Prevalence of overweight and obesity among children andadolescents of the Indian subcontinent: a meta-analysis

Mohammad E Hoque, Suhail AR Doi, Munim Mannan, Kurt Long, Louis W Niessen, andAbdullah A Mamun

In order to examine the prevalence of overweight and obesity in childhood withinthe Indian subcontinent, a meta-analysis of studies was conducted. Within thedata sets analyzed, six homogeneous statistical subgroups were observed andthree levels of prevalence were discernible (low, intermediate, and high). Thepooled estimates of the prevalence of overweight and obesity in children were2% (95% confidence interval [CI], 2–3%) to 6% (95% CI, 6–7%) for the low-prevalence group, 11% (95% CI, 11–12%) to 18% (95% CI, 17–18%) for theintermediate-prevalence group, and 23% (95% CI, 22–24%) to 36% (95% CI,34–37%) for the high-prevalence group. Data on subjects in the low-prevalencegroup were obtained from national-level data and from data sets in which urbanand rural subjects were combined. Neither the intermediate- nor the high-prevalence category contained any data from the rural or national level. The inter-mediate group largely included urban children, whereas the high-prevalence groupgenerally included affluent children within major urban centers. Most of the datasets reported the prevalence of overweight and obesity among children in the10−18-year age range. The prevalence of overweight and obesity was higheramong boys than girls, and had increased among urban dwellers during the lastdecade.© 2014 International Life Sciences Institute

INTRODUCTION

The problem of overweight and obesity in childhood andadolescence is a global phenomenon and has beenincreasing in the developing world.1,2 For the purpose ofthis review, the word “overweight” is used to refer to bothconditions. In childhood, the condition of overweight is aserious public health problem that tracks into adulthoodwith a higher risk of morbidity and mortality.3,4 The mor-bidities associated with overweight include an increasedrisk of heart disease as well as other chronic diseases inadult life, such as type 2 diabetes mellitus, atherosclerosis,hypertension, dyslipidemia, and metabolic syndrome,

which are all becoming common among children andadolescents.1,2,5

In the Indian subcontinent, especially India, Bangla-desh, and Pakistan, malnutrition leading to underweighthas been the major public health concern for decades,with little or no attention being paid to overweight untilrecently. However, several studies have now reported anincreasing prevalence of overweight in this region.6–18

Among these studies, some reported the prevalence ofoverweight to be as high as 35−40%, which is close to thenational estimates of overweight in many industrializedcountries, including the United States4,5,19 and Australia.20

While underweight in childhood is still a major public

Affiliations: ME Hoque, SAR Doi, M Mannan, K Long, and AA Mamun are with the School of Population Health, University of Queensland,Brisbane, Queensland, Australia. LW Niessen is with the Center for Global Health, Johns Hopkins School of Public Health, Baltimore,Maryland, USA.

Correspondence: ME Hoque, School of Population Health, University of Queensland, Brisbane, QLD 4006, Australia. E-mail:mohammad.hoque@uq.net.au. Phone: +61-4-50803626. Fax: +61-7-33655599.

Key words: adolescent, children, Indian subcontinent, obesity, overweight

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Special Article

doi:10.1111/nure.12130Nutrition Reviews® Vol. 72(8):541–550 541

health problem in the Indian subcontinent, the increasingprevalence of overweight poses an additional threat topublic health.

Many of the studies performed to date have mea-sured the country-specific prevalence of overweight.However, it is not clear whether the policy makers inthese countries can utilize the information appropriatelygiven the gross heterogeneity across studies even withinthe same country. In addition, the studies reporting over-weight within the Indian subcontinent are mainly local-ized to particular regions or groups of people withincountries and, thus, are not nationally representative. Noattempt has been made to measure the overall prevalenceof overweight in the Indian subcontinent, though thesocioeconomic conditions and the historical and culturalbackgrounds of the countries are similar. In addition, themethods used to measure the prevalence of overweighthave beene heterogeneous and applied in an ad hocmanner. The aim of this review was, consequently, tomake sense of this heterogeneity and come up with esti-mates of the overall prevalence of overweight within theIndian subcontinent. This is particularly importantbecause an accurate estimate of the prevalence of over-weight is needed for health policy planning and publichealth interventions in this part of the world. Previousmeta-analyses of the prevalence of overweight that havebeen carried out in various settings have not addressedheterogeneity properly or have focused on trends. Forexample, Wang et al.21 reported a shift from under- toovernutrition in India, while Midha et al.22 recentlyreported a meta-analysis of the prevalence of childhoodobesity in India. However, the study of Midha et al.22 wasnot comprehensive in the sense that many publishedarticles were not included, the heterogeneity among thestudies was not considered, and the quality of theincluded studies and the standardization of the popula-tion groups (urban/rural) was not accounted for in theestimation of prevalence. To date, no meta-analysisappears to have been conducted to examine the combinedprevalence and location of the overweight and obese chil-dren and adolescents within the Indian subcontinent. Thepresent review aims to bridge this gap by addressing het-erogeneity and by estimating the pooled prevalence ofoverweight within different homogeneous groups.

METHODS

Study selection criteria

Studies performed in countries of the Indian subconti-nent that defined overweight categories according tobody mass index (BMI) were eligible for inclusion. Pres-ence of overweight in the individual studies was defined

according to cutoff points from various sources, includ-ing the child growth standards of the World Health Orga-nization, the growth charts of the Centers for DiseasesControl and Prevention (CDC), the International ObesityTask Force (IOTF) standard, and the modified BMI cri-terion for Asian populations.As the objective of this studywas to review the prevalence of overweight, studies thatused any of the above-stated criteria, considering themconceptual rather than exact replications, were included.Studies eligible for inclusion also met the following crite-ria: used primary or secondary data, were published inEnglish language, were conducted in humans, and pro-vided prevalence of overweight defined as including bothchild and adolescent populations. Editorial and method-ological articles or studies with no reported prevalencemeasures were excluded. No restrictions were placed onthe type of residential location of the subjects; thus, datawas collected from urban (defined for this review as met-ropolitan cities), semi-urban (defined as peri-urban areas,based on geographical locations identified by theauthors), rural (defined as data collected from rural set-tings), and rural/urban areas (combined data from bothrural and urban settings reported in a way that could notbe separated). Data also came from affluent areas, withparticipants reported in the respective studies as affluentstudents or students from affluent schools and fromhigher socioeconomic strata, e.g., selected from private,English-medium schools. In addition, some of the datacame from national surveys, i.e., country-wide surveyscovering both rural and urban settings.

Search strategy

A systematic computerized literature search was con-ducted using Medline via the PubMed interface, as well asthe CINHAL and EMBASE databases over the period1961 to June 2013. The key words used in the search were“child,” “adolescent,” “overweight,” “obesity,” “body massindex,” “Indian subcontinent,” as well as the followingnames of individual countries: Bangladesh, India, andPakistan. The literature search was carried out during2013 with the combination of key words specified above,as well as MeSH terms and other free-text terms suitablefor the purpose. A snowball method, characterized bymanual checking of references from retrieved articles,was applied in order to ensure complete collection. Addi-tional studies identified in the course of reading were alsoretrieved for consideration.

Literature search, data extraction, andquality appraisal

Two authors (MEH and MM) individually screened theabstracts of the 2,406 articles originally identified (after

Nutrition Reviews® Vol. 72(8):541–550542

duplicates were removed) and excluded 2,274 on the basisof irrelevance. The remaining 132 full-text papers werethen screened for the presence of relevant informationand any disagreement was resolved by consensus. A totalof 43 full-text articles were deemed relevant andincluded.6–18,23–56 In the meta-analysis, these 43 studiesaccounted for 73 data sets (i.e., number of estimates)(Figure 1).

A standardized data extraction form was used tocollect information from each article and summarize thestudy samples and settings (e.g., sample size, country, geo-graphic location, demographic and socioeconomics char-acteristics), survey year, and relevant raw data (Table S1,available online in the Supporting Information). Whenthe original studies did not report the survey year, thepublication year was listed.

A quality scoring instrument (Table S2, availableonline in the Supporting Information) was applied based

on standard bias criteria in observational studies57 con-sidering factors that might result from design-specificbias, selection bias, potential confounding, informationbias, and analytical biases. A quality score was assignedthat was rescaled between 0 and 1 (inclusive) by dividingthe maximum possible score based on the set criteria.

Statistical analysis

Among the data sets evaluated, six statistically homoge-neous subgroups could be observed; these were sub-jectively labeled as subgroups A, B, C, D, E, and Fin decreasing order of prevalence using the clustersobserved within the forest plot (Figure 2). However,three groups of prevalence were discernible for thesesubgroups: low (subgroups E + F; 20 datasets), interme-diate (subgroups C + D; 22 datasets), and high (sub-groups A + B; 31 datasets). These three categories were

Studies included for further evalua�on

(n = 65)

Records iden�fied through database searching

(n = 3148)

PubMed = 1142

Embase = 1911

CINAHL = 95Sc

reen

ing

Incl

uded

Studies included in quan�ta�ve synthesis

(meta-analysis): n = 43

Total data sets (n = 73)

(57 from India, 10 from Pakistan, and

6 from Bangladesh)

Elig

ibili

tyId

en�fi

ca�o

n Addi�onal records iden�fied through other sources

(n = 2)

Records a�er duplicates removed (n = 2406)

Abstracts screened (n = 132)

Records excluded for unreported and absence of required informa�on

(n=67)

Records excluded for improper topic (n=2274)

Figure 1 Flow diagram of the literature search and selection process.

Nutrition Reviews® Vol. 72(8):541–550 543

INDSCprev by INDSC_SG

Prev0.40.20

Study or Subgroup

BBS (2007)56 (B/U5/M) BBS (2007)56 (B/U5/F)

F subgroup

Marwaha R K, (2006)40 (I/SU/O5/F) Jafar TH et al (1), (2008)23 (P/U/O5/M)

Marwaha R K, (2006)40 (I/SU/O5/M) Jafar TH et al (1), (2008)23 (P/U/O5/F)

Bishwalata R et al, (2010)38 (I/U/O5/M) Jafar TH et al (2), (2008)23 (P/U/O5/M) Bishwalata R et al, (2010)38 (I/U/O5/F)

Keerthan KM et al , (2011)52 (I/UR/O5/F) Raj M (1), (2007)35 (1) (I/U/O5/F)

Keerthan KM et al , (2011)52 (I/UR/O5/M) Laxmaiah A et al, (2007)34 (I/U/O5/M)

Mahajan P B et al, (2011)48 (I/UR/O5/M)

E subgroup

Jafar TH et al (2), (2008)23(P/U/O5/F) Mahajan P B et al, (2011)48 (I/UR/O5/F)

Raj M(1), (2007)35 (1) (I/U/O5/M) Raj M (1), (2007)35 (2) (I/U/O5/F)

Jeemon P et al, (2009)7 (I/U/O5/F) Laxmaiah A et al, (2007)34 (I/U/O5/F)

Raj M (1), (2007)35 (2) (I/U/O5/M) Jeemon P et al, (2009)7 (I/U/O5/M) Goyal RK et al, (2010)45 (I/U/O5/F)

Mehta M, (2007)33 (I/U/O5/F) Premenath M et al, (2009)39 (I/U/O5/F)

D subgroup

Premenath M et al, (2009)39 (I/U/O5/M)

Cherian, (2012)50 (I/U/O5/M)

A

Q=32.77, p=0.00, I2=66%

B

Q=105.65, p=0.00, I2=83%

C

Q=48.70, p=0.00, I2=69%

D

Q=58.61, p=0.00, I2=91%

E

Q=64.41, p=0.00, I2=80%

F

Q=31.91, p=0.00, I2=84%

Overall Q=11976.77, p=0.00, I2=99%

Thakre SB et al, (2011)51 (I/U/O5/M) Thakre SB et al, (2011)51 (I/U/O5/F) Kotian MS et al, (2010)42 (I/U/O5/M) Kotian M S et al, (2010)42 (I/U/O5/F)

Subramanyam V et al (1), (2002)30 (I/UA/O5/F) Subramanyam V et al (2), (2002)30 (I/UA/O5/F)

Ramesh K, (2010)47 (I/U/O5/M) Goyal RK et al, (2010)45 (I/U/O5/M)

Cherian, (2012)50 (I/U/O5/F)

C subgroup

Sood A et al, (2007)43 (I/UA/O5/F) Sidhu S, (2006)55 (I/U/O5/M)

Misra A, (2011)6 (1) (I/U/O5/M) Khadilkar VV, (2011)8 (I/UA/F)

Ramachandran A, (2002)31 (I/U/O5/F) Misra A, (2011)6 (1) (I/U/O5/F)

Goyal JP et al, (2011)49 (I/U/O5/F) Ramesh K, (2010)47 (I/U/O5/F) Uma Iyer, (2011)36 (I/U/O5/F) Uma Iyer, (2011)36 (I/U/O5/M) Sidhu S, (2006)55 (I/U/O5/F)

Khadilkar VV, (2011)8 (I/UA/M) Ramachandran A, (2002)31 (I/U/O5/M)

Goyal JP et al, (2011)49 (I/U/O5/M) Marwaha RK, (2006)40 (I/UA/O5/M)

Mushtaq MU et al, (2011)10 (P/U/O5/F)

B subgroup

Bose K, (2007)32 (I/UA/O5/F) Hasmi A et al, (2013)13 (P/U/O5/F) Marwaha R K, (2006)40 (I/UA/O5/F) Sultan K et al, (2008)11 (B/UA/U5/F)

Jain S et al, (2010)16 (I/UA/O5/F) Patnaik S et al, (2011)18 (I/UA/O5/F) Sultan K et al, (2008)11 (B/UA/U5/M)

Mushtaq MU et al, (2011)10 (P/U/O5/M) Jain S et al, (2010)16 (I/UA/O5/M)

Kapil U et al15, 2002 (I/U/O5/M) Gupta DK et al (1), (2011)14 (I/U/O5/M)

Patnaik S et al, (2011)18 (I/UA/O5/M) Anwar A, (2010)12 (P/UA/O5/F)

Kapil U et al, (2002)15 (I/U/O5/F) Anwar A, (2010)12 (P/UA/O5/M)

A subgroup

Gupta DK et al (2), (2011)14 (I/U/O5/F) Gupta DK et al (1), (2011)14 (I/U/O5/F)

Gupta DK et al (2), (2011)14 (I/U/O5/M) Hasmi A et al, (2013)13 (P/U/O5/M)

Mohsin F et al, (2010)9 (B/UA/O5/M) Mohsin F et al, (2010)9 (B/UA/O5/F)

Prev (95% CI) % Weight

0.01 ( 0.01, 0.02) 1.150.02 ( 0.01, 0.02) 1.10

0.02 ( 0.01, 0.03) 7.96

0.02 ( 0.02, 0.03) 2.400.03 ( 0.02, 0.04) 0.790.03 ( 0.03, 0.04) 1.750.03 ( 0.02, 0.04) 0.77

0.05 ( 0.04, 0.06) 0.880.05 ( 0.03, 0.06) 0.730.05 ( 0.04, 0.06) 1.030.05 ( 0.03, 0.08) 0.340.05 ( 0.05, 0.06) 5.060.06 ( 0.03, 0.10) 0.320.06 ( 0.04, 0.08) 0.470.06 ( 0.05, 0.08) 0.67

0.06 ( 0.05, 0.07) 19.37

0.07 ( 0.05, 0.08) 0.720.07 ( 0.06, 0.08) 0.650.07 ( 0.07, 0.08) 4.300.07 ( 0.07, 0.08) 2.900.08 ( 0.07, 0.09) 0.820.08 ( 0.06, 0.10) 0.48

0.10 ( 0.09, 0.10) 2.710.10 ( 0.09, 0.11) 0.820.11 ( 0.10, 0.12) 1.090.11 ( 0.07, 0.16) 0.320.11 ( 0.11, 0.12) 7.17

0.11 ( 0.08, 0.15) 20.65

0.13 ( 0.12, 0.13) 8.53

0.13 ( 0.11, 0.16) 0.48

0.14 ( 0.11, 0.17) 100.00

0.14 ( 0.12, 0.17) 0.710.15 ( 0.12, 0.17) 0.680.15 ( 0.11, 0.18) 0.580.15 ( 0.12, 0.18) 0.570.16 ( 0.13, 0.18) 0.450.16 ( 0.13, 0.19) 0.420.16 ( 0.14, 0.19) 0.480.17 ( 0.16, 0.19) 1.340.17 ( 0.15, 0.20) 0.46

0.18 ( 0.17, 0.19) 27.39

0.18 ( 0.17, 0.19) 1.420.18 ( 0.15, 0.22) 0.440.18 ( 0.18, 0.19) 9.060.18 ( 0.18, 0.19) 3.130.18 ( 0.17, 0.20) 0.950.19 ( 0.18, 0.19) 6.20

0.20 ( 0.17, 0.23) 0.530.20 ( 0.17, 0.23) 0.490.20 ( 0.17, 0.24) 0.490.21 ( 0.17, 0.24) 0.520.21 ( 0.17, 0.24) 0.450.21 ( 0.20, 0.21) 3.840.21 ( 0.20, 0.23) 0.970.22 ( 0.18, 0.26) 0.410.22 ( 0.21, 0.23) 2.750.23 ( 0.20, 0.25) 0.74

0.23 ( 0.21, 0.25) 17.78

0.23 ( 0.19, 0.27) 0.290.24 ( 0.19, 0.30) 0.330.25 ( 0.24, 0.26) 2.840.25 ( 0.14, 0.37) 0.140.25 ( 0.22, 0.28) 0.710.26 ( 0.21, 0.31) 0.420.26 ( 0.18, 0.34) 0.150.26 ( 0.23, 0.29) 0.780.29 ( 0.27, 0.31) 0.93

0.31 ( 0.28, 0.35) 0.360.32 ( 0.30, 0.34) 1.250.33 ( 0.26, 0.39) 0.400.33 ( 0.26, 0.40) 0.000.33 ( 0.28, 0.39) 0.300.35 ( 0.27, 0.44) 0.00

0.36 ( 0.34, 0.38) 6.85

0.36 ( 0.34, 0.38) 1.090.37 ( 0.35, 0.40) 0.890.37 ( 0.36, 0.39) 1.570.38 ( 0.32, 0.44) 0.330.41 ( 0.35, 0.47) 0.340.42 ( 0.35, 0.49) 0.32

Figure 2 Forest plot of the data sets from the articles selected for meta-analysis.

Nutrition Reviews® Vol. 72(8):541–550544

subsequently created to facilitate clear reporting andinterpretation of the prevalence summaries. In additionto a meta-analysis of homogeneous clusters, meta-analyses of sex and age categories were also performed.For the analysis of age categories, the age of each studypopulation was calculated as the mean age of the studysample. The mean ages were then grouped into the fol-lowing three categories based on the following cutoffsdefined for childhood and adolescence by the WorldHealth Organization: <5 years, 5−9 years, and 10−18years.58

The statistical analysis was performed using thequality effects model,59,60 which was favored over therandom effects (RE) model to adjust for bias in observa-tional studies. This choice was guided by the authorsopinion that 1) the methodology in the RE model is grosslyflawed, to the extent that, even in standard meta-analyses,there is a lack of interpretation of an RE summary61; and 2)use of the RE model requires strong assumptions that areunlikely to be valid in practice.Regarding heterogeneity inprevalence, this was addressed by creating homogeneoussubgroups and discussing subgroup differences.

The primary outcome of interest was the proportionof overweight subjects within the respective prevalencegroups and the composition of overweight within thesegroups and how they differ. Heterogeneity in the pooledprevalence was, thus, explained by the latter evaluation.For subgroups with sufficient data sets (more than 10) thefunnel plots were examined. Publication bias was assessedby inspection of the funnel plots. All statistical analyseswere performed using MetaXL version 1.4.62

RESULTS

Study characteristics

The studies included in this review were predominantlyconducted in urban areas; however, several were also con-ducted in urban and rural areas.46,48,52,53,56 Most of thestudies reported obesity and overweight prevalence sepa-rately; however, a few reported combined prevalence7,23,25

and it was not possible to determine the prevalence ofeach group separately based on the data provided. Amixed picture was observed for gender, with some studiesreporting the prevalence of overweight for femalesonly30,32,33,43 while others reported the combined preva-lence for both males and females.17,25–27,29,37,44,46,54,63 Theage groups also varied across the studies. Finally, thedata were collected from several demographic and socio-economic settings, i.e., rural and urban combined,semi-urban, urban, and affluent, while some comparedthe prevalence of overweight between urban and ruralsettings.35,37,46

Study quality

While selecting the study samples, most of the researcherscarried out a census of the study population6–8,16,24,27,39.Most of the studies did not report data on participantrefusals during the baseline data collection. Fewstudies6,14,16,23,27,40,42,51,56 attempted to minimize the likeli-hood of non-response bias. All of the studies, excepttwo,32,50 used an acceptable case definition (i.e., over-weight defined as BMI ≥25 kg/m2) in their study. Thereliability and validity of the instruments used for heightand weight measurement were described in most of thereports6,14,16,23,24,27,39,40,56. Appropriate numerators anddenominators were used for the parameters of interest inthe data sets.

All of the data sets were from cross-sectional surveysand the sample sizes varied from 172 to 43,152. The 73valuated data sets represented studies containing 227,988total subjects, with India contributing 95% of this popu-lation, followed by Pakistan (3%) and Bangladesh (2%).The study years varied from 1981 to 2011. The data setsthemselves were also mainly from India, with 57 (78%) ofthe total data sets coming from India, 10 (14%) fromPakistan, and 6 (8%) from Bangladesh. Among the datasets, two were obtained using national-level data. Onlyfour (6%) data sets reported the prevalence of overweightfor children under 5 years of age, and the rest reported theprevalence of school-age children, though the age groupsvaried.A pooled analysis considering different age groups(Table 1) revealed that the total prevalence of overweightwas 2% (95% CI, 0–4%), 11% (95% CI, 8–14%), and 15%(95% CI, 13–17%) for children in the age groups less than5 years, 5−9 years, and 10−18 years, respectively.

Quantitative syntheses

A quantitative synthesis of the three groups of prevalenceis provided below. It is worth noting that in these threegroups there were no marked differences among the chil-dren from Bangladesh, India, and Pakistan.

Low prevalence of overweight (subgroups F and E). Thepooled estimate of overweight for subgroups F and Ewere 2% (95% CI, 2–3%) and 6% (95% CI, 6–7%), respec-tively (Table 1). Table 1 also shows that the pooled esti-mate of overweight for males and females in group F was2% (95% CI, 2–3%) and 2% (95% CI,2–3%), respectively;in group E, it was 6% (95% CI, 6–7%) for both males andfemales. The total population in this group constitutedjust over a quarter (27%) of the total data set and con-sisted of 53,650 (88%) participants from India, 3,647 (6%)from Pakistan, and 3,797 (6%) from Bangladesh. Thisgroup also included half of the Bangladeshi children andmost of the Pakistani children. Fifty-five percent of par-

Nutrition Reviews® Vol. 72(8):541–550 545

ticipants in the low-prevalence data set were evaluatedusing the growth reference charts of the CDC, followedby the child growth standards of the WHO (25%), theIOTF standard (10%), and the modified BMI criterion forAsian populations (10%). The male to female ratio was45:55. The median year of survey performance was 2005(range, 1992–2011). Only two (10%) data sets reportedthe prevalence of overweight for children younger than 5years. No data set reported in group E calculated theprevalence of overweight for children in the age groups<5 years and 10–18 years. The pooled estimates of over-weight in group F were 1% (95% CI, 1–2%), 3% (95% CI,2–4%), and 6% (95% CI, 6–7%) for the age groups <5years, 5–9 years, and 10–18 years, respectively. The pooledestimate of the prevalence of overweight in group E was6% (95% CI, 5–7%) for 5–9-year-old children. Most ofthese overweight subjects were urban-dwelling (60%),with data on the remainder stemming from rural, semi-urban, or peri-urban subjects or national-level data. Onlythis low-prevalence group included data obtained fromnational-level data collections and data sets in which theprevalence of urban and rural subjects was combined.There were also no participants from affluent areas in thisgroup. Thus, this group is characterized by national-level,rural-mix, school-age children who are generally unlikelyto be overweight and obese.

Intermediate prevalence of overweight (subgroups D andC). The pooled estimate of overweight for subgroups Dand C within this group were 11% (95% CI, 11–12%) and18% (95% CI, 17–18%), respectively (Table 1). Table 1 alsoshows the pooled estimates of overweight for males andfemales in subgroup D were 12% (95% CI, 10–13%) and11% (95% CI, 11–12%), respectively; in subgroup C, theywere 17% (95% CI, 16–19%) and 18% (95% CI, 17–18%),respectively. The total population of this group com-prised just over one-quarter of the total data sets (30%)with 120,902 subjects, and all of the data sets were fromIndia. Fifty percent of the data sets in this group used theCDC growth reference charts, 45% used the IOTF stan-dard, and 5% used the child growth standards of theWHO. None of the data sets in this group included theprevalence of overweight for children younger than 5years of age. In subgroup D, none of the data sets includedthe prevalence of overweight for children aged 5–9 years,whereas only one data set in subgroup C included theprevalence of overweight for this age group. The pooledestimate for the prevalence of overweight in subgroup Cwas 18% (95% CI, 15–22%) for 5–9-year-old children and18% (95% CI, 17–18%) for 10–18-year-old children. Thepooled estimate of the prevalence of overweight in sub-group D was 12% (95% CI, 11–12%) for 10–18-year-oldchildren.All subjects in this group came from urban Indiaand all were school-going children. Within this group, aTa

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Nutrition Reviews® Vol. 72(8):541–550546

majority (82%) of children were urban poor and a minor-ity (18%) were from affluent segments of society. Themale to female ratio was 45:55. The median year in whichthe surveys were conducted was 2007 (range, 1981–2010).This group did not contain any rural, semi-urban, ornational-level data; thus, only urban school-going chil-dren in India are represented.

High prevalence of overweight (subgroups B and A). Thepooled estimate of overweight for subgroups B and Awithin this high-prevalence group were 23% (95% CI,22–24%) and 36% (95% CI, 34–37%), respectively(Table 1). Table 1 also shows the pooled estimate of over-weight for males and females in subgroup B were 22%(95% CI, 21–24%) and 23% (95% CI, 22–25%), respec-tively; in subgroup A they were 35% (95% CI, 33–37%)and 37% (95% CI, 35–38%), respectively. The total popu-lation in this group formed just under half (43%) of thetotal data set with 42,698 (93%), 2,654 (5.5%), and 640(1.5%) subjects from India, Pakistan, and Bangladesh,respectively. The male to female ratio was 48:52. Themedian study year was 2008 (range, 2002–2011). Fifty-five percent of the data sets in this group used the CDCgrowth reference charts, 25% used the child growth stan-dards of the WHO, and 20% used the IOTF standard.Most (75%) of the data sets reported the prevalence ofoverweight for 10–18-year-old children. No data set insubgroup A reported the prevalence of overweight for5–9-year-old children. The pooled estimates of preva-lence of overweight for the different age groups in sub-group B were 26% (95% CI, 19–33%), 23% (95% CI,21–24%), and 17% (95% CI, 13–21%) for <5 years, 5–9years, and 10–18 years, respectively. In subgroup A, thepooled estimates of prevalence of overweight were 10%(95% CI, 9–10%) and 36% (95% CI, 34–37%) for the <5year and 10–18 year age groups, respectively. About half(45%) of the children were from recognized affluentpopulations. There were no subjects from rural, semi-urban, or national areas. The main characteristics of chil-dren in this group are an association with affluence andresidence in major urban centers.

Publication bias

The funnel plots for the intermediate- and high-prevalence groups (available in Figure S1C–F in the Sup-porting Information online) were asymmetrical, withsubgroup B (pooled prevalence, 23%) demonstrating apreponderance of smaller studies of higher prevalenceand subgroup C (pooled prevalence, 18%) demonstratinga preponderance of smaller studies of lower prevalence.This reflects the heterogeneity induced by larger andsmaller studies in these subgroups, reflecting differentsocietal segments in terms of affluence.

DISCUSSION

This is the first comprehensive study to systematicallyevaluate the scientific literature and include a meta-analysis of the prevalence of overweight in the Indiansubcontinent. The estimate of overweight in this region asa whole was pooled by prevalence group and was 2–6% inrural children, 16–18% in semi-urban or non-affluenturban children, and 23–36% in affluent urban children.These findings differ from the findings of Midha et al.,22

which estimated the prevalence of childhood overweightin India to be 12.64% (95% CI, 0.08–0.16) and obesity as3.39% (95% CI, 0.02–0.04). However, unlike in thepresent analysis, Midha et al.22 reported an estimate thatdid not account for the heterogeneity in populations andmostly reflected the urban poor. Thus, their results arecomparable to those reported above for the intermediategroup.

The findings of the present meta-analysis indicatethere is large variation in the prevalence of overweightbetween children in rural versus urban settings, which isconsistent with other findings.21,64 They also indicate thatthe prevalence of overweight is low in rural and semi-urban areas. On the other hand, the prevalence is muchhigher among the children and adolescents of affluent,mainly urban-dwelling, parents. This high prevalenceamong the affluent is comparable to the situation indeveloped nations65; however, the overall prevalence ofoverweight among urban children and adolescents islower than would be expected. This may be due to theexistence of slums in urban areas, where the health statusof children and adolescents is very poor; thus, the urbanpoor and urban affluent seem to have very differentobesity trajectories. When this is considered, it is obviousthat by combining urban slum-dwelling children withother urban children, the total pooled prevalence of over-weight among urban children and adolescents is reducedsubstantially.

The findings of this meta-analysis are consistent withthe findings of other studies showing that boys have ahigher prevalence of overweight than girls.7,13,35,49 Theanalysis also demonstrates that, as age increases, theprevalence of overweight increases (Table 1). However,very few studies to date have investigated the prevalenceof overweight for children below 5 years of age, so thepresent results consequently address the prevalence ofoverweight primarily for older children.

The high prevalence of overweight among urban chil-dren and adolescents in countries of the Indian subconti-nent may be attributable to economic development andincreasing urbanization. During the last decade, urbaniza-tion has risen very fast in this region, with urban popula-tion growth rates in India, Pakistan, and Bangladesh beingreported as 2.47%, 2.68%, and 2.96%, respectively.66

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Urbanization is associated with increased sedentarybehavior and lack of physical activity34,42,45 due to lack ofplay areas,both in the community and in schools,as well asincreasing time spent watching television and playingcomputer games.37,42,49,53 Additionally, changes in dietaryhabits have occurred, with families and individualsincreasingly eating outside the home, often purchasingtake-away or fast foods.45,49 The globalization of the foodmarket and increased consumption of junk food, dueto more widespread availability of fast-food shops, havealso contributed to dietary changes.45,49,53

The results of the present meta-analysis of the preva-lence data also suggest a rising trend of overweight withinthe Indian subcontinent (see Figure S2 in the SupportingInformation online). Individual studies of this trend alsofound that overweight is increasing over time.14,23,30,35 Ifthis growth persists, the increasing incidence of over-weight will be seriously disadvantageous to healthcareresources in these countries. In fact, it will cause a signifi-cant increase in the economic costs of overweight-relatedillness.

Although several studies on overweight prevalencehave been conducted in the Indian subcontinent, most ofthese studies were solely conducted in India. In Bangla-desh, in particular, little data exist on the prevalence ofoverweight among children.9,11,56 Furthermore, previousstudies have not separated out national-level informationinto rural, urban, semi-urban, affluent, and slum settings,which is critical for understanding the implications of thedata for policy planning. Meta-analyses should also notpool heterogeneous prevalence data, as done previously,22

since there is not expected to be a common underlyingprevalence of overweight in such diverse populations. Infuture reports, when such heterogeneity is present, theapproach used here or, alternatively, direct standardiza-tion for population heterogeneity needs to be undertaken.

There is very limited research on the burden of over-weight among children below 5 years of age in the Indiansubcontinent. Again, even among the data sets that tar-geted children older than 5 years, researchers arbitrarilyincluded different age groups between 5 and 18 years,thus creating problems for pooling prevalence. This wasovercome in the present analysis by using a stratifiedapproach. Specific age-stratified results are needed forfuture studies on overweight, and the following groupingsare recommended: children <5 years, pre-adolescentsbetween the ages of 5 and 9 years, and adolescents aged10–18 years. The same is true for selecting overweightcutoffs. For instance, the CDC growth reference chart wasused for 53% of the total data sets,8,9,14,16,18,23,30,34–36,43,47–51,55

the IOTF standard was used for 25%,6,7,15,31–33,39,40,42,45 theWHO criteria was used for 19%,7,10–13,38,56 and the modi-fied BMI criterion for Asian populations was used for theremaining 3%.52 Such variability makes it difficult for

policy makers to make any specific policy decision basedon the information. As a consequence, researchers shouldreach a consensus and use one specific guideline that issuitable for countries of the Indian subcontinent.

This review and meta-analysis has several potentiallimitations. A specific limitation of the meta-analysis isthe combination or comparison of studies using differentcriteria to define overweight. In addition, some of thestudies were not originally designed or intended to reportthe prevalence of overweight. Moreover, since reportsand articles based on the local languages of the subcon-tinent are not often available electronically, some datamay have been missed. For this review, studies conductedunder different circumstances in different parts of theIndian subcontinent were pooled together, although thiswas accounted for through effect size subgrouping. Inaddition, regional differences among the countries existand, again, the pooled estimates reported in this paper arederived from cross-sectional studies and cover a longperiod of time. Performing a meta-analysis of suchstudies, however, results in gross heterogeneity. As aresult, the funnel plots of the subgroups also show asym-metry in some graphs. In addition, since most of the sub-jects in the included studies were from India, the findingsmay not be generalizable to the entire subcontinent.However, the inhabitants of this subcontinent arebelieved to be similar and the additional data from Ban-gladesh and Pakistan can serve to augment the data fromIndian populations.

CONCLUSION

This study demonstrates that the main characteristics ofoverweight and obese children in the Indian subconti-nent are their connection with affluence and residencewithin major urban centers, while the prevalence of over-weight in rural areas is still low. These findings are con-sistent with the reported trend of increasing prevalence ofoverweight among urban children in this region duringthe last decade.14,23,35 The present review also reveals thatresearch performed to date has largely been targeted at10−18-year-old children. Therefore, national, population-based surveys that investigate the prevalence of over-weight in countries of the Indian subcontinent need toadequately stratify survey respondents. The results of thepresent review suggest such stratification should be basedon sex, socioeconomic characteristics (including bothslum and non-slum residences), and geographical loca-tion (such as rural and urban). Widespread nationalcampaigns to address the epidemic of overweight arealso recommended, particularly those targeted to urbanareas. This should help countries in the Indian subconti-nent to more efficiently reduce future public health and

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economic burdens and may also help prevent the spreadof overweight and obesity from rich to middle-incomeand poor communities.

Acknowledgment

Justin Clark is gratefully acknowledged for his assistancein collecting references.

Author contributions. MEH was involved in the conceptand design of the manuscript, literature review, dataextraction and analysis, interpretation of the data, andpreparing the draft manuscript. SD contributed to theconcept and design, methodological development of themeta-analysis, data interpretation, and critical review ofthe draft manuscript. MM participated in the literaturereview, data extraction, and manuscript editing. KL andLN contributed to the concept and design and criticallyreviewed the drafts. AM was involved in the concept anddesign of the manuscript, methodological developmentof the meta-analysis, data analysis and interpretation,critical review of the manuscript, and overall guidance ofthe manuscript.

Funding. There were no external funding sources forthis study.

Declaration of interest. The authors have no relevantinterests to declare.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in theonline version of this article at the publisher’s web-site:

Figure S1 Funnel plots characterizing the assymetry ofthe five subgroups of data sets. a: Funnel plot ofsubgroup A. b: Funnel plot of subgroup B. c: Funnel plotof subgroup C. d: Funnel plot of subgroup D. e: Funnelplot of subgroup E. f: Funnel plot of subgroup F.Figure S2 Median (whiskers depict the range) of theyears in which data sets were collected for the subgroupsreported in Table 1. Subgroup A represents the categorywith the highest prevalence of overweight while F repre-sents the subgroup with the lowest prevalence.Table S1 General characteristics of the studiesreviewed.Table S2 Quality score table for the meta-analysis.

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