Calvo-Muñoz et al. BMC Pediatrics 2013, 13:14
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RESEARCH ARTICLE

Open Access

Prevalence of low back pain in children andadolescents: a meta-analysis
Inmaculada Calvo-Muñoz1*, Antonia Gómez-Conesa2 and Julio Sánchez-Meca3

Abstract
Background: Low back pain (LBP) is common in children and adolescents, and it is becoming a public healthconcern. In recent years there has been a considerable increase in research studies that examine the prevalence of
LBP in this population, but studies exhibit great variability in the prevalence rates reported. The purpose of thisresearch was to examine, by means of a meta-analytic investigation, the prevalence rates of LBP in children andadolescents.
Methods: Studies were located from computerized databases (ISI Web of Knowledge, MedLine, PEDro, IME, LILACS,
and CINAHL) and other sources. The search period extended to April 2011. To be included in the meta-analysis,
studies had to report a prevalence rate (whether point, period or lifetime prevalence) of LBP in children and/or
adolescents (≤ 18 years old). Two independent researchers coded the moderator variables of the studies, andextracted the prevalence rates. Separate meta-analyses were carried out for the different types of prevalence inorder to avoid dependence problems. In each meta-analysis, a random-effects model was assumed to carry out thestatistical analyses.
Results: A total of 59 articles fulfilled the selection criteria. The mean point prevalence obtained from 10 studies
was 0.120 (95% CI: 0.09 and 0.159). The mean period prevalence at 12 months obtained from 13 studies was 0.336(95% CI: 0.269 and 0.410), whereas the mean period prevalence at one week obtained from six studies was 0.177(95% CI: 0.124 and 0.247). The mean lifetime prevalence obtained from 30 studies was 0.399 (95% CI: 0.342 and0.459). Lifetime prevalence exhibited a positive, statistically significant relationship with the mean age of theparticipants in the samples and with the publication year of the studies.
Conclusions: The most recent studies showed higher prevalence rates than the oldest ones, and studies with abetter methodology exhibited higher lifetime prevalence rates than studies that were methodologically poor.
Future studies should report more information regarding the definition of LBP and there is a need to improve themethodological quality of studies.
Keywords: Low back pain, Children, Adolescents, Prevalence, Meta-analysis

Background
The term low back pain (LBP) was defined by Andersson[1] as “pain limited to the region between the lower margins of the 12th rib and the gluteal folds”. LBP is the mostcommon type of back pain [2], occurring in about 60–80%
of people at some point in their lives [3].
LBP often begins in childhood, and in adolescentsthe prevalence is similar to that of adults [4,5]. One* Correspondence: inmaculada.calvo@um.es
1
Department Physiotherapy, Faculty of Medicine, Espinardo Campus,
University of Murcia, Murcia 30100, Spain
Full list of author information is available at the end of the article

characteristic of LBP in childhood and adolescence is
its high recurrence and the tendency to reappear withgreater intensity [6]. Although initially intensity is usually
low [7] and it generally lasts for less than a week [7,8],
LBP causes limitations in carrying out activities [9], school
absenteeism and the reduction or ceasing of physicalactivity [10].
In recent years there has been a considerable increase
in research studies that examine the prevalence of LBP
in this population [7,10-16], but studies exhibit great
variability in prevalence rates, with estimates ranging
from 1.1% [14] to 66% [7].

© 2013 Calvo-Muñoz et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
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This variability found in the prevalence estimates may
be due to differences among the studies in such factors
as the age of the sample, the sample size, the definition
of LBP, the LBP recall period, the strategy for extracting
data and the methodology used.
The prevalence of a condition is the number of cases
in a specified population at a particular time [17]. The
prevalence is described in terms such as: point prevalence(the number of persons in a defined population who had a
specified disease or condition at a particular point in time,
usually the time the survey was carried out), periodprevalence (the number of persons who had a specified
disease or condition at any time during a specified timeinterval), and lifetime prevalence (the number of persons
who had a specified disease or condition at some point intheir life) [18].
According to the literature on the epidemiology of LBP
in children and adolescents, the prevalence rates increase
with the age of the subjects [11,19-21] and females havehigher prevalence rates than males [12,16,22-24]. Epidemiological studies indicate that the point prevalence is less thanthe period prevalence and, in turn, this is less thanthe lifetime prevalence [12,25,26].
Several narrative reviews have been published about theprevalence of LBP in children and adolescents [27-29].
However, to our knowledge, a meta-analysis of the LBP
prevalence in children and adolescents has not yet been
carried out. Thus, the main purpose of this research was tosystematically review the prevalence of LBP obtainedin studies with samples composed by children and/or
adolescents, in order to: (a) obtain estimates of point,
period, and lifetime prevalence rates; (b) determine
whether the heterogeneity exhibited by the prevalence
rates of the studies can be explained by random sampling alone or, on the contrary, the studies presentvery discrepant prevalence rates, and (c) in the latter
case, to search for substantive and methodologicalcharacteristics of the studies that can explain thisheterogeneity.
Based on the results of previous research, several
hypotheses were raised: (a) the mean estimate ofpoint prevalence will be lower than that of periodprevalence and, in turn, this will be lower than thatof lifetime prevalence; (b) the prevalence rates will
increase with the age of the participants in the samples,and (c) females will present higher prevalence rates
than males.

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Selection criteria of the studies

To be included in the meta-analysis, the studies had tomeet the following criteria: (a) the study must be anobservational study (survey research, longitudinal or crosssectional design) that reports any prevalence rate of low
back pain in children and/or adolescents (≤ 18 years old);(b) the sample size had to have at least 50 participants; (c)studies had to be carried out between 1980 and April of2011, as previous literature searches carried out by our
research team evidenced an absence of studies that report
LBP prevalence rates for children/adolescents before 1980;(d) studies had to be written in English, French, Italian,
Spanish, or Portuguese; (e) studies could be published or
unpublished. Cohort, case–control, controlled clinical trials,
and randomized clinical trials were excluded, as they reportincidence rates instead of prevalence rates.
Literature search

To locate studies that met the selection criteria, several
literature searching strategies were used. Firstly, thefollowing electronic databases were consulted: ISI Web
of Knowledge, MedLine, PEDro, IME, LILACS, and
CINAHL. The search period extended to April 2011,
with the Medical Subject Headings: adolescents, children,
childhood, back pain, low back pain, spinal pain, epidemiology, and prevalence. Secondly, several electronic specialized journals were consulted such as, Spine, Spine Journal,
Pain, European Spine Journal, Fisioterapia, Scandinavian
J Public Health, European Journal of Public Health,and, finally, the reference lists of the studies recovered
were also consulted.
Two reviewers independently: (a) screened the title andabstract of each reference to locate potentially relevant
studies, and once hardcopies of the screened papers wereobtained, (b) reviewed them in detail to identify articles
that fulfilled the selection criteria.
The total number of references identified with all of
the searching strategies used was 2,272, of which 2,188
were excluded in a first screening. The main reasons fordeleting these studies were because the participants inthe samples were adults (about 55%), the studies did notreport prevalence rates (about 20%), the studies wereclinical trials (about 15%), or other reasons (about 10%).
The searching process enabled us to select 59 articles that
met the selection criteria, with a total sample size of
125,483 participants. Figure 1 presents the flow chart of theselection process of the studies.
Data extraction

Methods
In order to accomplish our objectives, a systematic review
and a meta-analysis were carried out on observational
studies that reported prevalence rates of LBP in children
and/or adolescents.

With the purpose of identifying characteristics of thestudies that may affect the prevalence rates estimated, acodebook was produced that included a series of potential moderator variables of the prevalence rates of LBP.
The study characteristics were classified into substantive

Identification

Calvo-Muñoz et al. BMC Pediatrics 2013, 13:14
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Records identified by searching
databases ISI Web of Knowledge,
MEDLINE, PEDro, IME, LILACS y
CINAHL(n =2240)

Additional records
identified via othersources(n =32)

Included

Eligibility

Screening

Records after removal of duplicates(n =2272)

Records screened(n =2272)

Full-text articles
assessed for eligibility(n=84)

Records excluded(n =2188)

Full-text articlesexcluded(n=25)> 18 years (n=2)
Clinical trials (n=8)
Reviews (n=4)
Back pain (n=11)

Articles included in thequantitativesynthesis(meta-analysis)(n=59)

Figure 1 Flow chart of the selection of studies for the meta-analysis.

(subject and context variables), methodological, andextrinsic variables [30].
The following subject characteristics were coded: (a)the origin of the sample (school, community, university,or sport origin); (b) the mean age of the sample (inyears), and (c) the gender distribution of the sample(percentage of males). Two context characteristics werecoded: the continent and the country where the study
was carried out.
The following methodological characteristics were coded:(a) the initial sample size of the study; (b) the final sample
size (subjects actually used to calculate the prevalencerate); (c) the response rate (percentage of people in the
final sample divided by the number of people in the initialsample); (d) the study design (longitudinal vs. crosssectional); (e) the data extraction method (questionnaire,interview, physical examination); (f) the type of prevalence
reported (point, period, or lifetime prevalence); (g) the
methodological quality of the study, assessed with an
instrument used in previous systematic reviews on theprevalence of LBP [31-33]. This instrument contains 12
items grouped in three clusters focusing on the sample
representativeness, the quality of the data collected, andthe clarity of the definition of LBP, see Additional file 1.
Finally, two extrinsic characteristics were coded: the publication date (year) and the publication source (publishedvs. unpublished).
To assess the reliability of the coding process, two
researchers independently coded 14 studies randomly
selected from the total set of studies (24%). The inconsistencies between the two coders were solved by consensus,
and when these were due to ambiguity in the codebook,this was corrected. The codebook can be obtained fromthe corresponding author upon request. To assess thecoding reliability, intra-class and kappa coefficients were
calculated for the continuous and categorical variables,
respectively. On average, the intra-class correlation was
0.994 (range: 0.98 - 1.0), and the kappa coefficient was
0.989 (range: 0.77 - 1.0), all of which can be considered tobe highly satisfactory [34].

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Prevalence rates

Three types of prevalence were included in our review:
point, period, and lifetime prevalence. In turn, periodprevalence was classified according to the specific period oftime reported in the study (one and two weeks, 1, 3, 6,
and 12 months). From each study, at least one prevalence rate could be obtained. When a study applied alongitudinal design, only the prevalence rate at the beginning of the study was recorded in our meta-analysis. To
assess the reliability in the calculations of the prevalence
rates, the same 14 studies used to assess the coding reliability of the moderator variables were used. The mean
intra-class correlation was 0.999 (range: 0.996 – 1.0),
which was highly satisfactory [34].
Statistical analysis

As each study could report more than one prevalence rate
(e.g., point and lifetime prevalences), separate metaanalyses were carried out for the different types of prevalence in order to avoid dependence problems. To normalize
the distribution of the prevalence rates, these were transformed by means of the logit event rate: Lp = Ln[p/(1 − p)],p being the prevalence rate, Ln the natural logarithm, andLp the logit event rate. The sampling variance of each logit
event rate, V(Lp), was calculated by means of: V(Lp) = 1/(np) + 1/[n(1 − p)], with n being the sample size. Once thestatistical analyses were carried out, the results were backtransformed to prevalence rates in order to facilitate theirinterpretation, by means of: p = eLP/(eLP + 1), with e being
the base of the natural logarithm [35].
In each meta-analysis, a random-effects model was
assumed to calculate a mean prevalence rate and a 95%
confidence interval [36]. The random-effects model was
assumed because it is the most realistic assumption
when the studies to be meta-analyzed can be reasonably
considered a representative sample of a population ofpotential studies that have been conducted or that canbe conducted in the future about the topic. The
random-effects model allows a higher generalization of
the results than the fixed-effects model [37].
The heterogeneity of the prevalence rates was assessedwith the Q statistic and the I2 index. When the number ofprevalence estimates was large enough (20 or more studies),
a forest plot was constructed. In order to test whether
publication bias might be a threat to the validity of theprevalence estimates, funnel plots were constructed andDuval and Tweedie’s trim-and-fill method was applied [38].
When the Q statistic was statistically significant and theI2 index exhibited a large value (over 25%), then analysesof potential moderator variables were carried out. However, due to the small number of studies, these analyses
were applied only for the lifetime prevalence rate, as thiswas reported in 30 studies. The analyses of moderator
variables were carried out by assuming a mixed-effects

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model. For the categorical moderator variables, weighted
ANOVAs were calculated, with the QB statistic assessing
the statistical significance of the moderator variable on
the prevalence rates, and the QW statistic assessing themodel misspecification. For the continuous moderators,
weighted simple meta-regressions were applied, with the
QR statistic testing the statistical significance of themoderator variable, and the QE statistic assessing themodel misspecification. In all of the moderator analyses,
the proportion of variance accounted for by the moderator variable, R2, was calculated following Raudenbush’s
22=^τ Total, withproposal [39]. In particular, R2 ¼ 1  ^τ Res
2
2^τ Res and ^τ Total being the estimated residual and total
heterogeneity variances, respectively [40]. In order to findthe subset of moderator variables that can explain most of
the prevalence rates variability, a multiple meta-regression
model (by assuming a mixed-effects model) was adjusted.
The moderator variables included in the model wereselected taking into account the statistical significanceachieved in the previous bivariate analyses. This regression
model allowed us to identify the most relevant study
characteristics to explain the variability exhibited by
the prevalence rates.
The statistical analyses were carried out with the metaanalysis program Comprehensive Meta-analysis 2.0 [41]and with the SPSS macros elaborated by D. B. Wilson [42].
The PRISMA checklist was used to check the reporting
quality of the meta-analysis (Additional file 2).

Results
Descriptive characteristics of the studies

Fifty-nine articles fulfilled the selection criteria [4,8-12,
14,19-26,43-86], and were carried out between 1984 and2010, see Figure 1. The individual characteristics of eachof the integrated studies are presented in Additional file 3(This file contains data collected from other articles[87-103]).
The sample size distribution was very skewed, with a
median of 622 subjects and minimum and maximum
values of 88 [22] and 34,423 [43], respectively. Most of
the studies were carried out in Europe (42 studies; 73%),
followed by North America (6 studies; 10.2%), Oceania
and Asia with four studies each (6.8%), and Africa with
two studies (3.4%). Only three of the 59 studies had notbeen published. Fifty-five studies were written in English,
three in Portuguese, and one in Spanish.
Three subject characteristics were coded: the origin of
the sample, the mean age, and the percentage of males inthe sample. Most of the samples were recruited fromschools (46 studies; 78%), followed by the community(7 studies, 11.9%). On average, the mean age of the samples
was 13.6 years old (range: 9–18.4) and the mean percentage
of males was 51.1% (range: 41.5% – 100%).

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With regards to the methodological variables, the mean
response rate was 83.8%, and most of the studies used across-sectional design (52 studies, 88.1%). The data extraction method most commonly used was the questionnaire(46 studies, 78%), followed by the questionnaire and physical examination combined (5 studies, 8.5%), the interview (4 studies, 6.8%), the questionnaire and interview
combined (2 studies, 3.4%), and the interview and physicalexamination combined (2 studies, 3.4%).
Out of the different types of prevalence, period prevalence was the most commonly reported (25 studies,
42.4%), followed by the lifetime and period prevalencecombined (15 studies, 25.4%), the lifetime prevalencealone (eight studies, 13.5%), the lifetime, period, andpoint prevalences combined (five studies, 8.5%), the lifetime and point prevalences combined (three studies,5.1%), the point prevalence alone (two studies, 3.4%),
and the period and point prevalences combined (onestudy, 1.7%).
The methodological quality scores of the included
studies are reported in Additional file 4. The minimum
and maximum scores were 40% and 100%, respectively,
with a mean of 76.7%, and five studies reaching the maximum score of 100%.
For the first block of methodological questions concerning the sample representativeness (questions 1, 2
and 3 of Additional file 1), 74.6% of the studies used atleast one of the following conditions: an entire target
population, randomly selected sample, or sample statedto represent the target population. With regards tononresponse, 23.7% of the studies specified at least oneof the following situations: reasons for nonresponse
described, nonresponders described, comparison of
responders and nonresponders, or comparison of sample
and target population. Finally, 86.4% of the studies
reported the response rate.

In relation to questions related to the quality of thedata (questions 4, 5, 6, 7, 8 and 9 of Additional file 1),
the majority of the studies reported primary data on
LBP (57 studies). LBP data were collected directly fromeach child or adolescent in 79.7% of the studies, with theremaining studies being collected from a proxy. Out of
the 59 studies, 58 used the same mode of data collection
for all participants in the sample.
To collect the data, the studies used different instruments, such as questionnaires (79.7%), interviews (8.5%),and examinations (11.9%). The instruments had to bevalidated or at least its reliability had to be tested.
With regards to the definition of LBP (questions 10,
11 and 12 of Additional file 1), 61% of the studies
applied a precise anatomic delineation of the lumbar
area or made a reference to an easily obtainable article
that contained such specification. A further useful specification of the definition of LBP was applied in 52.5% of
the studies by means of questions such as the frequency,
duration or intensity, and character of the pain. 96.6% of
the studies used recall periods that were clearly stated(1 and 2 weeks, 1, 3, 6 and 12 months or lifetime).
Mean prevalence and heterogeneity testing

Table 1 presents the mean rates and 95% confidence
intervals for each one of the types of prevalence, as wellas the minimum and maximum prevalence rates, andthe heterogeneity statistics (Q and I2). The mean point
prevalence obtained from 10 studies was 0.120 (95%
confidence limits: 0.09 and 0.159). As expected, period
and lifetime prevalence rates were higher than point
prevalence. Thus, the mean period prevalence at
12 months obtained from 13 studies was 0.336 (95%
confidence limits: 0.269 and 0.410), whereas the mean
period prevalence at one week obtained from six studies
was 0.177 (95% confidence limits: 0.124 and 0.247). The

Table 1 Mean prevalences, 95% confidence intervals, and heterogeneity statistics
Q

df

I2

0.159

546.87*

9

98.3

0.247

156.56*596.8

Type of prevalence

k

N

Min.

Max.

p+

95%

C. I.

pl

pu

Point prevalence

10

49,124

0.032

0.350

0.120

0.090

6

9,812

0.097

0.350

0.177

0.124

Period prevalence:
1 week2 weeks

1

1,193

0.246

0.246

0.246

0.222

0.271

–

–

–

1 month14

23,191

0.025

0.398

0.183

0.128

0.255

1618.85*13

99.2

3 months

2

4,126

0.224

0.513

0.355

0.134

0.662

187.49*1

99.5

6 months

7

25,037

0.008

0.420

0.177

0.088

0.324

2197.00*

6

99.7

12 months

13

19,673

0.174

0.603

0.336

0.269

0.410

997.00*

12

98.8

30

61,732

0.086

0.648

0.399

0.342

0.459

4957.79*29

99.4

Lifetime prevalence* p < .001. k: number of studies. N: total sample size. Min. and Max.: minimum and maximum prevalence rates. p+: mean prevalence. pl and pu: lower and upper
confidence limits of the 95% confidence interval around the mean prevalence. Q: heterogeneity statistic to test the homogeneity hypothesis of the prevalence
rates. df: degrees of freedom of the Q statistic. I2: heterogeneity index.

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mean lifetime prevalence obtained from 30 studies was
0.399 (95% confidence limits: 0.342 and 0.459). Figure 2
presents a forest plot of the 30 lifetime prevalence rates.
The lifetime prevalence estimates exhibited a large
heterogeneity, with such extreme minimum and maximum
values as 0.086 [52] and 0.648 [9], respectively.
For all of the types of prevalence, the heterogeneity Q
statistic reached statistical significance (p < .001) and allof the I2 indices were over 95% (see Table 1). Therefore,
an analysis of the potential influence of moderator variables was in order. However, the small number of studies that reported point and period prevalence rates
discouraged the application of additional analyses onthese rates. Therefore, moderator analyses were carriedout for the lifetime prevalence rates only.
Analysis of publication bias

Out of the 30 studies that reported lifetime prevalence estimates, 29 of them were published studies. In order to check
whether publication bias might be biasing the estimatedmean prevalence, a funnel plot was constructed, aspresented in Figure 3. As the figure shows, a slightly higher
concentration of prevalence estimates was on the right sideof the mean prevalence leading to a slight asymmetry of thefunnel plot. By applying Duval and Tweedie’s trim-and-fillStudy ID

Balagué et al. (1988)
Balagué et al. (1993)
Balagué et al. (1994)
Balagué et al. (1995)
Bejia et al. (2005)
Burton et al. (1996)Çakmak et al. (2004)
Coelho et al. (2005)
Ebrall (1994)
Fairbank et al. (1984)
Gunzburg et al. (1999)
Hangai et al. (2010)
Harreby et al. (1999)
Hestbaek et al. (2004)
Jones et al. (2004)
Kristensen & Ommundsen (2001)
Kujala et al. (1992)
Murphy et al. (2007)
Newcomer & Sinaki (1996)
Olsen et al. (1992)
Prendeville & Dockrell (1998)
Prista et al. (2004)
Ratliffe (2010)
Salminen et al. (1992)
Sato et al. (2008)
Shehab et al. (2004)
Sjölie & Ljunggren (2001)
Skoffer et al. (2007)
Staes et al. (2003)
Troussier et al. (1994)
Mean prevalence (RE model)

p

pl

pu

0,345
0,336
0,210
0,510
0,274
0,116
0,325
0,481
0,403
0,086
0,360
0,616
0,589
0,320
0,402
0,630
0,280
0,550
0,510
0,304
0,415
0,280
0,564
0,303
0,288
0,578
0,646
0,648
0,603
0,368
0,399

0,320
0,255
0,191
0,471
0,240
0,080
0,275
0,414
0,365
0,063
0,314
0,602
0,563
0,309
0,360
0,559
0,211
0,512
0,411
0,279
0,347
0,223
0,523
0,279
0,283
0,529
0,541
0,607
0,592
0,341
0,342

0,371
0,428
0,230
0,549
0,310
0,166
0,380
0,549
0,442
0,116
0,409
0,630
0,615
0,331
0,446
0,696
0,361
0,587
0,608
0,330
0,487
0,346
0,604
0,328
0,293
0,626
0,739
0,687
0,614
0,396
0,459

method [38], three additional prevalence estimates shouldbe imputed to the set of original prevalence estimates toachieve symmetry in the funnel plot. The mean prevalence
rates obtained with the 30 original prevalence rates andafter imputing three estimated prevalences to the originalones were very similar: 0.399 and 0.372, respectively.
Therefore, we can discard publication bias as a threat tothe validity of the estimated mean lifetime prevalence.
Similar analyses were carried out for the other types ofprevalence that were represented in the meta-analysis withat least 10 prevalence estimates. This was the case for point
prevalence and for one and 12 months period prevalences.
Point prevalence exhibited a funnel plot that was very closeto symmetry, and only one new prevalence rate had to be
imputed to the 10 original prevalence estimates to adjust
the funnel plot to symmetry, leading to original (p+ = 0.120)
and adjusted (p+adj = 0.103) mean point prevalences thatwere very similar. With regards to one month period prevalence, the trim-and-fill method did not have to imput any
new prevalence rates to achieve symmetry in the funnel
plot. On the contrary, the 12 month period prevalenceneeded to add five new prevalence rates on the left side of
the funnel plot to achieve symmetry, leading to a clear
decrease in the mean prevalence from 0.336 (with the 13
original prevalence rates) to 0.245 (when imputing five newPrevalence and 95% CI

0

0,50

1

Figure 2 Forest plot of the lifetime prevalence rates. p: prevalence rates. pl and pu: lower and upper confidence limits of the 95%
confidence interval around the mean prevalence rate. The last row in the figure presents the mean prevalence assuming arandom-effects model.

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Page 7 of 120,0Standard Error

0,1

0,2

0,3

0,4
-3

-2

-1

0

1

2

3

Logit event rate

Figure 3 Funnel plot of the lifetime prevalence logits. The three full circles are imputed logits by means of the Duval and Tweedie’strim-and-fill method.

prevalence rates to the original ones). Therefore, although
the point and one month period prevalences do not seemto be affected by publication bias, the 12 month periodprevalence showed an overestimation of the population
prevalence rate.
Analyzing moderator variables

As mentioned above, the analysis of moderator variables
was carried out for the lifetime prevalence rates only, as
the other types of prevalence were represented in themeta-analysis with a small number of estimates (under 20).
Tables 2 and 3 present the results of applying ANOVAs
and simple meta-regressions on the subject characteristics

coded from the studies. The mean age of the participantsin the samples exhibited a positive, statistically significant
relationship with the prevalence rates, with a 45.7% ofvariance accounted for (Table 3). Figure 4 presents a
dispersion diagram that illustrates this relationship.
Other subject characteristics did not reach a statisticallysignificant relationship with the prevalence rates, such as
the origin of the sample (Table 2) and the percentage ofmales in the sample (Table 3).
The continent where the study had been carried out did
not affect prevalence rates (Table 2). However the publication year presented a statistically significant relationship with the prevalence rates, with the most recent

Table 2 Results of the weighted ANOVAs of qualitative moderator variables on the lifetime prevalence estimates
Moderator variable

k

p+

95%

C. I.

pl

pu

ANOVA results

0.314

0.472

QB(4) = 1.95, p = .745; R2 = 0.0
QW(25) = 4227.25, p < .001

Continent:
Europe21

0.390

North America

3

0.455

0.255

0.670

Oceania

1

0.403

0.128

0.757

Africa

2

0.277

0.114

0.532

Asia

3

0.490

0.285

0.698

25

0.398

0.333

0.467

QB(3) = 0.87, p = .833; R2 = 0.0
QW(26) = 3833.03, p < .001

Origin of the sample:
SchoolCommunity

2

0.396

0.197

0.638

University

2

0.470

0.249

0.703

School + sport

1

0.280

0.085

0.619

Questionnaire

23

0.411

0.354

0.471

QB(1) = 0.36, p = .546; R2 = 0.143

Interview

4

0.365

0.243

0.507

QW(25) = 2345.19, p < .001

Data extraction method:

k: number of studies. p+: mean prevalence. pl and pu: lower and upper confidence limits of the 95% confidence interval around the mean prevalence. QB:
between-categories statistic to test the influence of the moderator variable on the prevalence rates. QW: within-categories statistic to test the model
misspecification. R2: proportion of variance accounted for by the moderator variable.

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Table 3 Results of the weighted meta-regressions ofcontinuous moderator variables on the lifetime
prevalence estimates
Moderator variable k

bj

QR

p

QE

R2

p

Mean age (years)

30 0.1045.10 .024 2017.26 < .001 0.457

Gender (% of males)

27 0.008

0.34 .557 4819.51 < .001 0.0

Response rate

27 −0.023 2.94 .086 4807.91 < .001 0.0

Quality scale (0–100) 30 0.019

6.21 .013 2886.94 < .001 0.348

Publication year

9.82 .002 4957.61 < .001 0.0

30 0.062

k: number of studies. bj: regression coefficient of the moderator variable. QR:
statistic to test the influence of the moderator variable on the prevalence
rates. QE: statistic to test the model misspecification. R2: proportion of varianceaccounted for by the moderator variable.

studies showing higher prevalence rates than the oldest
ones (Table 3).
Several methodological variables were also coded in order
to examine their potential influence on the prevalence rates.
Thus, the data extraction method did not show a statistical
relationship with the prevalence rates (Table 2), although
the percentage of variance accounted for was not negligible
at 14.3%, and the questionnaires (p+ = 0.411) giving slightly
higher prevalence rates than the interviews (p+ = 0.365).
The response rate did not show a statistical relationshipwith the prevalence rates (Table 3). However, with 34.8% ofvariance accounted for, the total score of the scale used toassess the methodological quality of the studies exhibited apositive, statistically significant relationship with the prevalence rates, that is, studies with a better methodology
tended to show higher lifetime prevalence rates than studies
that were methodologically poor (Table 3).
Table 4 presents the results of analyzing the influence ofeach item of the methodological quality scale on the
lifetime prevalence rates. Out of the twelve quality
criteria, two of them showed a statistically significant
relationship with the prevalence rates. One of themwas whether the non response rate was described in
the study (p = .036, R2 = 0.676), with higher prevalence
rates when the study described the non response. The

other one was whether the study clearly defined thespecific area of pain (p < .001, R2 = 0.376), with higher
prevalence rates in the studies with a clear specification of
the area of pain.
An explanatory model

Several substantive and methodological characteristics ofthe studies showed a statistical relationship with the lifetime
prevalence rates, so a multiple regression model can offer a
predictive model of the expected prevalence under certain
conditions. The predictor variables included in the model
were the mean age of the participants in the sample, thepublication year, and two methodological variables: the total
score on the quality scale (0–100) and the quality item“delimitation of pain” (Yes, 1; No, 0). As shown in Table 5,
the predictive model reached a statistically significant relationship with the prevalence rates (p < .001, R2 = 0.438).
Two of the four predictive variables showed a statisticallysignificant relationship with the prevalence rates once theother predictors were controlled: the delimitation of pain
(p = .041) and the publication year (p = .013). With the regression coefficients shown in Table 5, it is possible to makepredictions about what is the expected lifetime prevalence
under certain conditions. The model was however misspecified, so other study characteristics may be influencing the
lifetime prevalence rates obtained in the studies.

Discussion
The aim of this research was to examine, by means of ameta-analytic investigation, the prevalence rates of LBP inchildren and adolescents, as well as to search for characteristics of the studies that can explain the heterogeneity
exhibited by the prevalence rates. With this purpose, a totalof 59 studies fulfilled our selection criteria and were
included in our meta-analysis.
The results confirmed the hypothesis that the lifetime
prevalence is higher than the period prevalence and, in turnthis is higher than the point prevalence. These results coincide with those obtained by Louw et al. [33] that detected

0,80

Logit prevalence rate

0,42
0,04
-0,34
-0,72
-1,10
-1,48
-1,86
-2,24
-2,62
-3,00
8,06

9,19

10,32

11,44

12,57

13,70

14,83

15,96

17,08

18,21

19,34

Mean age (years)

Figure 4 Dispersion diagram of the simple meta-regression of the mean age of the subjects and the logit prevalence rates.

Calvo-Muñoz et al. BMC Pediatrics 2013, 13:14
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Table 4 Results of the weighted ANOVAs of quality items on the lifetime prevalence estimates
Quality item

k

p+

95%

C. I.

pl

pu

ANOVA results

Target population described:
Yes

21

0.416

0.346

0.489

QB(1) = 0.77, p = .380; R2 = 0.0

No

9

0.358

0.260

0.469

QW(28) = 4942.26, p < .001

Yes

5

0.486

0.401

0.572

QB(1) = 4.42, p = .036; R2 = 0.676

No

20

0.384

0.343

0.427

QW(23) = 1162.44, p < .001

Yes

27

0.403

0.343

0.467

QB(1) = 0.21, p = .648; R2 = 0.0

No

3

0.358

0.202

0.551

QW(28) = 4910.99, p < .001

Yes

26

0.411

0.344

0.480

QB(1) = 0.87, p = .352; R2 = 0.0

No

4

0.326

0.190

0.498

QW(28) = 4853.39, p < .001

Yes

26

0.400

0.339

0.464

QB(1) = 1.32, p = .250; R2 = 0.015

No

3

0.517

0.332

0.698

QW(27) = 4754.11, p < .001

Yes

20

0.472

0.413

0.532

QB(1) = 17.98, p < .001; R2 = 0.376

No

10

0.266

0.205

0.338

QW(28) = 2192.08, p < .001

Questionnaire

16

0.399

0.316

0.487

QB(1) = 0.00, p = .999; R2 = 0.0

Interview

14

0.399

0.311

0.494

QW(28) = 4635.79, p < .001

Non response described:

Response rate reported:

Direct reporting from subjects:

Validated instrument:

Delimitation of pain:

Specification of pain:

k: number of studies. p+: mean prevalence. pl and pu: lower and upper confidence limits of the 95% confidence interval around the mean prevalence. QB:
between-categories statistic to test the influence of the moderator variable on the prevalence rates. QW: within-categories statistic to test the model
misspecification. R2: proportion of variance accounted for by the moderator variable. Five of the 12 quality items were not included in the table due to one of
their categories being represented by one study only or by none (survey designed for studying prevalence rates, data collection was uniform, validated interview,
validated exam, and memory period clear).

mean LBP point, one-year, and lifetime prevalences foradolescents of 12%, 33%, and 36%, respectively.
The hypothesis that the prevalence rate would increase
with the age of the participants in the samples was
confirmed by our results. These findings are in line withthose obtained by Balagué et al. [11] and Jones et al. [19].
Balagué et al. [11] reported lifetime prevalences of 16% andTable 5 Results of the multiple meta-regression on the
lifetime prevalence estimates
Predictor variable

bj

Z

p

Results of the full model

Constant−80.160 −2.49 .013

Delimitation of pain

−0.516−2.05 .041 QR(4) = 27.24, p < .001

Mean age (years)

0.029

0.55

.584 R2 = 0.438

Quality scale (0–100)

0.005

0.63

.531 QE(25) = 1306.02, p < .001

Publication year

0.040

2.49

.013

bj: partial regression coefficient of each moderator variable. Z: statistic to test
the influence of each moderator variable on the prevalence rates. QR: statistic
to test the statistical significance of the full model. QE: statistic to test themodel misspecification. R2: proportion of variance accounted for by themoderator variables.

58% for children and adolescents, respectively, and Joneset al. [19] reported lifetime prevalences of 18.2% for 10 years
old children and of 65.6% for 16 year old adolescents.
With regards to gender, authors such as Shebad et al.
[20] and Kovacs et al. [62] found higher lifetime prevalences for women (64.7% and 69.3%, respectively) than formen (50.8% and 50.9%, respectively). On the contrary,
Newcomer et al. [72] reported higher prevalence rates formen than for women (57% and 44%, respectively). Finally,
Olsen et al. [74] did not find relevant differences between
the lifetime prevalence rates for men and women (30.7%
and 30%, respectively). The results of our meta-analysis
exhibited a nonstatistically significant relationship between
gender and lifetime prevalence and, as a consequence, ourhypothesis of a larger prevalence for women than for men
was not confirmed.
The large heterogeneity found in all types of prevalenceindicates the existence of characteristics of the studies
causing this variability. The publication year influenced
the lifetime prevalence rates, with the higher prevalence
rates being reported in the most recent studies. This result

Calvo-Muñoz et al. BMC Pediatrics 2013, 13:14
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seems to be very solid, as in the multiple meta-regression
model publication year was one of the two predictors that
achieved a statistically significant relationship with the
lifetime prevalence, once controlled the methodologicalquality of the studies, the delimitation of pain, and themean age of the sample. However, what our analyses do
not enable us to determine are the reasons of that increasein the prevalence rates. Thus, it could be that children arechanging their activity level or they are more overweight;
it could be that the actual prevalence has not changed but
the reporting has; or it could be that the questions used toassess the prevalence have changed. This point would
need more research. Regarding the methodological qualityof the studies, the studies with a better quality tended toshow higher lifetime prevalence rates than studies thatwere methodologically poor.
The most common methodological shortcomings of thestudies included in the review were the lack of a clear definition and delimitation of LBP and the absence of important specifications of LBP such as the frequency of
episodes, its intensity and duration. In particular, out of thestudies published between 1980 and 2001, only 40.9%
reported a clear definition of LBP, whereas in the mostrecent studies (from 2002 to date) this percentage was 73%.
With regards to the LBP specifications, the percentage was
71% for studies published in the last 10 years and 29% for
those that were not published in the last 10 years.
Limitations of the meta-analysis

It is important to note some limitations of our meta-analysis. The small number of studies that reported point andperiod prevalence rates discouraged the analysis of moderator variables and, as a consequence, this kind of analysis
was applied for lifetime prevalence rates only. On the other
hand, the absence of a more detailed description in the
primary studies about such important aspects as the exact
area of pain caused uncertainty in our coding process.
Implications for clinical practice

The results of our meta-analysis have important consequences for professionals. Our finding of higher prevalence
rates in the most recent studies suggests that LBP is aproblem that is increasing in childhood and adolescence.
As a consequence, more attention should be devoted todevelop and apply prevention programs for young children
in order to break this trend. On the other hand, our findingof higher prevalence rates in the studies with older subjects
points to the need for efforts towards an early detection of
LBP in children and adolescents.
Implications for future research

Our results enable us to make recommendations for future
research in this field. Firstly, it is advisable that future studies report more information regarding the definition ofPage 10 of 12

LBP. Issues relating to the exact area of pain, the frequency
of episodes, their intensity, and the duration need to
be specified. Secondly, there is a need to improve themethodological quality of studies in order to avoid threatsagainst the representativeness of the samples and theinternal validity of the studies. Finally, studies should reportprevalence rates of LBP disaggregated by age and sex.

Conclusion
In conclusion, the most recent studies seem to show higher
prevalence rates than the oldest ones, and studies with abetter methodology tend to show higher lifetime prevalence
rates than studies that are methodologically poor.
Additional files
Additional file 1: Methodological criteria of epidemiological studies.
Additional file 2: PRISMA Checklist.
Additional file 3: Characteristics of the studies presenting LBP
prevalence data used in this meta-analysis.
Additional file 4: Methodological Quality of the 59 epidemiologicalstudies.

Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
All authors contributed to the conception and design, acquisition, analysis
and interpretation of data and drafting of the manuscript. AGC and JSM
participated in the critical revision of the manuscript for important
intellectual content. ICM and JSM performed the statistical analyses.
All authors read and approved the final manuscript.
Acknowledgements
No funding was received for this study.
Author details
1
Department Physiotherapy, Faculty of Medicine, Espinardo Campus,
University of Murcia, Murcia 30100, Spain. 2Department of Physiotherapy,
University of Murcia, Murcia, Spain. 3Department of Basic Psychology andMethodology, University of Murcia, Murcia, Spain.
Received: 5 September 2012 Accepted: 17 January 2013
Published: 26 January 2013
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doi:10.1186/1471-2431-13-14
Cite this article as: Calvo-Muñoz et al.: Prevalence of low back pain inchildren and adolescents: a meta-analysis. BMC Pediatrics 2013 13:14.