthesis of results

3.3.1. Pooled prevalence of malocclusion by proportion

The pooled prevalence of malocclusion was 35.40% (CI: 35.37–35.43, 54 studies, 97959 participants) (Table 2.). Males had a higher proportion of malocclusion (36.20%, CI: 36.12–36.28, 33 studies, 40456 participants) than females (31.98%, CI: 31.93–32.03, 33 studies, 36938 participants). Participants of 13 years age group had a comparatively higher prevalence of malocclusion (33.50%, CI: 33.34–33.66, 11 studies, 3366 participants) than other age groups. Prevalence of malocclusion was found to be higher among the urban population (32.78%, CI: 32.71–32.85, 11 studies, 18313 participants) when compared to rural population (26.07%, CI: 26.03–26.12, 14 studies, 36237 participants). South India showed a higher prevalence of malocclusion (39.58%, CI: 39.54–39.62, 41 studies, 58645 participants) than other regions of India.

3.3.2. Pooled prevalence of malocclusion by mean scores

The pooled estimate of prevalence of malocclusion as assessed by mean DAI score was 21.23 (CI: 21.14–21.33, 11 studies, 12345 participants) (Table 3.). When the mean DAI score was stratified by gender, the prevalence of malocclusion among males and females were 21.46 (CI: 21.36–21.57, 15 studies, 9547 participants) and 21.52 (CI: 21.41–21.64, 15 studies, 8276 participants) respectively. Based on mean DAI score, 15 years old children showed higher prevalence of malocclusion i.e. 23.22 (CI: 23.06–23.39, 12 studies, 4306 participants). Urban population had a comparatively higher mean DAI score (22.74, CI: 22.51–22.97, 4 studies, 2480 participants) than rural population (21.76, CI: 21.59–21.92, 4 studies, 4508 participants).

Table 3.

Prevalence of malocclusion based on DAI score.

Characteristics Variables No. of studies Total sample (N) Pooled prevalence 95% Confidence Interval
Lower Limit Upper Limit
Overall Total 11 12345 21.23 21.14 21.33
Gender Male 15 9547 21.46 21.36 21.57
Female 15 8276 21.52 21.41 21.64
Population Urban 4 2480 22.74 22.51 22.97
Rural 4 4508 21.76 21.59 21.92
Age (in years) 12 11 4038 22.88 22.71 23.05
13 9 2742 22.37 22.17 22.58
14 9 2791 22.44 22.23 22.64
15 12 4306 23.22 23.06 23.39
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4. Discussion

Malocclusion is a developmental anomaly that could be corrected if identified and treated at an early stage. Estimation of the prevalence of malocclusion, at the national level, should therefore replace the siloed approach of region wise and state wise prevalence estimation of malocclusion. However, the National Oral Health Survey of 2002 is the only national survey undertaken in India for estimating the prevalence of malocclusion. Though malocclusion is a morphological variation rather than a disease, it has significant negative impact on an individual’s life. Thus, it is important to identify malocclusion at an early age to improve the individual’s quality of life.

The American Academy of Orthodontists (AAO) recommend that children should have an early examination by 7 years of age for early diagnosis and treatment planning.6 According to Gray and Demirjian, assessments made in the permanent dentition are most reliable as they are devoid of issues associated with dental development.7

Large number of point prevalence studies estimating the prevalence of malocclusion among children in India are available in literature. A systematic review of these studies is essential for estimating the burden of malocclusion among Indian children. Additionally, the large number of criterias and indices available for the assessment of prevalence of malocclusion reduces the uniformity in estimation. Thus, this systematic review was undertaken to estimate the prevalence of malocclusion among 8-15yearsold Indian children from studies using DAI and Angle’s classification of malocclusion by meta -analysis technique.

Results of our review show that the pooled prevalence of malocclusion among 8–15 years children in India is 35.40% (CI: 35.37–35.43, 54 studies, 97959 participants) (Table 2.). Since our review assessed the prevalence of malocclusion from studies using DAI and Angle’s classification of malocclusion, the estimates of malocclusion by either of these measurement criteria were different from when the indices were pooled together. This is suggestive of variations in interval estimates.

Systematic review by Lombardo et al. to estimate the global prevalence of malocclusion among different stages of dentition found that the prevalence of malocclusion among children and adolescents is estimated to be 56% (CI: 11.0–99.0) without significant gender wise difference. Across the continents, Africa had a higher prevalence of malocclusion of 81% (CI: 64.0–98.0),followed by Europe with 71% (CI: 62.0–82.0), America with 53% (CI: 47.0–59.0) and Asia with 48% (CI: 34.0–62.0).8

Another systematic review using Angle’s classification of malocclusion among Iranian children under the age of 18 years showed a prevalence of malocclusion of 83.1% (25 studies, 28,693 participants).9 Besides the differences in age groups and index used for malocclusion assessment, underlying influence of genetic and environmental factors is reflected on comparison of our results with these global patterns.

Recent systematic review on prevalence of malocclusion among Indian children by Mehta et al. showed prevalence of malocclusion ranging from 28.4% (CI: 25.02–31.0, 34 studies, 71409 participants) for DAI scores to 66.7% (CI: 50.7–81.06, 8 studies, 10,663 participants) for Angle’s classification of malocclusion.10Our review yielded the prevalence of malocclusion based on DAI assessment as 26.69%(CI: 26.66–26.72, 42 studies, 77797 participants) and 70.68% (CI: 70.61–70.74, 11studies, 19094 participants) for studies using Angle’s classification of malocclusion (Table 2.).However, the age groups considered in the review by Mehta et al. and present study were different.

The increase in the prevalence of malocclusion by 13–14 years followed by its decline in 15 years (Table 2.) can be understood by Knutson’s explanation that these are age related correction of temporary malocclusion when the child outgrows deforming habits.3

Urban population showed a higher prevalence of malocclusion compared to rural population (Table 2.). Studies report that consistency of diet plays a significant role in jaw, oral musculature and teeth development thereby highlighting the soft and more refined urban diet to be the reason for higher prevalence of malocclusion in urban population than rural population.11

Males showed a higher prevalence of malocclusion (36.20%, CI: 36.12–36.28, 33 studies, 40456 participants). This estimate is similar to that given by the National Oral Health Survey 2002 where males showed a higher prevalence of malocclusion 26.78% (CI: 26.74–26.83, 19930 participants) than females (23%, CI: 23.06–23.96, 18629 participants). However,only 12 and 15 years participants of the National Oral Health Survey were eligible for comparison with our systematic review.12 Similarly, systematic review of prevalence of malocclusion among Indian children and adolescents showed a higher prevalence of malocclusion among males than females.10 Higher susceptibility of malocclusion among 8–15 years males can be attributed to their delayed growth spurts.13 Moreover, due to aesthetic reasons females have more inclination towards undergoing orthodontic treatment and our systematic review excluded those participants who are undergoing or had undergone orthodontic corrections.3

Region wise stratification of included studies and subsequent analysis showed that there is geographical variation in the prevalence of malocclusion with higher proportion of malocclusion in South India (39.58%, CI: 39.54–39.62, 41 studies, 58645 participants). This finding may be due to the ethnic affinity for malocclusion as exhibited by South Indian population.1

Till date, a continuous surveillance of oral disease in India has yet to be achieved, thus pooling data from prevalence studies should be prioritized for estimation of the nation’s oral disease burden. Since the systematic review showed a high prevalence of malocclusion among Indian children, it is imperative to plan and implement school based oral health promotion programs. Data from this review can be utilized by public health professionals and oral health policy makers to draft measures for the prevention and early identification of malocclusion among Indian children.

4.1. Strengths and limitations

We have included more studies for metanalysis than previous reviews in India. The pooled prevalence was grouped according to the measure of malocclusion assessment. We had assessed the pooled estimation of prevalence by proportion and means. DAI is a good predictor of future orthodontic treatment and its validity in screening of malocclusion has been recognised by the US Indian Health Service and Australian Dental Service.14 Representativeness of our estimates can be justified by two reasons – firstly, the studies including the National Oral Health Survey 2002 has covered extensive geographical regions of India (16 states and 4 union territories) and secondly, the large number of participants included in the systematic review highlights the precise estimation of prevalence of malocclusion among 8–15 years old Indian children.

There may be underestimation of malocclusion by those studies that used DAI since it does not measure buccal crossbite, posterior open bite and deep overbite. This makes DAI an incomplete measurement of malocclusion7 thereby weakening the estimation of malocclusion. DAI links aesthetic perceptions to anatomical traits resulting in a single score.11 Bimaxillary protrusion which is common in India is not measured by DAI and Angle’s classification of malocclusion.15 Presence of high amount of heterogeneity was explored by subgroup analysis. The presence of heterogeneity can be attributed to the methodological assessment of malocclusion, clinical variation (the population) and to some extent unexplained. Metaanalysis was performed using the random effect model.

5. Conclusion

Facial aesthetics has a profound implication on the psychological and social well-being of an individual. Malocclusion is a harbinger of poor self-esteem, dental conditions and disturbed orofacial functions. Thus, priority should be given for early detection and orthodontic treatment in children to safeguard them from aggravation of the condition and future oral care expenses. Our review estimated the prevalence of malocclusion among 8–15 years old Indian children to be 35.40% (CI: 35.37–35.43, 54 studies, 97959 participants). Presence of epidemiological data helps in planning of orthodontic treatment and evaluation of oral care services.

Ethics statement

The authors do not have any financial or other competing interests to declare.

Author contribution

P Balachandran contributed to the data acquisition, data interpretation and analysis and drafted the manuscript. C Janakiram contributed to the design of the review, data interpretation and analysis and drafted the manuscript.

Acknowledgement

The authors wish to thank Dr. Ramanarayanan Venkitachalam (Department of Public Health Dentistry, Amrita School of Dentistry, Kerala) for his valuable inputs in the preparation of this article.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jobcr.2021.01.011.

Contributor Information

Parvathy Balachandran, Email: parvathysree7@gmail.com.

Chandrashekar Janakiram, Email: sekarcandra@gmail.com.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Multimedia component 1
mmc1.docx (24.6KB, docx)

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