Skip to main content
Download PDF

Prevalence and socio-demographic correlates of stunting and thinness among Pakistani primary school children

BMC Public Health volume 11, Article number: 790 (2011) Cite this article

Abstract

Background

Child growth is internationally recognized as an important indicator of nutritional status and health in populations. Child under-nutrition is estimated to be the largest contributor to global burden of disease, and it clusters in South Asia but literature on under-nutrition among school-aged children is difficult to find in this region. The study aimed to assess the prevalence and socio-demographic correlates of stunting and thinness among Pakistani primary school children.

Methods

A population-based cross-sectional study was conducted with a representative multistage cluster sample of 1860 children aged 5-12 years in Lahore, Pakistan. Stunting (< -2 SD of height-for-age z-score) and thinness (< -2 SD of BMI-for-age z-score) were defined using the World Health Organization reference 2007. Chi-square test was used as the test of trend. Logistic regression was used to quantify the independent predictors of stunting and thinness and adjusted odds ratios (aOR) with 95% confidence interval (CI) were obtained. Linear regression was used to explore the independent determinants of height- and BMI-for-age z-scores. Statistical significance was considered at P < 0.05.

Results

Eight percent (95% CI 6.9-9.4) children were stunted and 10% (95% CI 8.7-11.5) children were thin. Stunting and thinness were not significantly associated with gender. Prevalence of stunting significantly increased with age among both boys and girls (both P < 0.001) while thinness showed significant increasing trend with age among boys only (P = 0.034). Significant correlates of stunting included age > 8 years, rural area and urban area with low SES, low-income neighborhoods, lower parental education, more siblings, crowded housing and smoking in living place (all P < 0.001). Significant correlates of thinness included rural area and urban area with low SES, low-income neighborhoods and lower parental education (all P < 0.001), and age > 10 years (P = 0.003), more siblings (P = 0.016) and crowded housing (P = 0.006). In multivariate logistic regression analyses adjusted simultaneously for all factors, older age (aOR 3.60, 95% CI 1.89-6.88), urban area with low SES (aOR 2.58, 95% CI 1.15-5.81) and low-income neighborhoods (aOR 4.62, 95% CI 1.63-13.10) were associated with stunting while urban area with low SES (aOR 2.28, 95% CI 1.21-4.30) was associated with thinness. In linear regression analyses adjusted for all factors, low-income neighborhoods and older age were associated with lower height-for-age z-score while rural area with low/disadvantaged SES was associated with lower BMI-for-age z-score.

Conclusions

Relatively low prevalence of stunting and thinness depicted an improvement in the nutritional status of school-aged children in Pakistan. However, the inequities between the poorest and the richest population groups were marked with significantly higher prevalence of stunting and thinness among the rural and the urban poor, the least educated, the residents of low-income neighborhoods and those having crowded houses. An increasing trend with age was observed in prevalence of stunting and thinness. Smoking in living place was associated with stunting. Findings suggest the need to implement evidence-based child health policy and strategies, prioritizing the poor and socially disadvantaged population.

Peer Review reports

Background

Child growth is internationally recognized as an important indicator of nutritional status and health in populations. Stunting (low height-for-age) is acknowledged as the best indicator for child growth [1]. It indicates chronic under-nutrition and reflects the cumulative effects of under-nutrition and recurrent infections. Thinness (low BMI-for-age) corresponds to wasting and indicates acute under-nutrition, usually because of insufficient food intake or a high incidence of infectious diseases. Weight-for-age is inadequate indicator for monitoring child growth beyond pre-school years due to its inability to distinguish between relative height and body mass, therefore, BMI-for-age is recommended by the World Health Organization (WHO) and United States' Centers for Disease Control and Prevention (US CDC) to assess underweight in school-aged children and adolescents [2, 3]. Stunting results from long-term nutritional deprivation, inadequate childcare and poor environmental and socio-cultural conditions. It is associated with higher morbidity and mortality, delayed mental development, poor educational achievement and reduced intellectual capacity, and is a strong predictor of human capital and social progress [4–6]. Child under-nutrition is estimated to be the largest contributor to global burden of disease, killing millions of children in the developing countries and causing heavy health expenditures [7–9]. It clusters in South Asia but literature on under-nutrition among school-aged children is difficult to find [10–13]. Routine surveillance for under-nutrition among school-aged children has not been conducted and national nutritional surveys do not include school-aged children in this region including Pakistan. In 2009-10, a cross-sectional survey titled the Nutritional Assessment among School-going Children in Lahore, Pakistan (NASCL) was conducted among primary school children aged 5-12 years. Prevalence and socio-demographic correlates of under-nutrition (stunting and thinness) among Pakistani primary school children is the subject of current paper.

Methods

Design, setting and sample

This was a population-based cross-sectional study among primary school children aged 5-12 years in Lahore, Pakistan. Lahore is the capital of Pakistan's most populous province Punjab and a metropolis with multiethnic populations. It has a population of about 9 million, including about 2.5 million primary school children aged 5-12 years, and 81% of the population resides in the urban area [14].

A multistage cluster sample of 1860 children aged 5-12 years in twelve primary schools of City District Lahore was enrolled. In the first stage, stratified random sampling based on the population and educational system characteristics, was used to have proportionate representation of gender, area of residence and socioeconomic status (SES). The list of all the public and private primary schools in Lahore was provided by the Punjab Department of Education. The listed schools were stratified according to the geographic area and monthly fee structure of the schools into following four strata: a) urban with high SES (urban area and fee > 2500 PKR), b) urban with middle SES (urban area and fee = 1000-2500 PKR), c) urban with low SES (urban area and fee < 1000 PKR), and d) rural with low/disadvantaged SES (rural area and fee ~100 PKR or free). The former two strata included private (including public-private mix) schools and the later two strata included public schools. In Pakistan, public schools cater low SES urban and rural children while high and middle SES urban children are educated in private and public-private mix schools. Three schools were selected at random from each stratum, and were contacted by the Departments of Education and Health to participate voluntarily in the study. School administration of three schools refused to participate and the next school was selected randomly from the respective stratum. For each school, a list of all classes in five grades (one to five) was obtained and one class in each grade was randomly selected. In this way, 60 classes, five from each school, were selected. For each of the selected classes, first 31 children on class attendance register, present on data collection day and aged 5-12 years, were included in the study. Children not willing to participate in the study were excluded. Sample size was calculated using Epi Info 6.04d (US CDC, 2004) with a confidence (1-α) of 95%, anticipated prevalence of 5% and margin of error of ± 1. The minimum sample size calculated was 1823 and a sample of 1860 was deemed sufficient.

Data Collection

The sampled schools were visited on pre-arranged dates in summer 2009. Twenty trained senior medical students including 10 males and 10 females, lead by the Principal Investigator, collected the data. Data collection activity in each school was completed in two working days and it took four weeks to complete data collection. Data collection activity was planned to avoid measurements during the first two weeks of a new school term or immediately after a major holiday. Health education of children and teachers was also carried out after data collection in the respective school. Analogue physician health scales were used to measure height and weight [15]. All instruments were standardized before the measurements and balances were zero calibrated on a daily basis. Height and weight were measured without shoes and in light summer school uniform. Timing of the measurements was in mornings or early afternoons. Height measurement was in centimeters (cm) and weight was measured in kilogram (kg) with a range of 0-160 kg. Height and weight were measured to the nearest 0.1 cm and 0.5 kg respectively. Feet were placed together with heels, buttocks and shoulder blades against the stick and head positioned in the Frankfurt horizontal plane.

For each of the sampled classes, demographic information of all officially enrolled students was obtained before data collection, including gender, date of birth, residential address and parental education. Demographic information of students not found on official rosters but currently enrolled in that class was obtained from class teachers. Parental education level was based on the parent with the highest total years of schooling and neighborhood income level was based on the approximate income estimate of child's residential area obtained from the Revenue Department of City District Government Lahore. The study instrument was a structured questionnaire, designed in English. It was pre-tested in the study's intended population and modified accordingly. The questionnaire was tested for reliability (one-week test-retest). It included questions on family and socio-cultural environment including parental working status, number of siblings, number of persons in child's living room and smoking in living space [16, 17]. Senior medical students trained in the interviewing techniques interviewed children in the presence of their class teacher (guardian). Each child was asked regarding whether his/her mother works outside or is she a housewife, how many older/younger siblings he/she has, how many persons were living in his/her living room and does anyone smoke in living place? Smoking was defined as smoking tobacco by cigarette, cigar, pipe or hookah. Hookah (water pipe) is a single or multi-stemmed instrument used for smoking tobacco in South Asia.

Quality control measures and good practices included training of data collection team, pre-testing of processes and materials and field monitoring of data collection. Timely availability of the study instruments, meeting of data collection team at the end of everyday to share experiences and submit completed forms, and troubleshooting field problems was ensured. Informed consent statement was printed on the form. Verbal informed consent for the child to participate in the study was taken from class teachers and school heads considering them as guardians. As the study involved no invasive procedure, verbal informed consent was deemed sufficient. The study was approved by the Ethical Review Board of Allama Iqbal Medical College, Lahore. Permissions to conduct the study were granted by the Punjab Departments of Education and Health, and the sampled schools.

Statistical Analysis

Data were entered and analyzed by manual and computerized checking using SPSS version 18.0 (SPSS Inc. Chicago IL, United States, 2009). Age was calculated to the precise day by subtracting the date of birth from the date of examination. The z-score values for height-for age and BMI-for-age were calculated using the WHO AnthroPlus [18]. Stunting (< -2SD of height-for-age z-score) and thinness (< -2SD of BMI-for-age z-score) were defined using the WHO reference 2007 [19, 20]. For international comparison, three grades of thinness were also defined using the International Obesity Task Force (IOTF) cut-offs corresponding to a BMI of 18.5, 17.0 and 16.0 kg/m2 at 18 years of age [21]. Bivariate analysis, using chi-square test as the test of trend, was conducted to compare differences in prevalence of stunting and thinness among the study variables. Crude odds ratios (OR) with 95% confidence interval (CI) were calculated to examine the relationship between stunting and thinness and the study variables by univariate analyses. Multivariate logistic regression was used to quantify the independent predictors of stunting and thinness and adjusted odds ratios (aOR) with 95% CI were obtained. Linear regression was used to explore the predictive power of independent variables in relation to height-for-age and BMI-for-age z-scores as dependent variables. Statistical significance was considered at P < 0.05 and all tests were 2-sided.

Results

The study included a sample of 1860 primary school children aged 5-12 years. The male-female ratio was 1.11 with 52.5% boys and 47.5% girls. The sample involved 20% children from each grade and 25% children from each area and SES stratum. Seventy-five percent children were urban and 25% were rural. Twenty percent parents were illiterate followed by those educated up to high school (27%), college (28%) and higher (25%). Majority of children had 1-3 siblings (54%) followed by > 3 siblings (44%) and no sibling (1%). Most children (51%) had > 3 persons in living room followed by 1-3 persons (43%) and no person (6%). Smoking in living place was 30%. Most children (49%) lived in middle-income neighborhoods followed by low-income (35%) and high-income (16%) neighborhoods. Median age (range) was 8 (5-12) years and mean age (SD) was 8.49 (1.81) years.

Stunting (chronic under-nutrition)

Mean (SD) height and mean (SD) height-for-age z-score was 128.4 (11.4) cm and -0.2 (1.3) respectively. Eight percent (95% CI 6.9-9.4) children were stunted while mild stunting (< -1 SD of height-for-age z-score) and severe stunting (< -3 SD of height-for-age z-score) were observed in 18% (95% CI 16.6-20.1) and 1% (95% CI 0.7-1.6) children respectively [Table 1].

Table 1 Prevalence of stunting among primary school children in Lahore, Pakistan
Full size table

Gender disparity in overall stunting prevalence was not significant. Stunting among children aged 5-6 years (4%) and 7-8 years (6%) was significantly lower (P < 0.001) as compared to children aged 9-10 years (10%) and 11-12 years (19%). Stunting increased with age among both boys and girls, and the trend was significant (both P < 0.001). More girls were stunted in 9-10 years age group while stunting was higher among boys in all other age groups [Figure 1]. Rural children with low/disadvantaged SES (17%) and urban children with low SES (10%) had significantly higher risk of being stunted (P < 0.001) than urban children with middle SES (2%) or high SES (3%). Among both boys and girls, stunting was significantly higher in rural area and urban area with low SES (boys P < 0.001, girls P = 0.001). In rural area, stunting was higher among boys while girls were more likely to be stunted in urban area [Figure 2]. Children living in low-income neighborhoods (17%) were significantly more likely to be stunted (P < 0.001) than those living in middle-income (4%) and high-income (2%) neighborhoods. Stunting was significantly higher in low-income neighborhoods among both boys and girls (both P < 0.001). Boys were more likely to be stunted in low-income neighborhoods while stunting was higher among girls in middle-income and high-income neighborhoods [Figure 3]. Stunting among children with illiterate parents was 14% that significantly decreased to 4.5% among children with parents having higher education (P < 0.001). Stunting was 4% among children having no siblings that significantly increased to 12% among children having > 3 siblings (P < 0.001). Eleven percent children having > 3 persons in living room were stunted and the proportion significantly decreased to 1% among children with no person in living room (P < 0.001). Smoking in living place was significantly associated with stunting (P < 0.001). [Table 2]

Figure 1
figure 1

Age- and gender-specific prevalence (with confidence interval bars) of stunting among primary school children in Lahore, Pakistan.

Full size image
Figure 2
figure 2

Gender-specific prevalence (with confidence interval bars) of stunting among primary school children in Lahore, Pakistan by area and socioeconomic status (SES).

Full size image
Figure 3
figure 3

Gender-specific prevalence (with confidence interval bars) of stunting among primary school children in Lahore, Pakistan by neighborhood income level.

Full size image
Table 2 Socio-demographic correlates of stunting and thinness among primary school children in Lahore, Pakistan
Full size table

In univariate analyses, significant predictors of stunting included age > 8 years, rural area and urban area with low SES, low-income neighborhoods, lower parental education, crowded housing and smoking in living place. Multivariate logistic regression analysis was adjusted simultaneously for all socio-demographic factors [Table 3]. Age 11-12 years as compared to age 5-6 years (aOR 3.60, 95% CI 1.89-6.88), urban area with low SES as compared to urban area with high SES (aOR 2.58, 95% CI 1.15-5.81) and low-income neighborhoods (aOR 4.62, 95% CI 1.63-13.10) as compared to high-income neighborhoods showed a significant independent association with stunting. In linear regression analysis of all socio-demographic factors significantly associated with stunting (as independent variables) and height-for-age z-score (as dependent variable), high-income neighborhood showed a significant independent positive association while age (in months) showed a significant independent inverse association [Table 4].

Table 3 Logistic regression analysis of socio-demographic factors associated with stunting among primary school children in Lahore, Pakistan (n = 1860)
Full size table
Table 4 Linear regression analysis of socio-demographic factors associated with height-for-age z-score among primary school children in Lahore, Pakistan (n = 1860)a
Full size table

Thinness (acute under-nutrition)

Mean (SD) BMI and mean (SD) BMI-for-age z-score was 20.7 (5.0) kg/m2 and -0.3 (1.5) respectively. Ten percent (95% CI 8.7-11.5) children were thin while mild thinness (< -1 SD of BMI-for-age z-score) and severe thinness (< -3 SD of BMI-for-age z-score) were observed in 23% (95% CI 20.8-24.6) and 3% (95% CI 2.1-3.6) children respectively [Table 5]. According to the IOTF cut-offs, thinness grade 1 was observed in 1.2% (95% CI 0.8-1.8, n = 22) children and thinness grades 2 and 3 were observed in only four and five children respectively.

Table 5 Prevalence of thinness among primary school children in Lahore, Pakistan
Full size table

Gender showed no significant association with overall thinness prevalence. Children aged 5-6 years (11%) and 7-8 years (8%) were significantly less likely to be thin (P = 0.003) as compared to children aged 9-10 years (9%) and 11-12 years (17%). Thinness was 12% among boys aged 5-6 years that slightly decreased among boys aged 7-10 years (8-9%) and peaked among boys aged 11-12 years (17%), and the trend was significant (P = 0.034). Thinness was not significantly associated with age among girls [Figure 4]. Children living in rural area with low/disadvantaged SES (14.4%) and urban area with low SES (14%) had significantly higher risk of being thin (P < 0.001) than children living in urban area with middle SES (7%) or high SES (5%). Among both boys and girls, thinness was significantly higher in rural area and urban area with low SES (boys P < 0.001, girls P = 0.001). In rural area and urban area with middle SES and high SES, thinness was higher among girls while boys were more likely to be thin in urban area with low SES [Figure 5]. Living in low-income neighborhoods (14%) significantly increased the risk of thinness (P < 0.001) as compared to living in middle-income (9%) or high-income (5%) neighborhoods. Among both boys and girls, thinness was significantly higher in low-income neighborhoods (boys P = 0.001, girls P = 0.003). Boys were more likely to be thin in middle-income neighborhoods while thinness was higher among girls in low-income and high-income neighborhoods [Figure 6]. Thinness among children with illiterate parents was 15% that significantly decreased to 6% among children with parents having higher education (P < 0.001). Children having more siblings (P = 0.016) and more persons in living room (P = 0.006) were significantly more likely to be thin. Smoking in living place was not significantly associated with thinness. [Table 2]

Figure 4
figure 4

Age- and gender-specific prevalence (with confidence interval bars) of thinness among primary school children in Lahore, Pakistan.

Full size image
Figure 5
figure 5

Gender-specific prevalence (with confidence interval bars) of thinness among primary school children in Lahore, Pakistan by area and socioeconomic status (SES).

Full size image
Figure 6
figure 6

Gender-specific prevalence (with confidence interval bars) of thinness among primary school children in Lahore, Pakistan by neighborhood income level.

Full size image

In univariate analyses, significant predictors of thinness included rural area and urban area with low SES, low-income and middle-income neighborhoods, lower parental education and crowded housing. In multivariate logistic regression analysis adjusted simultaneously for all socio-demographic factors, only urban area with low SES as compared to urban area with high SES (aOR 2.28, 95% CI 1.21-4.30) showed a significant independent inverse association with thinness [Table 6]. In linear regression analysis of all socio-demographic factors significantly associated with thinness (as independent variables) and BMI-for-age z-score (as dependent variable), only rural area with low/disadvantaged SES showed a significant independent inverse association [Table 7].

Table 6 Logistic regression analysis of socio-demographic factors associated with thinness among primary school children in Lahore, Pakistan (n = 1860)
Full size table
Table 7 Linear regression analysis of socio-demographic factors associated with BMI-for-age z-score among primary school children in Lahore, Pakistan (n = 1860)a
Full size table

Discussion

Prevalence estimates and socio-demographic correlates of stunting and thinness among Pakistani primary school children aged 5-12 years are presented. This was the first study in Pakistan to report prevalence estimates for under-nutrition among school-aged children based on the WHO reference 2007. We could find only three studies in previous literature with a representative sample that report prevalence of stunting (14-17%) and wasting (25-32%) among school-aged children in Pakistan using the World Health Organization/National Centre of Health Statistics (WHO/NCHS) reference [11–13]. Prevalence of stunting and underweight among urban school-aged children in Pakistan has been reported as 17% and 30% respectively in 1990-94 (National Health Survey of Pakistan, n = 1670) that decreased to 14% and 27% respectively in 2004-05 (Karachi survey, n = 1381) [13]. Significant improvement in the nutritional status was observed in the present study with stunting and thinness prevalence of 8% and 10% respectively; however, the inequities were more marked with the highest prevalence being among the poor and socially disadvantaged population. Thinness prevalence by the IOTF cut-offs was very low and grade 1 thinness corresponded to severe thinness by the WHO reference.

Both stunting and thinness were not significantly associated with gender. However, gender differences were more marked in low socioeconomic groups with boys being more undernourished, consistent with previous literature [22]. Stunting and thinness showed a significant increasing trend with age and older age was the independent predictor of stunting. Progression of height deficit with increasing age had been reported previously in Pakistan and elsewhere [8, 23–27].

Rural children, urban children with low SES and those living in low-income neighborhoods were at higher risk of being stunted and thin. Poverty and low socioeconomic status had a more detrimental effect on linear growth than on body weight, in line with previous literature [4, 28–30]. Higher stunting and wasting among rural children had been reported in Pakistan and elsewhere [12, 31–36]. Economic inequality is an independent determinant for childhood under-nutrition and a number of studies have illustrated that the poor children tend to be at higher risk of being undernourished and having restricted growth [11, 25, 26, 28, 31, 37–45]. Countries with a greater degree of economic inequality tend to have a poor health status than countries with more economic equality [46]. The developing countries remain vulnerable to food insecurity, poor access to health services, under-nutrition and increased morbidity and mortality, and the health and nutritional benefits from economic growth tend to be concentrated only among the economically advantaged population groups [9, 28, 31, 38, 47].

Stunting and thinness among children with illiterate parents was significantly higher as compared to children with parents having higher education. Parental education had been identified in other populations as a predictor of under-nutrition [25, 26, 31, 41–44, 48–50]. Poorest population segments are the least educated that increases the gap between the richest and the poorest. Stunting and thinness were significantly higher in children having more siblings and living in crowded houses, both these factors are indirect predictors of lower socioeconomic status. Smoking in living place was significantly associated with being stunted while the association was not significant for thinness. Although the effect did not remain significant in adjusted analysis but it corresponded with previous studies that report higher risk of childhood under-nutrition with smoking [51–53].

Integrated nutrition programs in the developing countries have had a substantial impact through a combination of targeted interventions involving fields of health, water supply, sanitation, food and education [54–57]. A school-based food program focused on the poorest population groups can be considered in the developing countries where a child can be fed in school just for about US$ 34 annually and school-based feeding programs can provide a launching pad for preventive programs including immunization, growth monitoring, deworming and targeted micronutrient supplementation [57]. In Pakistan, School Health and Nutrition Supervisors working in the National Maternal, Newborn, and Child Health (MNCH) Program can be used for this purpose.

Cross-sectional nature of the study should be considered when interpreting the findings. Although data collection followed a standard protocol, digital scales were not used. Variability in the data ascertainment may have introduced error into the prevalence estimates; however, we do not anticipate large or systematic differences. In Pakistan, estimates for neighborhood income level were not available in the census and statistical data; therefore, the division in low-, middle- and high-income neighborhoods was based on the approximate estimates by the Revenue Department of City District Government Lahore. The findings can be generalized to urban South Asian primary school children, who share the same genetic and environmental factors with the sample.

Conclusions

Relatively low prevalence of stunting and thinness depicted an improvement in the nutritional status of school-aged children in Pakistan. However, the inequities between the poorest and the richest population groups were marked with significantly higher prevalence of stunting and thinness among the rural and the urban poor, the least educated, the residents of low-income neighborhoods and those having crowded houses. An increasing trend with age was observed in prevalence of stunting and thinness. Smoking in living place was associated with stunting. Findings suggest the need to implement evidence-based child health policy and strategies, prioritizing the poor and socially disadvantaged population. Future national nutritional surveys in the developing countries ought to consider including school-aged children and nutritional component of the child health programs need to be strengthened.

References

  1. World Health Organization: Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. 1995, Geneva, (Technical Report Series No. 854)

    Google Scholar 

  2. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J: Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007, 85: 660-667. 10.2471/BLT.07.043497.

    Article  PubMed  PubMed Central  Google Scholar 

  3. US Centers of Disease Control and Prevention, the Pediatric Nutrition Surveillance System: Growth Indicators. [http://www.cdc.gov/pednss/what_is/pednss_health_indicators.htm#growth]

  4. Black RE, Allen LH, Bhutta ZA, et al: Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008, 371: 243-260. 10.1016/S0140-6736(07)61690-0.

    Article  PubMed  Google Scholar 

  5. Victora CG, Adair L, Fall C, et al: Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008, 371: 340-357. 10.1016/S0140-6736(07)61692-4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Grantham-McGregor S, Cheung YB, Cueto S, Glewwe P, Richter L, Strupp B: Developmental potential in the first 5 years for children in developing countries. Lancet. 2007, 369: 60-70. 10.1016/S0140-6736(07)60032-4.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Black R, Morris S, Bryce J: Where and why are 10 million children dying every year?. Lancet. 2003, 361: 2226-2234. 10.1016/S0140-6736(03)13779-8.

    Article  PubMed  Google Scholar 

  8. Ezzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ: Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 2003, 362 (9380): 271-280. 10.1016/S0140-6736(03)13968-2.

    Article  PubMed  Google Scholar 

  9. United Nations, Standing Committee on Nutrition: The fifth report on the world nutrition situation: Nutrition for improved development outcomes. 2004, New York

    Google Scholar 

  10. de Onis M, Frongillo EA, Blossner M: Is malnutrition declining? An analysis of changes in levels of child malnutrition since 1980. Bull World Health Organ. 2000, 78: 1222-1233.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Khuwaja S, Selwyn BJ, Shah SM: Prevalence and correlates of stunting among primary school children in rural areas of southern Pakistan. J Trop Pediatr. 2005, 51 (2): 72-77. 10.1093/tropej/fmh067.

    Article  PubMed  Google Scholar 

  12. Anwer I, Awan JA: Nutritional status comparison of rural with urban school children in Faisalabad District, Pakistan. Rural remote health. 2003, 3 (1): 130-

    CAS  PubMed  Google Scholar 

  13. Jafar TH, Qadri Z, Islam M, Hatcher J, Bhutta ZA, Chaturvedi N: Rise in childhood obesity with persistently high rates of undernutrition among urban school-aged Indo-Asian children. Arch Dis Child. 2008, 93 (5): 373-378. 10.1136/adc.2007.125641.

    CAS  Article  PubMed  Google Scholar 

  14. Pakistan Census Organization, Government of Pakistan: Census Report of Pakistan 1998. 1998, Islamabad, Pakistan

    Google Scholar 

  15. East High Scales, China Scale Manufacturer, Nanjing, China: ZT Mechanical Physician Scale. [http://www.easthighscale.com/ZT-Mechanical-Physician-Scale.html]

  16. Durkin MS, Islam S, Hasan ZM, Zaman SS: Measures of socioeconomic status for child health research: comparative results from Bangladesh and Pakistan. Soc Sci Med. 1994, 38: 1289-1297. 10.1016/0277-9536(94)90192-9.

    CAS  Article  PubMed  Google Scholar 

  17. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD: Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 2006, 60 (2): 95-101. 10.1136/jech.2004.028092.

    Article  PubMed  PubMed Central  Google Scholar 

  18. World Health Organization: WHO AnthroPlus for personal computers Manual: Software for assessing growth of the world's children and adolescents. 2009, Geneva

    Google Scholar 

  19. World Health Organization: WHO Child Growth Standards. [http://www.who.int/growthref/en/]

  20. Butte NF, Garza C, de Onis M: Evaluation of the feasibility of international growth standards for school-aged children and adolescents. J Nutr. 2007, 137: 153-157.

    CAS  PubMed  Google Scholar 

  21. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH: Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000, 320: 1240-1243. 10.1136/bmj.320.7244.1240.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Wamani H, Astrøm AN, Peterson S, Tumwine JK, Tylleskar T: Boys are more stunted than girls in Sub-Saharan Africa: a meta-analysis of 16 demographic and health surveys. BMC Pediatrics. 2007, 7: 17-10.1186/1471-2431-7-17.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kimani-Murage EW, Kahn K, Pettifor JM, et al: The prevalence of stunting, overweight and obesity, and metabolic disease risk in rural South African children. BMC Public Health. 2010, 10: 158-10.1186/1471-2458-10-158.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Al-Saffar AJ: Stunting among primary-school children: a sample from Baghdad, Iraq. East Mediterr Health J. 2009, 15 (2): 322-329.

    Google Scholar 

  25. Kikafunda JK, Walker AF, Collett D, Tumwine JK: Risk Factors for Early Childhood Malnutrition in Uganda. Pediatrics. 1998, 102: e45-10.1542/peds.102.4.e45.

    CAS  Article  PubMed  Google Scholar 

  26. Katona-Apte J, Mokdad A: Malnutrition of Children in the Democratic People's Republic of North Korea. J Nutr. 1998, 128: 1315-1319.

    CAS  PubMed  Google Scholar 

  27. Adair LS, Guilkey DK: Age-specific determinants of stunting in Filipino children. J Nutr. 1997, 127 (2): 314-320.

    CAS  PubMed  Google Scholar 

  28. Zere E, McIntyre D: Inequities in under-five child malnutrition in South Africa. Int J Equity Health. 2003, 2: 7-10.1186/1475-9276-2-7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kanjilal B, Mazumdar PG, Mukherjee M, Rahman MH: Nutritional status of children in India: household socio-economic condition as the contextual determinant. Int J Equity Health. 2010, 9: 19-10.1186/1475-9276-9-19.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Janevic T, Petrovic O, Bjelic I, Kubera A: Risk factors for childhood malnutrition in Roma settlements in Serbia. BMC Public Health. 2010, 10: 509-10.1186/1471-2458-10-509.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Larrea C, Kawachi I: Does economic inequality affect child malnutrition? The case of Ecuador. Soc Sci Med. 2005, 60: 165-178. 10.1016/j.socscimed.2004.04.024.

    Article  PubMed  Google Scholar 

  32. Smith LC, Ruel MT, Ndiaye A: Why is child malnutrition lower in urban than in rural areas? Evidence from 36 developing countries. World Dev. 2005, 33 (8): 1285-1305. 10.1016/j.worlddev.2005.03.002.

    Article  Google Scholar 

  33. Van de Poel E, O'Donnell O, Van Doorslaer E: Are urban children really healthier? Evidence from 47 developing countries. Soc Sci Med. 2007, 65 (10): 1986-2003. 10.1016/j.socscimed.2007.06.032.

    Article  PubMed  Google Scholar 

  34. de Souza AC, Peterson KE, Cufino E, Gardner J, Craveiro MV, Ascherio A: Relationship between health services, socioeconomic variables and inadequate weight gain among Brazilian children. Bull World Health Organ. 1999, 77 (11): 895-905.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Li Y, Guo G, Shi A, Li Y, Anme T, Ushijima H: Prevalence and correlates of malnutrition among children in rural minority areas of China. Pediatr Int. 1999, 41: 549-556. 10.1046/j.1442-200x.1999.01103.x.

    CAS  Article  PubMed  Google Scholar 

  36. Ricci JA, Becker S: Risk factors for wasting and stunting among children in Metro Cebu, Philippines. Am J Clin Nutr. 1996, 63: 966-975.

    CAS  PubMed  Google Scholar 

  37. Hakeem R: Socio-economic differences in height and body mass index of children and adults living in urban areas of Karachi, Pakistan. Eur J Clin Nutr. 2001, 55: 400-406. 10.1038/sj.ejcn.1601172.

    CAS  Article  PubMed  Google Scholar 

  38. Thang NM, Popkin BM: In an era of economic growth, is inequity holding back reduction in child malnutrition in Vietnam?. Asian Pa J Clin Nutr. 2003, 12: 405-410.

    Google Scholar 

  39. Hong R, Banta JE, Betancourt JA: Relationship between household wealth inequality and chronic childhood under-nutrition in Bangladesh. Int J Equity Health. 2006, 5: 15-10.1186/1475-9276-5-15.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Van de Poel E, Hosseinpoor AR, Speybroeck N, Van Ourti T, Vega J: Socioeconomic inequality in malnutrition in developing countries. Bull World Health Organ. 2008, 86: 282-291. 10.2471/BLT.07.044800.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Uthman OA: Using extended concentration and achievement indices to study socioeconomic inequality in chronic childhood malnutrition: the case of Nigeria. Int J Equity Health. 2009, 8: 22-10.1186/1475-9276-8-22.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Janevic T, Petrovic O, Bjelic I, Kubera A: Risk factors for childhood malnutrition in Roma settlements in Serbia. BMC Public Health. 2010, 10: 509-10.1186/1471-2458-10-509.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Mikki N, Hanan F, Abdul-Rahim HF, Faisal Awartani F, Holmboe-Ottesen G: Prevalence and sociodemographic correlates of stunting, underweight, and overweight among Palestinian school adolescents (13-15 years) in two major governorates in the West Bank. BMC Public Health. 2009, 9: 485-10.1186/1471-2458-9-485.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Pongou RS, Ezzati M: Health impacts of macroeconomic crises and policies: determinants of variation in childhood malnutrition trends in Cameroon. Int J Epidemiol. 2006, 35: 648-656. 10.1093/ije/dyl016.

    Article  PubMed  Google Scholar 

  45. Van de Poel E, Hosseinpoor AR, Jehu-Appiah C, Vega J, Speybroeck N: Malnutrition and the disproportional burden on the poor: the case of Ghana. Int J Equity Health. 2007, 6: 21-10.1186/1475-9276-6-21.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ross NA, Wolfson MC, Dunn JR, Berthelot J, Kaplan GA, Lynch JW: Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics. BMJ. 2000, 320: 898-902. 10.1136/bmj.320.7239.898.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Ruger JP, Kim HJ: Global health inequalities: an international comparison. J Epidemiol Community Health. 2006, 60: 928-936. 10.1136/jech.2005.041954.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Wachs TD: Mechanisms linking parental education and stunting. Lancet. 2008, 371 (9609): 280-281. 10.1016/S0140-6736(08)60144-0.

    Article  PubMed  Google Scholar 

  49. Semba RD, de Pee S, Sun K, Sari M, Akhter N, Bloem MW: Effect of parental formal education on risk of child stunting in Indonesia and Bangladesh: a cross-sectional study. Lancet. 2008, 371 (9609): 322-328. 10.1016/S0140-6736(08)60169-5.

    Article  PubMed  Google Scholar 

  50. Moestue H, Huttly S: Adult education and child nutrition: the role of family and community. J Epidemiol Community Health. 2008, 62 (2): 153-159. 10.1136/jech.2006.058578.

    CAS  Article  PubMed  Google Scholar 

  51. Semba RD, Kalm LM, de Pee S, Ricks MO, Sari M, Bloem MW: Paternal smoking is associated with increased risk of child malnutrition among poor urban families in Indonesia. Public Health Nutr. 2007, 10 (1): 7-15.

    Article  PubMed  Google Scholar 

  52. Best CM, Sun K, de Pee S, Bloem MW, Stallkamp G, Semba RD: Parental tobacco use is associated with increased risk of child malnutrition in Bangladesh. Nutrition. 2007, 23 (10): 731-738. 10.1016/j.nut.2007.06.014.

    CAS  Article  PubMed  Google Scholar 

  53. Chowdhury F, Chisti MJ, Hossain MI, Malek MA, Salam MA, Faruque AS: Association between parental smoking and nutritional status of under-five children attending Diarrhoeal Hospital, Dhaka, Bangladesh. Acta Paediatr. 2011, 100 (3): 390-395. 10.1111/j.1651-2227.2010.02067.x.

    Article  PubMed  Google Scholar 

  54. Monteiro CA, D'Aquino Benicio MH, Conde WL, et al: Narrowing socioeconomic inequality in child stunting: the Brazilian experience, 1974-2007. Bull World Health Organ. 2010, 88: 305-311. 10.2471/BLT.09.069195.

    Article  PubMed  Google Scholar 

  55. Marek T, Diallo I, Ndiaye B, Rakotosalama J: Successful contracting of prevention services: fighting malnutrition in Senegal and Madagascar. Health Policy Plan. 1999, 14: 382-389. 10.1093/heapol/14.4.382.

    CAS  Article  PubMed  Google Scholar 

  56. Sguassero Y, de Onis M, Carroli G: Community-based supplementary feeding for promoting the growth of young children in developing countries. Cochrane Database Syst Rev. 2005, CD005039-

    Google Scholar 

  57. United Nations Children's Fund: Progress for Children: a report card on nutrition, number 4. School feeding alleviates hunger and gives education a boost. 2006, New York, [http://www.unicef.org/progressforchildren/2006n4/index_aditionalstories.html#school]

    Google Scholar 

Pre-publication history

Download references

Acknowledgements

We dedicate the work to loving memories of Ubeera Shahid (1987-2010, Allama Iqbal Medical College Class of 2011) who left us in an unfortunate motorway car accident. We are grateful to Professor Javed Akram, TI, MBBS, MD, MRCP, FRCP (Eng, Ed & Glasg), FACP, FACC, Hussain M Abdullah, Final Year MBBS, Komal Mushtaq, Fourth year MBBS, Mahar M Shafique, MBBS, M Rizwan, MBBS and other Research Associates with us at the Ubeera Memorial Research Society of Allama Iqbal Medical College, Lahore, Pakistan. We would like to thank Dina Kakar, MD, Andrea Lyman, MD, MSc, MS and Douglas Proops MD, MPH. We are indebted to children, parents and staff at the sampled schools. We acknowledge partial financial support from Allama Iqbal Medical College and technical support from the Punjab Departments of Health and Education, Lahore, Pakistan.

Author information

Authors and Affiliations

  1. Ubeera Memorial Research Society, Allama Iqbal Medical College, Lahore, 54000, Punjab, Pakistan

    Muhammad Umair Mushtaq, Sibgha Gull, Usman Khurshid, Ubeera Shahid & Arif Mahmood Siddiqui

  2. District Health Office Nankana Sahib, Punjab Department of Health, Nankana Sahib, 39100, Punjab, Pakistan

    Muhammad Umair Mushtaq & Mushtaq Ahmad Shad

Authors
  1. Muhammad Umair Mushtaq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sibgha Gull
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Usman Khurshid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ubeera Shahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mushtaq Ahmad Shad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Arif Mahmood Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Umair Mushtaq.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors contributed significantly in all phases of the study in accordance with uniform requirements established by the International Committee of Medical Journal Editors. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Mushtaq, M.U., Gull, S., Khurshid, U. et al. Prevalence and socio-demographic correlates of stunting and thinness among Pakistani primary school children. BMC Public Health 11, 790 (2011). https://doi.org/10.1186/1471-2458-11-790

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2458-11-790

Keywords

  • Primary School Child
  • Lower Parental Education
  • Neighborhood Income Level
  • Illiterate Parent
  • Severe Thinness

Comments

BMC Public Health

ISSN: 1471-2458

Contact us