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Keywords:

  • overweight;
  • obesity;
  • chronic disease risk factors;
  • geographical location;
  • gender differences;
  • public health
  • surpoids;
  • obésité;
  • facteurs de risque de maladies chroniques;
  • situation géographique;
  • différences entre les sexes;
  • santé publique
  • sobrepeso;
  • obesidad;
  • factores de riesgo enfermedad crónica;
  • localización geográfica;
  • diferencias de género;
  • salud pública

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

Objective  To evaluate demographic variation in the prevalence of overweight (OW) and obesity (OB) among 3240 children and adolescents (girls: n = 1714; boys: n = 1526) aged 9–16 years attending primary and secondary schools in Benue State of Nigeria.

Methods  Participants’ anthropometric characteristics (body weight, stature, body mass index: BMI and lean body mass: LBM) were determined using standard protocols. OW and OB were estimated using International Obesity task Force diagnostic criteria. Data were analysed with one-way anova and binary logistic regression method.

Results  Overall, 88.5%, 9.7% and 1.8% of the adolescents had normal BMI and were OW and obese, respectively. Prevalence of OW was higher among girls (20.3%) than boys (16.2%), whereas a relatively higher incidence of OB was noted among the boys (3.5%). Girls in urban areas had a significantly higher BMI (t524 = 3.61, P = 0.002) than their rural peers, but the rural girls were more significantly OW than their urban counterparts (BMI: t1186 = 2.506). Logistic regression models assessing the influence of age, gender and location on OW/OB in children (α2(3, N = 1014) = 6.185, P = 0.103) and adolescents (α2(3, N = 2226) = 1.435, P = 0.697) did not turn up significant results. In the gender-specific analysis, the younger boys’ model was also not significant (α2(2, N = 488) = 1.295, P = 0.523) in contrast to the girls’ (α2(2, N = 526) = 15.637, P = 0.0005), thus discriminating between OW and healthy weight among the children. Overall, the model explained 2.9–4.4% of the variance in weight status and correctly classified 76.8% of the cases. Age wise, the model yielded a significant odds ratio of 1.49, suggesting that the likelihood of being OW increases by a factor of 1.5 with a unit increase in age. Also, the likelihood of an urban girl becoming OW or obese was 0.57 times that of a rural girl.

Conclusions  In general, girls in urban areas had higher prevalence of OW and OB than girls in rural settings. Among the boys, similar but less marked trends were found, except that the rural boys tended to be more OW on average than their peers in urban areas. In view of its public health significance, it is important to periodically evaluate the prevalence of weight disorders in children and adolescents so that appropriate preventative strategies can be instituted.

Objectif:  Evaluer la variation démographique dans la prévalence du surpoids (SP) et de l’obésité (OB) parmi 3.240 enfants et adolescents (1.714 filles et 1.526 garçons) âgés de 9 à 16 ans fréquentant les écoles primaires et secondaires dans l’Etat de Benue au Nigeria.

Méthodes:  Les caractéristiques anthropométriques des participants (poids corporel, stature, indice de masse corporelle: IMC et masse corporelle maigre: MCM) ont été déterminées en utilisant des protocoles standard. SP et OB ont été estimés à l’aide des critères de diagnostic de l’International Obesity Task Force (IOTF). Les données ont été analysées avec la méthode ANOVA one-way et de régression logistique binaire.

Résultats:  Dans l’ensemble, 88,5% des adolescents avaient un IMC normal, 9,7% avaient un SP et 1,8%étaient obèses. La prévalence du SP était plus élevée chez les filles (20,3%) que chez les garçons (16,2%), tandis qu’une incidence relativement plus élevée de l’OB a été notée chez les garçons (3,5%). Les filles dans les zones urbaines avaient un IMC significativement plus élevé (IMC: t524 = 3,61, p = 0,002) que celles en milieu rural, mais les filles en milieu rural étaient nettement plus en surpoids que celles en milieu urbain (IMC: t1186 = 2,506). Les modèles de régression logistique évaluant l’influence de l’âge, du sexe et de la localisation sur l’obésité/surpoids chez les enfants (χ2(3, N = 1014) = 6,185; p = 0,103) et les adolescents (χ2(3, N = 2226) = 1,435; p = 0.697) n’ont pas révélé des résultats significatifs. Dans l’analyse spécifique au sexe, les résultats du modèle des jeunes garçons n’était pas non plus significatifs (χ2(2, N = 488) = 1,295; p = 0,523), contrairement à celui des filles (χ2(2, N = 526) = 15,637; p = 0,0005), discriminant ainsi entre surpoids et poids sain parmi les enfants. Globalement, le modèle expliquait 2,9 à 4,4% de la variance dans le statut pondéral et a classé correctement 76,8% des cas. Pour ce qui est de l’âge, le modèle a révélé un rapport de cote (OR) significatif de 1,49 ce qui suggère que la probabilité d’être en SP augmente d’un facteur 1,5 avec une augmentation d’une unité de l’âge. Aussi, la probabilité d’une jeune fille en milieu urbain d’être en SP ou de devenir OB était 0,57 fois plus élevée que celle d’une jeune fille en milieu rural.

Conclusions:  En général, les filles dans les zones urbaines avaient une prévalence plus élevée de SP et d’OB que les filles en milieu rural. Chez les garçons, des tendances similaires, mais moins marquées ont été trouvées, sauf que les garçons en milieu rural avaient tendance àêtre en moyenne plus en SP que ceux en milieu urbain. Compte tenu de son importance pour la santé publique, il est important d’évaluer périodiquement la prévalence des troubles de poids chez les enfants et les adolescents afin que des stratégies de prévention appropriées puissent être instituées.

Objetivo:  Evaluar las variaciones demográficas en la prevalencia del sobrepeso (SP) y la obesidad (OB) entre 3240 niños y adolescentes (niñas: n=1714; niños: n=1526) con edades comprendidas entre los 9-16 años, escolarizados en escuelas primarias y secundarias del estado de Benué en Nigeria.

Métodos:  Utilizando protocolos estándar, se determinaron las características antropométricas de los participantes (peso, estatura, índice de masa corporal: IMC y la masa corporal magra: MCM). El SP y la OB se calcularon utilizando el criterio diagnóstico del grupo de trabajo sobre la obesidad (el International Obesity task Force - IOTF). Los datos se analizaron con una ANOVA de un sentido y el método de regresión logística binaria.

Resultados:  En total un 88.5%, 9.7% y 1.8% de los adolescentes tenían respectivamente un IMC normal, SP y eran obesos. La prevalencia de SP era mayor entre las niñas (20.3%) que entre los niños (16.2%), mientras que se observaba una incidencia relativamente mayor de OB entre los niños (3.5%). Las niñas provenientes de áreas urbanas tenían un IMC significativamente mayor (t524=3.61, p=0.002) que sus pares de áreas rurales, pero las niñas rurales eran significativamente más obesas que sus contrapartes (IMC: t1186=2.506). Los modelos de regresión logística para evaluar la influencia de la edad, el género y la localización sobre el sobrepeso/obesidad en niños (χ2(3, N = 1014)=6.185, p=0.103) y adolescentes (χ2(3, N = 2226)=1.435, p=0.697) no aportaron resultados significativos. En el análisis género-específico el modelo para niños más jóvenes tampoco era significativo (χ2(2, N = 488)=1.295, p=0.523), en contraste con el de las niñas (χ2(2, N = 526)=15.637, p=0.0005); discriminando entre el sobrepeso y un peso sano entre los niños. En general el modelo explicaba 2.9-4.4% de la varianza en el estatus de peso y clasificó de forma correcta un 76.8% de los casos. En términos de edad, el modelo arrojaba una razón de posibilidades significativa de 1.49, sugiriendo que la probabilidad de tener sobrepeso aumentaba en un factor de 1.5 con un aumento de 1 unidad de edad. Además, la probabilidad de que una niña viviendo en un área urbana tuviese sobrepeso o fuese obesa era 0.57 veces el de una niña viviendo en una zona rural.

Conclusiones:  En general, las niñas de áreas urbanas tenían una mayor prevalencia de sobrepeso y obesidad que las niñas de zonas rurales. Entre los niños se observaba una tendencia similar pero no tan marcada, excepto que los niños rurales tendían a tener, en promedio, más sobrepeso que sus pares de áreas urbanas. Dada su relevancia a nivel de salud pública, es importante evaluar periódicamente la prevalencia de los desórdenes de peso en niños y adolescentes de forma que puedan instituirse las estrategias preventivas apropiadas.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

Paediatric obesity (OB) has become a major public health problem, with increasing prevalence worldwide not only in industrialised countries, but also in developing countries including those in Africa. This situation undoubtedly presents a major public health challenge. OB has been defined as the accumulation of excessive adipose tissue to an extent that impairs physical as well as psychosocial health and well-being (Philip 2004). Overweight (OW) and OB are associated with increased risks of cardiovascular disease (CVD), hypertension, diabetes mellitus and other chronic diseases (Cole et al. 2000; Nieman 2003).

According to Dietz (1994), the critical periods for the development of OB are infancy, early childhood and adolescence. OB is known to track from childhood to adulthood, and it often begins early in childhood. When this occurs, the chances for adult OB are three times greater than in children of normal body weight. Tracking studies have indicated that 70–80% of obese children and adolescents are likely to become obese adults (Nicklasen et al. 2006), obese boys have a 78% likelihood of becoming obese adults and girls, 66% (Must 1996).

Although OW and OB in both developed and developing countries show similar trends, different patterns exist from country to country. These patterns are sometimes inconsistent across national and regional boundaries. Studies have shown that Indian boys exhibit a higher prevalence of OB than girls (Ramachandran et al. 2002). But in some African countries like Ethiopia, South Africa and Zimbabwe, BMI values of girls between five and 14 years were found to be slightly higher than those of boys (Medical Research Council of South Africa 2006; Mascie-Taylor & Goto 2007). This pattern is similar to reports from Britain and Australia (Taylor et al. 2005; Sanigorski et al. 2007). Regarding the influence of location on OW/OB rates, McMurray et al. (1999) reported a greater proportion of obese children in the rural than in the urban areas in North Carolina (29.5% rural vs. 21.7% urban) even after adjusting for the greater fat intake score of the rural children. The WHO Global database on BMI, National data on rural–urban pre-OB and OB rates for eight countries (Czech Republic, Egypt, India, Iran, Morocco, Pakistan, Peru and South Africa) indicated that urban pre-OB and OB rates were on the average significantly higher than rural ones (Mascie-Taylor & Goto 2007).

Recent studies have demonstrated dramatic increases in the prevalence of OW and OB in youths worldwide (Wang & Lobstein 2006; Dehghan et al. 2005; Bundred et al. 2001). For instance, in the United States, 25% of the children are OW and 11% obese (Dehghan et al. 2005), and OB is rated a second leading cause of preventable morbidity and mortality, surpassed only by smoking (Wang & Lobstein 2006). Prevalence of OW and OB among Greenlandic children and adolescents is 16.6% and 4.7%, respectively (Schnohr et al. 2005). Among South African children, prevalence rates are of 17.2–22.8% for OW and OB (Armstrong et al. 2006). Currently, our understanding of prevalent rates of OW and OB among youths in Nigeria is limited because of lack of representative data from different parts of the country. For instance, Opara et al. (2010) report an OB rate of 11.3% among primary school children in Uyo, a state capital, and Musa et al. (2002) documented an OW prevalence rate of 3.6% for male adolescents in Kano City, a state capital in Nigeria. Studies on OW and OB in Nigerian children have used different diagnostic criteria, but have highlighted the high prevalence of body weight disorders (Goon et al. 2009, 2011).

The purpose of this study was to examine the demographic variation in the prevalence of OW and OB among children and adolescents in Benue State of Nigeria. A secondary purpose was to determine the best demographic predictors individually or jointly of the variation in the prevalence of OW in Benue State.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

Design

A cross-sectional study design was used to evaluate the prevalence of OW and OB in a group of children and adolescents from primary and secondary schools in Benue State of Nigeria.

Setting

The participants in the study were selected from rural and urban settings in three senatorial districts of Benue State (Benue North, Benue Central and Benue South). Benue State is an agrarian state located within the North Central geopolitical zone of Nigeria. In the rural settings, the staple food is pounded white yam (Dioscorea rotundata), which has high carbohydrate content. Anecdotally, dietary practice in the urban setting is similar, except for occasional eating of fast foods such as meat pie, ice cream, yoghurt and pasta.

Participants

Participants comprised female and male children aged 9–16 years attending 21 schools (10 primary and 11 secondary schools) in the study setting. Multistage and systematic sampling techniques were used to select 3320 participants. In the first stage, 21 schools (seven from each senatorial district) were randomly selected from 1271 schools within the study area. In the second stage, children and adolescents were selected in a systematic manner from the school registers and were asked to participate in the study. In each selected class, every 5th child starting from the first or second on the register was selected to participate in the study. At the time of data collection, all children and adolescents were apparently healthy and had not participated in any organised exercise programme for at least 6 months prior to the study. Participants with known health problems were excluded from the study. The purpose and procedures of the test were fully explained to participants after obtaining permission from the heads of school. The research protocol was approved by the ethics review board of Benue State University, Makurdi. Written informed consent of parents and assent of children were obtained before participation.

Anthropometric measurements

Participants’ anthropometric characteristics were measured using the protocol of the International Society for the Advancement of Kinanthropometry (ISAK) (Marfell-Jones et al. 2006). Body mass and stature were measured using an electronic weighing scale (Seca digital floor scale, Sec-880; Bournville, Birmingham, UK) and a wall-mounted stadiometer (Seca model Sec-206; Bournville). Stature was measured to the nearest 0.1 cm, and measurement of body mass was taken with participants in minimal clothing to the nearest 0.1 kg. Participants’ body mass index (BMI) was calculated by dividing body mass by stature squared (kg/m2). OW and OB were defined using the age- and sex-specific BMI cut-off points proposed by the Childhood Obesity Working Group of the International Obesity Task Force (Cole et al. 2000). The cut-off points correspond to the most widely used adult cut-off points of 25 and 30 kg/m2 for OW and OB, respectively. BMI is considered one of the most appropriate measures for the indirect assessment of adiposity in youth (Frontini et al. 2001).

Data analysis

Descriptive statistics (Means, SDs, frequencies and percentages) were used to analyse the raw data. Prevalence of OW and OB was examined using frequencies and percentage distributions. Independent samples t-test was used to test for differences in BMI between genders and geographical locations. One-way analysis of variance (anova) and Scheffe multiple comparison method were used to test for age differences in BMI. The independent association of age and location of participants and OW/OB was examined using binary logistic regression models. Odds ratios of being OW were calculated for age and between location categories. The amount of variation in the dependent variable explained by the model was determined using the Cox and Snell R Square and the Negelkerke R Square. Separate analyses were performed for boys and girls in both age groups. All statistical analyses were conducted using SPSS for Windows (version 18.0, Chicago IL, USA) at a probability level of 0.05 or less.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

Because of absenteeism and incomplete data, 3240 participants (97.6%) completed the tests and their data were subsequently used in statistical analysis. Anthropometric characteristics and prevalence rates of OW and OB in the Nigerian boys and girls are presented in Table 1. In the younger age group, participants differed only in stature (P < 0.001) and body mass (P = 0.002) with the girls being taller. Of the total number of children, 78.5% had normal BMI, 18.3% were OW and 3.2% obese, with no significant gender difference (P = 0.67) in BMI. Gender-specific prevalence rates are shown in Table 1. In the adolescent group, participants differed in four variables (stature: P = 0.01; body mass: P < 0.0005; lean body mass; LBM: P = 0.001 and BMI: P = 0.014). Except for LBM among the boys, girls had higher values in the other three variables.

Table 1.   Anthropometric characteristics and weight status (Mean ± SD) of participants (n = 3240)
VariableChildren (n = 1014)Adolescents (n = 2226)
Boys (n = 488)Girls (n = 526) t-valueSig.Boys (n = 1038)Girls (n = 1188) t-valueSig.
  1. Sig., significant level; LBM, Lean body mass; BMI, body mass index; OW, overweight; OB, obese.

Age (year)10.1 ± 0.810.2 ± 0.81.670.09513.5 ± 1.313.6 ± 1.30.430.67
Stature (cm)137.5 ± 10.5140.1 ± 9.73.970.0005149.8 ± 12.0151.0 ± 10.82.540.01
Body mass (kg)34.0 ± 7.135.5 ± 8.13.130.00242.7 ± 9.244.3 ± 8.84.320.0005
LBM (kg)30.0 ± 5.929.5 ± 6.01.360.17637.0 ± 8.236.0 ± 6.53.160.002
BMI17.9 ± 3.118.0 ± 3.40.430.67019.0 ± 3.919.4 ± 3.72.450.014
OW (%)16.220.3  9.310.0  
OB (%)3.52.9  2.11.6  

Body mass index was normal in 88.5% of the adolescents, whereas 9.7% were OW and 1.8% OB. In both groups, prevalence of OW was higher among girls than boys, whereas OB prevalence was higher among boys.

When OW and OB were combined (Table 2), the prevalence rate was greater (21.5%) in the children than adolescents (11.5%). In both age groups, the prevalence rates were greater among the girls than the boys.

Table 2.   Prevalence of OW and OB combined among participants (n = 3240)
Group n OW (%)OB (%)Total (%)
  1. OW, overweight; OB, obese; %, per cent.

Children1014186 (18.3)32 (3.2)218 (21.56)
Adolescents2226216 (9.7)41 (1.8)257 (11.5)
Total3240402 (12.4)73 (2.3)475 (14.7)

Table 3 details the mean BMI and the prevalence rates of OW and OB stratified by age and sex. The average BMI for girls generally increases with age but that of boys is inconsistent, particularly among young boys (9–11 years). However, among the adolescent boys, there was an increase in BMI scores from 12 to 14 years, and thereafter, this remained constant up till age 16 years.

Table 3.   Prevalence of OW and OB stratified by age and sex using the IOTF criteria
AgeBoys (n = 1526)Girls (n = 1714)
N BMI MSD% OW% OB N BMI MSD% OW% OB
  1. M, mean; SD, standard deviation; %, per cent; BMI, body mass index; OW, overweight; OB, obese; IOTF, International Obesity task Force.

912917.53.2143.911216.92.413.41.8
1016118.23.121.75.018417.73.218.52.7
1119818.03.013.12.023018.83.725.23.5
Total48817.93.116.23.552618.03.420.32.9
1227618.53.77.64.331318.83.010.91.0
1326618.73.612.40.430219.23.69.32.0
1424419.53.911.90.025919.73.710.41.9
1515519.54.26.54.520920.13.911.01.0
169719.54.14.12.110520.24.66.72.9
Total103819.03.99.32.1118819.43.710.01.6

The prevalence of OW and OB tends to be higher among the younger boys and girls (9–11 years) than among adolescent children. Among the children, OW and OB rates were highest in the 10-year-old boys and 11-year-old girls (28.7%). However, there was no significant gender difference in BMI (t(1012) = 0.426, P = 0.67). In the adolescent group, prevalence of OW and OB was highest among the 13-year-old boys (12.8%) and 14-year-old girls (12.3%). However, the girls had a significantly higher BMI (t224 = 2.457, P = 0.014) than the boys.

Prevalence of OW and OB by geographical location and sex is presented in Table 4. Generally, boys living in rural areas had higher OW and OB prevalence rates than those living in urban areas. Among the girls, children living in urban areas had higher prevalence rates of OW and OB (28.7%) compared with their rural peers (18.7%).

Table 4.   Prevalence of OW and OB stratified by sex and location using the IOTF criteria
LocationBoys (n = 1526)Girls (n = 1714)
N BMI MSD% OW% OB N BMI MSD% OW% OB
  1. M, mean; SD, standard deviation; %, per cent; BMI, body mass index; OW, overweight; OB, obese; IOTF, International Obesity task Force.

Children
 Rural27918.02.818.32.528517.62.617.21.4
 Urban20917.83.413.44.824118.54.124.14.6
Adolescents
 Rural65419.14.19.32.367819.23.410.61.5
 Urban38419.03.49.41.851019.73.99.21.8

Among the children, boys in the rural areas had the tendency of being more OW than their urban peers, but there was no significant difference in their BMI (t486 = 0.892, P = 0.373). However, the urban girls had significantly higher BMI (t524 = 3.61, P = 0.002) than their rural counterparts. In the adolescent group, boys in rural areas tended to be marginally more OW (assessed with BMI) than their contemporaries in the rural areas (Boys: t1036 = 0.489, P = 0.625). Rural girls were, however, more OW than those in the urban areas with a significant difference in the BMI between the two locations (t1186 = 2.506).

Results of the logistics regression model assessing the impact of age, gender and location on OW/OB of children (α2(3, N = 1014) = 6.185, P = 0.103) and adolescents (α2(3, N = 2226) = 1.435, P = 0.697) were not significant. In the gender-specific analysis among the younger age group, like in the combined model, the boys’ model was also not significant (α2(2, N = 488) = 1.295, P = 0.523) and could only explain between 0.1% and 0.4% of the variance in weight status. Among the girls, however, the model was significant (α2(2, N = 526) = 15.637, P = 0.0005) (Table 5), indicating that it was able to distinguish between OW and healthy weight. Overall, the model explained between 2.9% and 4.4% of the variation in weight status and correctly classified 76.8% of the cases.

Table 5.   The odds of becoming overweight in female children according to age and location (n = 526)
VariableOdds ratio95% CL P-value
Location
 Urban10.38–0.870.008
 Rural0.57
Age1.491.13–1.980.005

As shown in Table 5, age made significant contribution to the model with an odds ratio of 1.49 suggesting that the likelihood of being OW increases by a factor of 1.5 with a unit increase in age. Regarding geographical location, the results showed that the likelihood of an urban girl becoming OW or obese was 0.57 times that of a rural girl. Among the adolescents, the gender-specific models (boys: α2(2, N = 1038) = 1.75, P = 0.417; girls: α2(2, N = 1188) = 0.4, P = 0.819) were not significant.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

There is an increasing prevalence of OW and OB in children and adolescents worldwide with negative deleterious metabolic and social health consequences (Strauss 2000; Danniels et al. 2005). Although comprehensive data on childhood OB are only recently available in Nigeria, these have been derived from studies in which different diagnostic criteria have been applied, thus making meaningful comparisons cumbersome (Goon et al. 2009, 2011; Opara et al. 2010). Also, Goon et al. (2009) pointed out the need to determine appropriate definitions of OW and OB in order not to overestimate prevalence rates. Thus, we examined in this population-based study the prevalence of OW and OB among school children and adolescents from Benue State of Nigeria.

According to WHO, the prevalence of OW and OB is no longer limited to developed countries, but are now a growing serious health concern in developing countries (Berghofer et al. 2008). In the USA, high prevalence rates of the epidemic of OW and OB, which has negative socio-economic and health consequences, range from 29% in white men to 50% in black women (Wang et al. 2007). Based on the WHO-MONICA study, prevalence rates across Europe range from 7% in Sweden to 45% in Lithuanian women (Berghofer et al. 2008). In South Africa, one in five children is either OW or obese (Reddy et al. 1998).

In the present study, the prevalence rates of OW and OB combined for boys and girls in the younger age group were 19.7% and 23.2%, respectively. The corresponding rates for adolescent boys and girls were 11.4% and 11.6%, respectively. In both age groups, the rates were higher in girls. Pereira et al. (2010) reported strikingly higher prevalence rates of combined OW and OB of 29.9% and 26.0% for 6- to 10-year-old Portuguese boys and girls. But the higher rate of OW and OB combined in Portuguese girls compared with boys is similar to those observed in this study. The incidence rates of combined OW and OB of 15.9% and 13.7% reported for 6- to 11-year-old Turkish boys and girls (Pirincci et al. 2010) are lower than the rates noted for the younger age group in this study. However, the higher rate of OW and OB in Turkish boys compared with their female counterparts is at variance with our finding in which the OW and OB rates are higher in girls compared with boys. Similarly, the prevalence of combined OW and OB of 16.6% and 4.7% found among Greenlandic children and adolescents as reported by Schnohr et al. (2005) is lower than those noted for the children and adolescents in our study.

With the exception of the young girls in the present study, an inconsistent sex-related trend was found regarding increasing prevalence of OW and OB in both age groups. However, the prevalence of OW and OB was higher among the children than adolescents. Pereira et al. (2010) reported a similar finding in which no significant increasing or decreasing age-related trend was found regarding the prevalence of OW and OB in their sample. A higher prevalent rate of OW and OB in children than adolescents has also been reported by Schnohr et al. (2006).

In this study, children in the urban area exhibited high OW and OB compared with their counterparts in the rural areas. As such from the public health perspective, the epidemic of OW and OB may have serious consequences, such as low human work capacity because of chronic disease of lifestyle associated with OW and OB. According to Malina et al. (2004), both OW and OB have a negative effect on growth and motor development of children and adolescents. Given the fact that the children and adolescents in our study are still in their formative years, OW and OB may have long-term effects on their growth and development.

Several cross-sectional studies in some parts of Nigeria reported similar prevalence of OW and OB in the children. For instance, using CDC’s BMI charts, Goon et al. (2009) reported prevalence rates of 2.1%, (1.6% boys) and 3.2% (2.8% girls) for OW and OB, respectively, in a cohort of 9- to 12-year-old school children. Particularly striking is the result of the present study in which the prevalence of OW is higher in rural boys in contrast to the boys and girls in urban areas, who had higher prevalence rates of OW and OB. These findings may be explained in the light of the plausibility that the children are becoming increasingly sedentary. The children also have limited opportunities to engage in structured regular physical activities as many of the schools in the rural area do not have adequate sports equipment and facilities which could facilitate the teaching of physical education.

Limitations

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

Our findings should be interpreted in the light of a number of limitations. First, the cross-sectional nature of the study may mask growth-related changes, which would have been discernible using a longitudinal design. Second, given the demographic and cultural diversity of Nigeria, the present findings cannot be generalised to the entire country. Third, genetic endowment could also account for the observed findings, but this was beyond the scope of the study. Finally, the children’s socio-economic status was not evaluated as this could have given a clearer indication of their lifestyle, thus elucidating the findings.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References

The prevalence of both OW and OB among Nigerian children and adolescents from two different settings was observed. In general, girls in urban areas had higher prevalence of OW and OB than their counterparts in rural settings. Among the boys, similar but less marked trends were found, except that the rural boys tended to be more OW on average than their peers in urban areas who were found to be more obese. Given the increasing prevalence of OW and OB in the children and adolescents, the present study recommends that urgent preventative strategies be implemented in Benue State primary and secondary schools to raise awareness about the health hazards of disordered body weight. Additionally, the study recommends the introduction of community-based wellness programmes to prevent OW and OB among the children and adolescents.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Limitations
  8. Conclusions
  9. References
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