- Email: [email protected]
Secondary anthropometric data analysis of the National Food Consumption Survey in South Africa: The double burden
Nutrition 21 (2005) 4 –13 www.elsevier.com/locate/nut
Applied nutritional investigation
Secondary anthropometric data analysis of the national food consumption survey in South Africa: The double burden N. P. Steyn, M.P.H., Ph.D.a,*D. Labadarios, M.B., Ch.B., Ph.D.b, E. Maunder, Ph.D.c, J. Neld, C. Lombard, Ph.D.e, Directors of the National Food Consumption Survey† b
a Chronic Diseases of Lifestyle Unit, Tygerberg, South Africa Department of Human Nutrition, University of Stellenbosch and Tygerberg Academic Hospital, Tygerberg, South Africa c Department of Dietetics and Human Nutrition, University of Natal, Pietermaritzburg, South Africa d Department of Logistics, University of Stellenbosch, Stellenbosch, South Africa e Biostatistics Unit, Medical Research Council, Tygerberg, South Africa
Manuscript received April 22, 2004; accepted July 25, 2004.
Abstract
Objective: There is an increase in the prevalence of overweight and obesity in children worldwide, including South Africa. We investigated the prevalences of overweight, obesity, and stunting in a current generation of children (ages 12 to 108 mo), which has a high prevalence of stunting, and evaluated the determinants of both nutritional disorders. Methods: Secondary data analysis of the weight and height measurements of 12- to 108-mo-old children (weighted n ⫽ 2200, non-weighted n ⫽ 2894) during the 1999 National Food Consumption Survey in South Africa is reported. The body mass index reference percentiles recommended for use in children by the International Obesity Task Force were used to determine the prevalence of overweight and obesity, and the National Center for Health Statistics (NCHS) percentiles were used to determine the prevalence of stunting. Results: Nationally, the prevalence of stunting (height-for-age ⱕ ⫺2 standard deviations, NCHS 50th percentile) in these children was 19.3% (95% confidence interval [CI] ⫽ 17.49 to 21.16) and was highest in 1- to 3-y-old children (24.4%) and in children of farm workers on commercial farms (25.6%). The prevalence of combined overweight and obesity (body mass index ⱖ 25 kg/m2 in 17.1%, 95% CI ⫽ 15.00 to 19.23) at the national level was nearly as high as that for stunting. Further, the types of determinants for stunting and overweight were generally similar (although directionally opposite in degree of risk conferred) and included type of housing, type of toilet in the home, fuel used in cooking, presence of a refrigerator or stove, presence of a television in the house, educational level of the caregiver, and maternal education level. An example of the directionally opposite degree of risk is exemplified by the use of paraffin as a fuel being protective against being overweight (odds ratio ⫽ 0.78, 95% CI ⫽ 0.63 to 0.97) but predictive of an increased risk for stunting (odds ratio ⫽ 1.24, 95% CI ⫽ 1.04 to 1.48). Stunting itself conferred an increased risk (odds ratio ⫽ 1.80, 95% CI ⫽ 1.48 to 2.20) of being overweight. Conclusion: Certain defined determinants appear to play important roles in children’s nutritional outcomes in relation to stunting and to overweight and obesity. © 2005 Elsevier Inc. All rights reserved.
Keywords:
Stunting; Obesity; Children; South Africa
Introduction By virtue of its economic growth, South Africa is considered to be one of the countries in sub-Saharan Africa that is
* Corresponding author: Tel.: ⫹27-21-938-0242; fax: ⫹27-21-933-5519. 0899-9007/05/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.nut.2004.09.003
undergoing rapid demographic and nutritional transition [1]. Several studies have documented the dietary and lifestyle † U. MacIntyre, Ph.D., R. Swart, M.Sc., G. Gericke, M.Sc., J. Huskisson, B.Hons, A. Dannhauser, Ph.D., H. H. Vorster, D.Sc., A. E. Nesmvuni, Ph.D., and T. Kotze, Ph.D. E-mail address: [email protected] (N. P. Steyn)
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
changes that have been part of this transition [2– 4]. Such changes have also, in more recent times, been associated with an increase in overweight and obesity in the adult population and an increasing incidence of non-communicable diseases, such as stroke, coronary heart disease, and diabetes mellitus [5]. This scenario has been described in other low- to middleincome countries undergoing rapid urbanization, with its concomitant lifestyle changes [6]. More recently, overweight and obesity have also become common nutritional disorders among children in high- and low-income countries [6 –10]. South Africa has a complex burden of diseases, which differ not only between different age and gender groups but also between ethnic groups and between urban and rural dwellers [11]. For instance, whereas obesity is very common among black women in urban and rural areas, there is paradoxically a high prevalence of stunting among children, particularly among children residing in rural areas [12]. Further, there has been considerable debate regarding the outcome of early linear growth retardation on weight status in adulthood [13–15]. Stunting during early childhood has been associated with excess weight gain in later life [16] and with the development of certain chronic diseases including type 2 diabetes and hypertension [17] In 1998, the South African Demographic and Health Survey determined the prevalence of non-communicable diseases and nutritional status of adults [11]; in 1999, the National Food Consumption Survey (NFCS) was undertaken to determine the nutritional status and its determinants in children [12]. In this report, we focus on the nutritional status of children (ages 12 to 108 mo, i.e., 1 to 8 y) who were examined in the NFCS. The development of age- and gender-specific body mass index (BMI) cutoff points for overweight in children [18] has enabled us to analyze the NFCS data in terms of BMI cutoff values. Because the NFCS database includes data on sociodemographic status, it was also possible to identify certain determinants associated with overweight, obesity, and undernutrition (stunting) in the same group of children.
Materials and methods Subjects The survey population comprised children ages 1 to 8.9 y (12 to 108 mo) in South Africa. The children were selected from the database of the NFCS (n ⫽ 2894), which was undertaken in 1999 and which had been oversampled by 25% for children from low socioeconomic areas, at the directive of the national department of health. All children who had a complete set of anthropometric and sociodemographic data were included in this secondary data analysis. A total of 156 enumerator areas (EAs) were included in the survey, 82 of which were urban and 74 non-urban [12]. The distribution of EAs per province was determined pro-
5
portionately to the distribution of the total population and the urban versus non-urban distribution in each province. An EA was defined as the EA drawn up for the 1996 census [11]. For economic and practical reasons, in formal and informal urban and tribal areas, only EAs with at least 16 qualifying households were considered for inclusion in the sample, whereas on commercial farms only EAs with at least six qualifying households were considered for inclusion. A qualifying household was defined as any household with at least one child between 12 and 108 mo old residing in it. All EAs were randomly selected and the survey had a response rate of 93% [12]. Sample selection and weighting procedures A self-weighting minimum sample size was generated in accordance with the population size in the nine provinces, stratified for age, urban and rural residence, and provincial and national representation. The result is presented in Table 1, column A. The total proposed minimum national sample size was given as 2200. Each EA included approximately 20 households (size of the clusters), and the number of subjects was therefore rounded to be multiples of 20. Another requirement was that there should be at least 50 subjects in each stratum. The results of these adjustments are presented in the column B of Table 1. The total sample size increased to 2440. To accommodate the national department of health’s tender requirement for high-risk populations within the proposed framework of a nationally representative sample, an additional over-sampling (25%) was required. The results of the additional over-sampling are presented in column C of Table 1. The total target sample size was now 3120 children. In the NFCS, 93% of the 3120 subjects’ target was met, which gave a sample of 2894 (with 2570 children having anthropometric measurements and sociodemographic data). It was therefore necessary to adjust the sample, by calculating weights based on these adjustments, to the original intended 2200 subjects. The first step in adjusting the sample was to calculate the base weight, which is the component of the sample weight that accounts for the differential probabilities of selection. This is defined as the inverse of the inclusion probability of the individual in the sample [19]. In this case, the base weights included adjustments for a minimum stratum size of 50 subjects, the requirement that the stratum size had to be a multiple of 20, and over-sampling for high-risk areas. The base weights are presented in column D of Table 1. The next step in calculating the final weights involved poststratification, to adjust the sample weights of the responding subjects so that the totals over various demographic categories matched known population totals [19]. The poststratification cells included age categories, and the known population totals were derived from the 1996 census figures. This adjustment provided for sampling frame inad-
6
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 1 Detailed account of the sampling procedures and weightings used in secondary dietary analysis of the National Food Consumption Survey in South Africa Province
Urban/rural
D
Age (y) groups
E
F
G
H
I
J
KZ
Urban
194
200
260
0.7462
260
320
0.7875
402
400
500
0.804
Rural
15
60
80
0.1875
EC
Urban
127
120
160
0.7938
EC
Rural
214
220
280
0.7643
NP
Urban
28
60
80
NP
Rural
211
220
280
0.7536
WC
Urban
215
220
280
0.7679
WC
Rural
24
60
80
0.3
NW
Urban
62
60
80
0.775
NW
Rural
115
120
160
0.7188
MP
Urban
59
60
80
0.7375
MP
Rural
95
100
120
0.7917
FS
Urban
100
100
120
0.8333
FS
Rural
44
60
80
0.55
NC
Urban
31
60
80
0.3875
NC
Rural
12
60
80
0.15
1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8
0.3662 0.3902 0.2436 0.3610 0.3910 0.2480 0.3793 0.3912 0.2295 0.3660 0.3944 0.2395 0.3471 0.3975 0.2554 0.3481 0.3957 0.2562 0.3546 0.3938 0.2516 0.3494 0.3911 0.2595 0.3651 0.3892 0.2457 0.3859 0.3797 0.2344 0.3581 0.3896 0.2523 0.3625 0.3866 0.2509 0.3644 0.3900 0.2456 0.3636 0.3871 0.2493 0.3463 0.3933 0.2604 0.3435 0.3947 0.2618 0.3576 0.3828 0.2595 0.3848 0.3924 0.2227
0.7325 1.1645 1.3501 0.7775 0.9660 2.1550 0.7820 1.4560 2.5627 0.1445 0.2958 0.5988 0.7111 0.8414 1.1183 0.9429 0.8822 1.0344 0.5516 0.5012 0.8804 0.6195 0.7570 1.3038 0.7072 0.8997 1.1485 0.3087 0.3142 0.3750 0.6938 0.8626 1.0429 0.5147 0.8233 1.6027 1.3436 1.3536 2.0702 0.8857 0.8553 1.4802 0.6660 0.7284 1.8597 0.3686 0.6432 0.8862 0.3261 0.5934 0.5747 0.1283 0.1744 0.8908
2440
3120
97 65 35 117 102 29 195 108 36 38 20 6 62 60 29 79 96 53 18 22 8 119 109 42 111 93 46 30 29 15 32 28 15 81 54 18 16 17 7 39 43 16 52 54 14 41 27 13 34 20 14 36 27 3 2570
0.981694 1.560725 1.809386 0.987317 1.226708 2.736542 0.972661 1.810945 3.187471 0.770603 1.577787 3.193561 0.895846 1.06002 1.408839 1.233679 1.154269 1.353387 1.576124 1.432066 2.515539 0.822124 1.004585 1.730177 0.921007 1.171654 1.495748 1.02909 1.047454 1.250075 0.895279 1.113076 1.345661 0.716131 1.145504 2.229787 1.821769 1.835435 2.807043 1.118756 1.080316 1.869683 0.799208 0.874039 2.231648 0.670228 1.169435 1.611226 0.841449 1.531381 1.48308 0.855211 1.162814 5.938814
2200
95.2 101.4 63.3 115.5 125.1 79.4 189.7 195.6 114.7 29.3 31.6 19.2 55.5 63.6 40.9 97.5 110.8 71.7 28.4 31.5 20.1 97.8 109.5 72.7 102.2 109.0 68.8 30.9 30.4 18.8 28.6 31.2 20.2 58.0 61.9 40.1 29.1 31.2 19.6 43.6 46.5 29.9 41.6 47.2 31.2 27.5 31.6 20.9 28.6 30.6 20.8 30.8 31.4 17.8 3120
71 76 47 91 99 62 153 157 92 5 6 4 44 51 32 74 85 55 10 11 7 74 82 55 78 84 53 9 9 6 22 24 16 42 44 29 21 23 15 34 37 24 35 39 26 15 17 12 11 12 8 5 5 2 2200
KZ
Rural
252
GP
Urban
GP
Total
A
B
C
0.35
A, proposed self-weighting minimum sample size per province; B, sample adjusted to be a multiple of 20 and a minimum sample size of 50; C, additional over-sampling to include high-risk populations; D, base weights (A/C); E, ratio according to 1996 national census (children ages 1 to 8 y); EC, Eastern Cape; F, distribution by age according to design (E ⫻ C); FS, Free State; G, actual surveyed values (realization for anthropometric data); GP, Gauteng; H, realization weight (F/G); I, final weight (H ⫻ D); J, final weighted totals; KZ, Kwazulu-Natal; MP, Mpumalanga; NC, Northern Cape; NP, Northern Province; NW, North West; WC, Western Cape.
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
7
Table 2 Percentage of South African children with height-for-age standard deviation scores (z scores) within different categories of the NCHS standards based on weighted sampling Height-for-age zscore category
Non-weighted* sample size % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 SD to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 SD to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1288)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
288
1019
290
1016
1304
1309
1198
975
440
30.6 25.6 17.67 33.47 25.7 18.43 32.94 44.9 39.84 49.97 2.8 0.73 4.91 1.0 0.00 2.40
16.0 14.5 12.28 16.77 27.1 23.14 31.02 46.3 42.14 50.52 8.7 5.98 11.50 3.3 1.85 4.82
19.3 19.7 13.10 26.24 33.4 28.76 38.3 37.7 32.05 43.46 6.2 3.06 9.29 3.0 1.08 4.94
25.3 23.6 20.52 26.76 28.3 24.94 31.71 40.2 36.67 43.72 5.2 3.70 6.71 2.6 1.33 3.93
26.5 23.8 20.91 26.80 28.0 24.87 31.21 40.7 37.49 43.93 4.9 3.57 6.31 2.4 1.27 3.64
16.7 15.7 13.33 18.02 28.5 25.18 31.81 44.4 40.81 48.01 8.2 5.86 10.47 3.3 2.02 4.50
25.5 24.4 21.95 26.85 27.7 24.62 30.70 37.2 34.44 40.04 6.9 5.22 8.55 3.8 2.55 5.08
20.7 19.7 16.49 22.95 27.6 24.63 30.50 43.0 39.47 46.57 6.4 4.67 8.08 3.3 1.86 4.77
13.0 11.3 8.29 14.27 30.3 25.31 35.39 50.4 44.68 56.15 7.0 3.94 10.14 0.9 0.00 2.01
National† (n ⫽ 2200)
2570 21.6 19.3 17.49 21.16 28.3 25.99 30.58 42.8 40.32 45.20 6.7 5.33 8.12 2.9 2.04 3.76
CI, confidence interval; NCHS, National Center for Health Statistics; SD, standard deviation [21] * Non-weighted sample for comparison. † Weighted sample with complete set of anthropometric and sociodemographic data.
equacies and non-responses. The results of this process are reflected in columns E to J of Table 1. The final totals, calculated separately for each age group, within each stratum (province, urban versus rural) is reflected in the column J of Table 1. Tables 2 to 4 present weighted and nonweighted sample sizes, with the final weighted sample comprising 2200 children. From the results of the sample sizes presented in Tables 2 to 6, it is apparent that some under-representation was present in the original 7- to 8-y-old group. This was most likely due to some, or many, of these children being in school during the survey. It was not always possible to visit the schools and the households within the allotted time frame. With weighting, the sample size of this group increased. Further, the over-sampled high-risk groups, which were found mainly in rural areas and farms, decreased. Despite these adjustments, the weighted and non-weighted findings were similar in most categories, with 1% to 3% differences in 90% of categories. Survey methodology A detailed description of the methodology has been published elsewhere [12]. All anthropometric measurements were made in accordance with recommended techniques [20].
Statistical analysis Data were analyzed with SAS 845 for Windows (SAS Institute, Cary, NC, USA), and National Center for Health Statistics (NCHS) percentiles was used to classify children into categories of nutritional status [21]. This was achieved by converting the data ⫺2 standard deviations (SD) or greater than 2 SD of the NCHS 50th percentiles of heightfor-age (HAZ), weight-for-age (WAZ), and weight-forheight (WHZ) z scores. BMI cutoff points, as recommended by the International Obesity Task Force, for overweight and obesity by gender between ages 2 and 18 y, defined to pass through BMI between 25 and 30 kg/m2 at age 18 y, obtained by averaging data from Brazil, Great Britain, Hong Kong, The Netherlands, Singapore, and United States were used to determine the prevalence of overweight and obesity in the sample [18]. The data were also analyzed by age group, by urban versus rural domain, and by area of residence (farms, tribal, formal urban, and informal urban). Multivariate analyses were conducted with sociodemographic variables to predict determinants of overweight, obesity, and stunting in the sample. The chi-square test was used to test for associations between outcome (stunting and overweight) and area of residence, urban versus rural status, and age. Odds ratios (ORs) were calculated to determine the odds of the proba-
8
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 3 Percentage of South African children with weight-for-age standard deviation scores (z scores) lying within different categories of the NCHS standards based on weighted sampling Weight-for-age z-score category
Weighted sample size† Non-weighted sample size* % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms
Formal urban
Informal urban
Tribal
Rural
Urban
1–3 y
4–6 y
7–8 y
108 288 18.1 13.7 8.93 18.51 30.8 25.58 36.07 49.2 41.71 56.72 3.3 1.16 5.47 2.9 0.10 5.77
946 1019 7.8 6.9 5.23 8.51 24.3 19.87 28.71 53.0 49.57 56.50 10.7 8.14 13.22 5.1 2.70 7.54
272 290 7.6 8.0 4.13 11.95 26.9 21.10 32.62 56.1 50.65 61.55 4.6 1.77 7.52 4.3 2.63 6.08
874 1016 11.3 10.6 8.32 12.86 29.6 26.29 32.93 54.1 50.83 57.45 3.6 2.45 4.67 2.1 1.20 3.02
982 1304 12.8 10.9 8.85 13.01 29.7 26.72 32.77 53.6 50.52 56.67 3.5 2.51 4.55 2.2 1.34 3.05
1218 1309 7.7 7.1 5.57 8.69 24.9 21.15 28.58 53.7 50.72 56.72 9.3 7.11 11.55 4.9 3.01 6.89
795 1198 12.4 11.4 9.50 13.33 26.6 23.93 29.21 49.4 46.50 52.40 8.0 6.14 9.86 4.6 3.24 5.92
861 975 8.8 8.0 6.16 9.84 26.6 22.96 30.21 55.5 52.19 58.89 6.7 4.87 8.45 3.2 1.73 4.70
544 440 7.7 6.4 3.99 8.73 28.5 23.77 33.15 56.9 51.83 61.92 5.0 2.79 7.28 3.3 0.86 5.67
National†
2200 2570 10.3 8.8 7.57 10.09 27.0 24.61 29.48 53.7 51.53 55.80 6.7 5.45 8.03 3.7 2.59 4.85
CI, confidence interval; NCHS, National Center for Health Statistics [21]; SD, standard deviation * Non-weighted sample for comparison. † Weighted total sample with complete set of anthropometric and sociodemographic data.
bility of the outcome (stunting and overweight) measured against exposure variables such as level of education and head of household. The Mantel-Haenzel test was used to test for associations between exposure variables and outcome (stunting and overweight) after adjusting for the effect of the confounders’ age, gender, and location (urban versus rural). Bonferroni’s test was used to show differences between pairs of means and their P values, and the F test of model effects used weighted data and took stratified data into consideration. Confidence intervals (CIs) were calculated by taking the complex sampling design into account [20]. The PROC SURVEY MEANS, which is part of the SAS program, was used to calculate confidence intervals. This takes into consideration the stratified nature of the data, where 18 strata were identified, with nine provinces, urban and rural, within each province. EAs within each stratum are seen as clusters. The data in this report are presented for the weighted sample, and non-weighted data are sometimes presented for comparison.
Results Stunting, a reflection of long-term undernutrition (ⱕ⫺2 SD HAZ), was, at the national level, prevalent in 19.3% of the children, with the highest prevalence in those 1 to 3 y
old (24.4%), in rural areas (23.8%), and particularly on commercial farms (25.6%; Table 2). Compared with the original published survey data [12], the largest difference in the prevalence of stunting was among children who lived on commercial farms (30.6% of non-weighted versus 25.6% of the weighted data), areas that had originally been oversampled. Low WAZ values, a reflection of underweight (ⱕ⫺2 SD WAZ), was, at the national level, prevalent in 8.8% of children, with the highest prevalence in those 1 to 3 y old (11.4%), in rural areas (10.9%), and particularly among those living on commercial farms (13.7%; Table 3). The effects of the original over-sampling were noted in the commercial farm areas, with smaller differences in the other categories. At the other end of the spectrum, 6.7% of children nationally were classified as being overweight (1 to 2 SD WAZ) and 3.7% as obese (ⱖ⫹2 SD WAZ). Overweight and obesity together (ⱖ⫹1 SD WAZ) was 10.4%, with the highest prevalence in children living in formal urban areas (Table 3). In terms of WHZ values, at the national level 3.3% of children could be regarded as wasted (ⱕ⫺2 SD WHZ), whereas 12.4% were overweight (1 to 2 SD WHZ) and 6.6% were obese (ⱖ2 SD WHZ; Table 4). Overall, the prevalence of overweight and obesity (BMI ⱖ 1 SD WHZ) was 19%. Urban areas had the highest rates (20.7%). The mean BMI values of the children by age and area of
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
9
Table 4 Percentage of South African children with weight-for-height standard deviation scores (z scores) within different categories of the NCHS standards based on weighted sampling Weight-for height z-score category
Non-weighted sample size* % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1218)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
288
1019
290
1016
1304
1309
1198
975
440
4.3 3.06 0.72 5.40 18.67 12.29 25.05 66.22 61.14 71.31 8.49 4.49 12.48 3.56 0.44 6.67
2.6 2.12 1.22 3.02 13.71 10.83 16.58 61.29 57.67 64.92 14.21 11.23 17.19 8.67 6.25 11.09
2.1 1.99 0.10 3.88 11.61 8.58 14.65 73.16 67.52 78.80 7.71 4.81 10.61 5.53 3.13 7.93
5.1 5.08 3.40 6.76 14.70 12.45 16.94 62.80 59.45 66.15 12.42 10.46 14.38 5.00 3.52 6.47
4.9 4.86 3.33 6.39 15.13 13.01 17.25 63.18 60.08 66.27 11.99 10.15 13.83 4.84 3.54 6.13
2.4 2.09 1.28 2.90 13.24 10.94 15.53 63.95 60.61 67.29 12.75 10.20 15.31 7.97 5.93 10.01
4.0 3.44 2.42 4.47 13.83 11.80 15.85 61.98 58.92 65.03 13.37 11.20 15.54 7.39 5.87 8.90
3.4 3.30 2.03 4.57 13.47 11.11 15.82 63.96 61.05 66.87 13.39 11.24 15.54 5.88 4.13 7.63
3.4 3.20 1.38 5.03 15.43 11.47 19.39 65.43 60.29 70.56 9.47 6.21 12.72 6.47 3.33 9.62
National† (n ⫽ 2200)
2570 3.7 3.3 2.52 4.14 14.1 12.51 15.65 63.6 61.32 65.89 12.4 10.80 14.03 6.6 5.32 7.82
CI, confidence interval; NCHS, National Center for Health Statistics [21]; SD, standard deviation * Non-weighted sample for comparison. † Weighted sample with complete set of anthropometric and sociodemographic data.
residence (Table 5) were significantly different between areas of residence (formal urban and tribal, P ⫽ 0.041), between urban and rural areas (P ⫽ 0.005), and between age groups (P ⬍ 0.001). Nearly 20% of children (17.1%) had a BMI of at least 25 kg/m2 (combined overweight and obe-
sity; Table 6). The groups with the highest prevalence of overweight were children living in urban areas (18.6%) and those ages 1 to 3 y (23.8%). Of interest, only 13.4% children living in informal urban areas were overweight compared with 15.8% of children in tribal areas. Of further interest
Table 5 Descriptive statistics on BMI of South African children based on the weighted National Food Consumption Survey sample Statistics
Domain analysis by area of residence Farms (n ⫽ 108)
Mean BMI Variance of the mean Median Lower 95% CI Upper 95% CI Minimum Maximum Tests of significance†
Formal urban (n ⫽ 946)
15.9 16.3 0.03 0.01 15.7 15.9 15.57 16.06 16.29 16.52 10.7 11.1 25.0 29.7 P ⫽ 0.0409‡
Domain analysis by urban/rural
Domain analysis by age group 1–3 y (n ⫽ 975)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1218)
16.2 0.01 15.8 15.94 16.38 12.0 27.4
15.9 0.01 15.7 15.77 16.08 10.4 29.4
15.9 16.3 0.01 0.01 15.7 15.9 15.78 16.07 16.06 16.45 10.4 11.1 29.4 29.7 P ⫽ 0.0052§
4–6 y (n ⫽ 861)
16.8 15.7 0.01 0.01 16.5 15.5 16.63 15.54 16.91 15.86 11.1 10.4 27.4 23.9 P ⱕ 0.0001储
National* (n ⫽ 2200)
7–8 y (n ⫽ 544) 15.8 0.02 15.4 15.54 16.06 11.6 29.7
16.1 0.01 15.8 15.99 16.23 10.4 29.7
BMI, body mass index; CI, confidence interval; SD, standard deviation * Weighted samples used Korn and Graubard, 2002 [19]. † Two methods were used to establish significant differences: Bonferroni’s test (weighted data without stratification of data) and regression model (F test of model effects; weighted data with stratification of data). ‡ F test of model effects: significant difference between formal urban and other areas. § Bonferroni’s test: significant difference between urban and rural areas. 储 Bonferroni’s test: significant difference between children ages 1 to 3 y and those ages 4 to 6 y and between children ages 1 and 3 y and those ages 7 to 8 y.
10
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 6 Percentage South African children with BMI values according to BMI standards储 for 1- to 8 y-old children (based on the weighted sample) BMI cutoff points
% ⬍25 Lower 95% CI Upper 95% CI 25–30 Lower 95% CI Upper 95% CI % ⱖ30 Lower 95% CI Upper 95% CI % ⱖ25§ Lower 95% CI Upper 95% CI Chi-square* Chi-square†
Domain analysis by area of residence*
Domain analysis by urban/rural*
Domain analysis by age group*
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 128)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
89.2 84.50 93.97 7.2 3.17 11.29 3.5 0.77 6.30 10.8 6.03 15.50 0.0066 0.0054
79.9 75.80 83.99 13.9 10.97 16.88 6.2 4.40 7.96 20.1 16.01 24.19
86.6 83.20 89.98 7.5 5.04 10.00 5.9 3.15 8.63 13.4 10.02 16.80
84.2 81.86 86.48 12.1 10.06 14.12 3.7 2.55 4.93 15.8 13.52 18.14
84.7 82.60 86.85 11.6 9.63 13.49 3.7 2.64 4.79 15.3 13.15 17.40 0.0257 0.0392
81.4 77.94 84.85 12.5 9.97 15.01 6.1 4.55 7.67 18.6 15.15 22.06
76.2 73.38 79.13 16.0 13.70 18.23 7.8 6.07 9.49 23.7 20.87 26.62 ⬍0.0001 ⬍0.0001
84.2 81.25 87.16 12.0 9.59 14.37 3.8 2.50 5.12 15.8 12.84 18.75
90.5 87.31 93.63 6.5 4.22 8.89 3.0 1.13 4.83 9.5 6.37 12.69
National (n ⫽ 2200)
82.9 80.77 84.99 12.1 10.45 13.70 5.0 4.07 6.02 17.1 15.00 19.23
BMI, body mass index; CI, confidence interval; SD, standard deviation * Chi-square P value for testing for associations, using weighted values [19], between BMI groupings, area of residence, urban versus rural, and age groups † Chi-square P value for testing for associations, using non-weighted values, between BMI groupings, area of residence, urban versus rural, and age groups § Refers to overweight and obesity combined. 储 Reference 18; international cutoff points for body mass index for overweight and obesity by gender between 2 and 18 y, defined to pass through BMI of 25 and 30 kg/m2 at age 18, obtained by averaging data from Brazil, Great Britain, Hong Kong, Netherlands, Singapore, and United States.
(Tables 4 and 6) is the comparative similarity in the percentages for overweight children as defined by WHZ and BMI criteria, respectively. The prevalence of combined overweight and obesity as determined by BMI [18] were lower but similar when compared with those determined by NCHS standards for WHZ [21] (Table 6). The variables (OR with 95% CI; adjusted for age, gender, and urban versus rural residence; Table 7) associated with having a BMI greater than 25 kg/m2 (overweight) or a HAZ value less than ⫺2 SD (stunted) were as follows. Factors found to be protective against stunting were having a married mother (OR ⫽ 0.84, CI ⫽ 0.70 to 0.99), having a father as the head of the household (OR ⫽ 0.83, CI ⫽ 0.69 to 0.99), living in a formal house (OR ⫽ 0.78, CI ⫽ 0.66 to 0.93), having a flush toilet (OR ⫽ 0.78, CI ⫽ 0.62 to 0.99), and having a television in the house (OR ⫽ 0.62, CI ⫽ 0.52 to 0.74). Factors that contributed to an increased risk of becoming stunted were a grandmother being the head of the household (OR ⫽ 1.23, CI ⫽ 1.03 to 1.48), the mother and/or caregiver having only primary school education (OR ⫽ 1.31;CI ⫽ 1.10 to 1.56), an informal house as a dwelling (shack; OR ⫽ 1.29, CI ⫽ 1.02 to 1.66), fewer than three rooms in the house (OR ⫽ 1.32, CI ⫽ 1.11 to 1.56), using paraffin for cooking (OR ⫽ 1.24, CI ⫽ 1.04 to 1.48), and absence of a refrigerator in the house (OR ⫽ 1.42, CI ⫽ 1.19 to 1.69). With regard to being overweight (and obese combined; BMI ⱖ 25 kg/m2), the following factors were associated with an increased risk of being overweight: residing in a traditional or mud house (OR ⫽ 1.44, CI ⫽ 1.09 to 1.89),
having a flush toilet (OR ⫽ 1.78, CI ⫽ 1.31 to 2.41), and having a HAZ value less than ⫺2 SD (stunted; OR ⫽ 1.80, CI ⫽ 1.48 to 2.20). Factors that contributed to a decreased risk of being overweight were having an outside drop toilet (OR ⫽ 0.57, CI ⫽ 0.34 to 0.95), using paraffin for cooking (OR 0.78, CI ⫽ 0.63 to 0.97), having no refrigerator (OR ⫽ 0.68, CI ⫽ 0.50 to 0.84), having no stove in the house (OR ⫽ 0.79, CI ⫽ 0.64 to 0.98), a mother having only a primary education (OR ⫽ 0.76, CI ⫽ 0.61 to 0.95), and a WAZ score less than ⫺2 SD (underweight; OR ⫽ 0.16, CI ⫽ 0.07 to 0.35).
Discussion Over the past two decades, there have been many studies that have probed for determinants of undernutrition in South African children [12,22–24]. These studies have been particularly important because 31% of underweight children in a rural area of Limpopo Province (previously known as the Northern Province) were found to have an overweight (BMI ⱖ 25 kg/m2) mother or caregiver [25]. Similarly, nearly 50% of mothers and/or caregivers of stunted and underweight children in a rural area of North West Province were found to be overweight [22]. This phenomenon has been found in other developing countries, including China, Russia, and Brazil [14]. Clearly, similar circumstances or factors, whether environmental, behavioral, or individual, favor the development of underweight and overweight in the same household [14 –26].
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
11
Table 7 Odds ratios of children in the weighted sample with BMI values ⱖ25 and less than minus 2 standard deviations (z scores) for height-for-age (adjusted for age and gender and urban/rural residence)
1. Mother is married 2. Person responsible for food preparation: Mother Grandmother 3. Decides on foods purchased: Mother Grandmother 4. Responsible for feeding child: Mother Grandmother 5. Head of household: Father Mother Grandmother 6. Type of house: Brick/cement Mud/traditional Tin/plank/wood 7. ⬎6 people living in house 8. ⬍3 rooms in house 9. Source of water: Own tap Communal tap 10. Type of toilet: Flush Bucket/pot 11. Fuel for cooking is paraffin 12. No refrigerator in house 13. No stove in house 14. Television in the house 15. Mother has a primary education 16. Mother is unemployed 17. Education level of caregiver (primary or none) 18. Father is unemployed 19. HAZ ⬍ ⫺2 SD 20. WAZ ⬍ ⫺2 SD
BMI ⱖ 25 kg/m2 (overweight)
HAZ ⱕ ⫺2SD (stunted)
OR
CI
OR
CI
0.92
0.77–1.11
0.84*
0.70–0.99
1.01 1.01
0.83–1.22 0.81–1.26
0.98 0.95
0.82–1.17 0.77–1.17
1.02 1.03
0.85–1.23 0.84–1.26
0.87 1.01
0.74–1.04 0.84–1.22
1.02 1.14
0.84–1.23 0.91–1.42
0.92 1.02
0.78–1.10 0.83–1.26
0.90 0.78 1.19
0.75–1.09 0.56–1.08 0.97–1.45
0.83* 0.84 1.23*
0.69–0.99 0.62–1.13 1.03–1.48
0.97 1.44* 0.68* 0.92 1.10
0.79–1.19 1.09–1.89 0.50–0.91 0.76–1.10 0.91–1.33
0.78* 1.15 1.29* 0.95 1.32*
0.66–0.93 0.93–1.43 1.01–1.65 0.80–1.12 1.11–1.56
1.15 0.78*
0.89–1.47 0.61–0.99
0.96 1.08
0.78–1.18 0.89–1.30
1.78* 0.57* 0.78*
1.31–2.41 0.34–0.95 0.63–0.97
0.78* 1.29 1.24*
0.62–0.99 0.90–1.86 1.04–1.48
0.68* 0.79* 1.17 0.76*
0.55–0.84 0.64–0.98 0.95–1.43 0.61–0.95
1.42* 1.10 0.62* 1.31*
1.19–1.69 0.92–1.32 0.52–0.74 1.10–1.56
1.04 0.75*
0.86–1.25 0.59–0.95
1.15 1.25*
0.97–1.36 1.04–1.50
1.01 1.80* 0.16*
0.78–1.31 1.48–2.20 0.07–0.35
1.02
0.82–1.27
BMI, body mass index; CI, 95% confidence interval; HAZ, height-for-age z score; OR, odds ratio; WAZ, weight-for-age z score. * Mantel-Haenszel method.
Tladinyane [22] compared childcare practices in mothers or caregivers of stunted and underweight children with those of well-nourished children ages 1 to 4 y in a village of North West Province of South Africa. It was found that poor hygiene practices and the mother’s level of education were the major determinants for development of undernutrition. A similar case-control study was undertaken in stunted versus non-stunted children in an urban area of Gauteng [23]. A positive association was found between stunting and certain childcare practices, including the unfair allocation of household food to its younger members and non-participation of fathers in child care. The NFCS (non-weighted
sample) examined risk factors for underweight and stunting in children ages 1 to 9 y [12]. The most influential determinants were low education levels of the mother or caregiver and economically related indicators such as having an informal housing structure and few rooms in the house, poor or inadequate water and sanitation, low income and unemployment, and no stove or refrigerator in the dwelling. Similar trends were found in the weighted sample. Most of these determinants are associated with poverty. Additional important findings from the NFCS are that the highest prevalences of stunting and underweight were found in rural areas and particularly among children ages 1 to 3 y.
12
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
In contrast, the highest prevalence of overweight was found in children ages 1 to 3 y living in urban areas. Clearly, children ages 1 to 3 y are most vulnerable to undernutrition and overnutrition. If stunting takes place at this age, it is postulated that these children may be at greater risk for being overweight [15,26]. The diet, which may be low in nutrient density, may lead to this outcome. There is a paucity of information on determinants of overweight and obesity in children in South Africa. In adults, particularly in women [27,28], determinants associated with an increased risk for overweight include black ethnicity, education level below grade 7, inactivity, and at least one overweight parent. In these same women, age has been positively associated with BMI and with waist circumference [27,28]. Because obesity affects nearly 30% of black urban women [11], it is clearly important to identify risk factors that might be environmentally controlled or manipulated by means of interventions for the prevention or treatment of overweight and obesity. In developing countries other than South Africa, such determinants of overweight among adults vary greatly across countries, races, and cultural groups. In Bahrain, for instance, marriage, a large family, and ownership of a car have been positively associated with obesity, whereas level of education was not in either gender [29]. A study undertaken in Egypt reported that obesity among female university students was associated with obesity during childhood, obesity in one or both parents, limited physical activity, food intake between meals, and long afternoon napping [30]. Additional studies have identified risk factors for obesity, which include older age, being married, parental obesity, low level of education, increased number of meals eaten, eating between meals, high family income, large number of servants, decreased number of people living at home, long periods of watching television, and inactivity in the workplace [31–33]. Since the early 1990s, there have been several studies that have shown an association between early stunting and overweight at a later stage, particularly in those countries undergoing the nutritional transition [34 –37]. Because the health and economic consequences of obesity are so debilitating and prevalent in the South African population, it is important to determine whether undernutrition in young children may in the long term lead to overweight or obese older children and adults. This is of national importance because the current focus and budget of health services addresses primarily the prevention and treatment of undernutrition rather than a predisposition to overweight and obesity. Although the mechanism by which stunting may predispose to overweight or obesity remains largely undefined, it has been postulated that nutritional stunting is associated with impaired fat oxidation [38]. Another postulate is that nutritionally stunted children may have an impaired regulation of energy intake, which increases the risk for obesity [39]. Further, data from Brazil appear to indicate that mild
stunting might be associated with a greater susceptibility to the effects of high-fat diets [40]. In this regard, excess weight gain among lower-income groups is believed to be the result of a change from a traditional “healthy” diet to a high-fat diet in addition to decreased levels of physical activity [14,41]. Whatever the reason, there appears to be a coexistence of stunting and overweight in low-income populations undergoing the nutritional transition. Martorell et al. [42] measured levels of overweight and obesity in preschool children from developing countries to determine trends in weight status. They found that overweight was more common in urban areas and particularly among girls. Moreover, gross national product was related negatively to stunting and positively to overweight. In countries where maternal and child malnutrition exist alongside rapid economic development, abdominal obesity and associated chronic diseases seem likely to increase [43]. A greater central fat distribution has also been found in children stunted early in childhood [14,44]. In the present study, we examined the NFCS data to identify determinants of overweight and stunting in children. With few exceptions, the types of determinants for these two nutritional disorders appear to be the same but directionally opposite in affording risk for a given nutritional disorder. For example, mothers or caregivers who have a lower level of education conferred a significantly lower risk for overweight but a significantly higher risk for stunting in their children. Conversely, having a flush toilet in the house conferred a high risk of overweight and a low risk of stunting, probably due to an association with economic status. The most important and relevant predictor of overweight is having a low HAZ score (ⱕ⫺2 SD). Stunted children have nearly twice the risk of being overweight (BMI ⱖ 25 kg/m2). Conversely, having a WAZ score no greater than ⫺2 SD confers a significantly lower risk for overweight. These associations have aptly been described: “If under- and overweight can occur in the same household, common underlying causes of both conditions may be identified” [14, p. 2595]. When examining the type of determinants of stunting and overweight in the context of poverty and rapid nutritional transition, it becomes decidedly important that neither nutritional disorder be treated in isolation but should, by necessity, be afforded equal priority in terms of health resources. The cumulative effect of the double burden of apparently related nutritional disorders in the developing world needs to be evaluated to develop satisfactory interventions for their prevention and management. References [1] Bourne LT, Lambert EV, Steyn K. Where does the black population of South Africa stand on the nutrition transition? Public Health Nutr 2001;5(1A):157– 62. [2] Bourne LT, Langenhoven ML, Steyn K, Jooste PL, Nesamvuni AE, Laubscher JA. The food and meal pattern in the urban African
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
[3]
[4]
[5]
[6]
[7] [8] [9] [10]
[11] [12]
[13]
[14]
[15]
[16]
[17]
[18]
[19] [20] [21]
[22]
[23]
population of the Cape Peninsula, South Africa: the BRISK Study. Cent Afr J Med 1994;40:140 – 8. Steyn NP, Senekal M, Brits S, Alberts M, Mashego T, Nel JH. Weight and health status of black female students. S Afr Med J 2000;90:146 –52. Steyn NP, Senekal M, Brits S, Nel JH. Urban and rural differences in dietary intake, weight status and nutrition knowledge of black female students. Asia Pac J Clin Nutr 2000;9:53–9. Fourie J, Steyn K. Review of research and identification of essential health research priorities. Chronic diseases of lifestyle in South Africa. MRC technical report. Tygerberg, Cape Town: South African Medical Research Council; 1995. Obesity. Preventing and managing the global epidemic. Report of a WHO consultation. WHO technical report series 894. Geneva: World Health Organization; 2000. Dietz WH. The obesity epidemic in young children. BMJ 2001;322: 313– 4. Childhood obesity: an emerging public-health problem. Lancet 2001; 357:1989. Yanovski JA, Yanovski SZ. Treatment of pediatric and adolescent obesity. JAMA 2003;289:1851–3. Rudolf MCJ, Sahota P, Barth JH, Walker J. Increasing prevalence of obesity in primary school children: cohort study. BMJ 2001;322: 1094 –5. Statistics South Africa. Stats in brief 2000. Pretoria: Department of Statistics; 2000. Labadarios D (editor), Steyn NP, Maunder E, MacIntyre U, Swart R, Gericke G, Huskisson J, Dannhauser A, Vorster HH, Nesmvuni AE. The National Food Consumption Survey (NFCS): children aged 1–9 years, South Africa, 1999. Pretoria: Department of Health; 2000. Available at: www.sahealthinfo.org/nutrition module/nfcs/chapter 2 methodology. Accessed June 21, 2004. Hoffman DJ, Sawaya AL, Verreschi I, Tucker KL, Roberts SB. Why are nutritionally stunted children at increased risk of obesity? Studies of metabolic rate and fat oxidation in shantytown children from São Paulo, Brazil. Am J Clin Nutr 2000;72:702–7. Doak C, Adair LS, Monteiro C, Popkin BM. Overweight and underweight coexist within households in Brazil, China and Russia. J Nutr 2000;130:2965–71. Popkin BM, Richards MK, Montiero CA. Stunting is associated with overweight in children of four nations that are undergoing the nutrition transition. J Nutr 1996;26:3009 –16. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med 1993;22:167–77. Diet, nutrition and the prevention of chronic diseases. Report of a Joint WHO/FAO Expert Consultation. WHO technical report series 916. Geneva: World Health Organization; 2003. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–3. Korn EL, Graubard BI. Analysis of health surveys. New York: John Wiley & Sons; 2002. Lee RD, Nieman DC. Nutritional assessment. Oxford: Brown and Benchmark; 1993. National Center for Health Statistics. NCHS growth curves for children. Birth to 18 years. DHEW publication PHS 78-1650. Hyattsville, MD: US Department of Health, Education and Welfare; 1977. Tladinyane P. The difference in factors influencing childcare between others/caregivers of undernourished and well nourished children aged 1– 4 years in Oukasie (Brits), North West Provence (masters thesis). Pretoria: MEDUNSA; 2003. Agboka JA. Quality of care practices of mothers/caregivers and stunting of children between the ages of 12 and 24 months in Gauteng (masters dissertation). Pretoria: Department of Food and Hospitality
[24]
[25]
[26] [27]
[28]
[29] [30] [31]
[32] [33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41] [42]
[43] [44]
13
Management, Faculty of Natural Sciences, Technickon Pretoria; 2001. Amissah A. Inadequate dietary intakes as a cause stunting among young children in rural South Africa (masters dissertation). Pretoria: Department of Food and Hospitality Management, Faculty of Natural Sciences, Technickon Pretoria; 2001. Steyn NP, Nel JH, Tichelaar HY, Prinsloo JF, Dhansay MA, Oelofse A, Benade AJS. Malnutrition in Pedi preschool children, their siblings and caretakers. S Afr J Clin Nutr 1994;7:12– 8. Popkin BM. The nutrition transition and obesity in the developing world. J Nutr 2001;131:871S–3. Senekal M, Steyn NP, Mashego T-AB, Nel JH. Evaluation of body shape, eating disorders and weight management related parameters in black female students of rural and urban origins. S Afr J Psychol 2001;31:45–53. Senekal M, Steyn NP, Nel JH. Factors associated with overweight/ obesity in economically active South African populations. Ethn Dis 2003;13:109 –16. al-Mannai A, Dickerson JW, Morgan JB, Khalfan H. Obesity in Bahraini adults. J R Soc Health 1996;116:30 – 40. Amine EK, Samy M. Obesity among female university students in the United Arab Emirates. J R Soc Health 1996;116:91– 6. Musaiger AO, Radwan HM. Social and dietary factors associated with obesity in university female students in United Arab Emirates. J R Soc Health 1995;115:96 –9. Al-Isa AN. Dietary and socio-economic factors associated with obesity among Kuwaiti college men. Br J Nutr 1999;82:369 –74. Vioque J, Torres A, Quiles J. Time spent watching television, sleep duration and obesity in adults living in Valencia, Spain. Int J Obes 2000;24:1683– 8. Walker SP, Gaskin PS, Powell CA, Bennett FI. The effects of birth weight and postnatal linear growth retardation on body mass index, fatness and fat distribution in mid and late childhood. Public Health Nutr 2002;5:391– 6. Sawaya AL, Dallal G, Solymos G, et al. Obesity and malnutrition in a Shantytown population in the city of Sao Paulo, Brazil. Obes Res 1995;3(suppl 2):107S–15. Florencio TM, Ferreira HS, da Franca AP, Cavalcante JC, Sawaya AL. Obesity and undernutrition in a very-low-income population in the city of Maceio, northeastern Brazil. Br J Nutr 2001;86:277– 84. Walker SP, Gaskin PS, Powell CA, Bennett FI, Forrester TE, Grantham-McGregor S. The effects of birth weight and postnatal linear growth retardation on blood pressure at age 11–12 years. J Epidemiol Community Health 2001;55:394 – 8. Hoffman DJ, Sawaya AL, Verreschi I, Tucker KL, Poberts SB. Why are nutritionally stunted children at increased risk of obesity? Studies of metabolic rate and fat oxidation in shantytown children from Sao Paulo, Brazil. Am J Clin Nutr 2000;72:702–7. Hoffman DJ, Roberts SB, Verreschi I, Martins PA, de Nascimento C, Tucker KL, Sawaya AL. Regulation of energy intake may be impaired in nutritionally stunted children from the shantytowns of Sao Paulo, Brazil. J Nutr 2000;130:2265–70. Sawaya AL, Grillo LP, Verreschi I, da Silva AC, Roberts SB. Mild stunting is associated with higher susceptibility to the effects of high fat diets: studies in a shantytown population in Sao Paulo, Brazil. J Nutr 1998;128(suppl 2):415S–20. Kain J, Vio F, Albala C. Obesity trends and determinant factors in Latin America. Cad Saude Publica 2003;19(suppl 1):77S– 86. Martorell R, Kettel KL, Hughes ML, Grummer-Strawn LM. Overweight and obesity in preschool children from developing countries. Int J Obes Relat Metab Disord 2000;24:959 – 67. James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res 2001;9(suppl 4):228S–33. Schroeder DG, Martorell R, Flores R. Infant and child growth and fatness and fat distribution in Guatemalan adults. Am J Epidemiol 1999;149:177– 85.
Applied nutritional investigation
Secondary anthropometric data analysis of the national food consumption survey in South Africa: The double burden N. P. Steyn, M.P.H., Ph.D.a,*D. Labadarios, M.B., Ch.B., Ph.D.b, E. Maunder, Ph.D.c, J. Neld, C. Lombard, Ph.D.e, Directors of the National Food Consumption Survey† b
a Chronic Diseases of Lifestyle Unit, Tygerberg, South Africa Department of Human Nutrition, University of Stellenbosch and Tygerberg Academic Hospital, Tygerberg, South Africa c Department of Dietetics and Human Nutrition, University of Natal, Pietermaritzburg, South Africa d Department of Logistics, University of Stellenbosch, Stellenbosch, South Africa e Biostatistics Unit, Medical Research Council, Tygerberg, South Africa
Manuscript received April 22, 2004; accepted July 25, 2004.
Abstract
Objective: There is an increase in the prevalence of overweight and obesity in children worldwide, including South Africa. We investigated the prevalences of overweight, obesity, and stunting in a current generation of children (ages 12 to 108 mo), which has a high prevalence of stunting, and evaluated the determinants of both nutritional disorders. Methods: Secondary data analysis of the weight and height measurements of 12- to 108-mo-old children (weighted n ⫽ 2200, non-weighted n ⫽ 2894) during the 1999 National Food Consumption Survey in South Africa is reported. The body mass index reference percentiles recommended for use in children by the International Obesity Task Force were used to determine the prevalence of overweight and obesity, and the National Center for Health Statistics (NCHS) percentiles were used to determine the prevalence of stunting. Results: Nationally, the prevalence of stunting (height-for-age ⱕ ⫺2 standard deviations, NCHS 50th percentile) in these children was 19.3% (95% confidence interval [CI] ⫽ 17.49 to 21.16) and was highest in 1- to 3-y-old children (24.4%) and in children of farm workers on commercial farms (25.6%). The prevalence of combined overweight and obesity (body mass index ⱖ 25 kg/m2 in 17.1%, 95% CI ⫽ 15.00 to 19.23) at the national level was nearly as high as that for stunting. Further, the types of determinants for stunting and overweight were generally similar (although directionally opposite in degree of risk conferred) and included type of housing, type of toilet in the home, fuel used in cooking, presence of a refrigerator or stove, presence of a television in the house, educational level of the caregiver, and maternal education level. An example of the directionally opposite degree of risk is exemplified by the use of paraffin as a fuel being protective against being overweight (odds ratio ⫽ 0.78, 95% CI ⫽ 0.63 to 0.97) but predictive of an increased risk for stunting (odds ratio ⫽ 1.24, 95% CI ⫽ 1.04 to 1.48). Stunting itself conferred an increased risk (odds ratio ⫽ 1.80, 95% CI ⫽ 1.48 to 2.20) of being overweight. Conclusion: Certain defined determinants appear to play important roles in children’s nutritional outcomes in relation to stunting and to overweight and obesity. © 2005 Elsevier Inc. All rights reserved.
Keywords:
Stunting; Obesity; Children; South Africa
Introduction By virtue of its economic growth, South Africa is considered to be one of the countries in sub-Saharan Africa that is
* Corresponding author: Tel.: ⫹27-21-938-0242; fax: ⫹27-21-933-5519. 0899-9007/05/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.nut.2004.09.003
undergoing rapid demographic and nutritional transition [1]. Several studies have documented the dietary and lifestyle † U. MacIntyre, Ph.D., R. Swart, M.Sc., G. Gericke, M.Sc., J. Huskisson, B.Hons, A. Dannhauser, Ph.D., H. H. Vorster, D.Sc., A. E. Nesmvuni, Ph.D., and T. Kotze, Ph.D. E-mail address: [email protected] (N. P. Steyn)
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
changes that have been part of this transition [2– 4]. Such changes have also, in more recent times, been associated with an increase in overweight and obesity in the adult population and an increasing incidence of non-communicable diseases, such as stroke, coronary heart disease, and diabetes mellitus [5]. This scenario has been described in other low- to middleincome countries undergoing rapid urbanization, with its concomitant lifestyle changes [6]. More recently, overweight and obesity have also become common nutritional disorders among children in high- and low-income countries [6 –10]. South Africa has a complex burden of diseases, which differ not only between different age and gender groups but also between ethnic groups and between urban and rural dwellers [11]. For instance, whereas obesity is very common among black women in urban and rural areas, there is paradoxically a high prevalence of stunting among children, particularly among children residing in rural areas [12]. Further, there has been considerable debate regarding the outcome of early linear growth retardation on weight status in adulthood [13–15]. Stunting during early childhood has been associated with excess weight gain in later life [16] and with the development of certain chronic diseases including type 2 diabetes and hypertension [17] In 1998, the South African Demographic and Health Survey determined the prevalence of non-communicable diseases and nutritional status of adults [11]; in 1999, the National Food Consumption Survey (NFCS) was undertaken to determine the nutritional status and its determinants in children [12]. In this report, we focus on the nutritional status of children (ages 12 to 108 mo, i.e., 1 to 8 y) who were examined in the NFCS. The development of age- and gender-specific body mass index (BMI) cutoff points for overweight in children [18] has enabled us to analyze the NFCS data in terms of BMI cutoff values. Because the NFCS database includes data on sociodemographic status, it was also possible to identify certain determinants associated with overweight, obesity, and undernutrition (stunting) in the same group of children.
Materials and methods Subjects The survey population comprised children ages 1 to 8.9 y (12 to 108 mo) in South Africa. The children were selected from the database of the NFCS (n ⫽ 2894), which was undertaken in 1999 and which had been oversampled by 25% for children from low socioeconomic areas, at the directive of the national department of health. All children who had a complete set of anthropometric and sociodemographic data were included in this secondary data analysis. A total of 156 enumerator areas (EAs) were included in the survey, 82 of which were urban and 74 non-urban [12]. The distribution of EAs per province was determined pro-
5
portionately to the distribution of the total population and the urban versus non-urban distribution in each province. An EA was defined as the EA drawn up for the 1996 census [11]. For economic and practical reasons, in formal and informal urban and tribal areas, only EAs with at least 16 qualifying households were considered for inclusion in the sample, whereas on commercial farms only EAs with at least six qualifying households were considered for inclusion. A qualifying household was defined as any household with at least one child between 12 and 108 mo old residing in it. All EAs were randomly selected and the survey had a response rate of 93% [12]. Sample selection and weighting procedures A self-weighting minimum sample size was generated in accordance with the population size in the nine provinces, stratified for age, urban and rural residence, and provincial and national representation. The result is presented in Table 1, column A. The total proposed minimum national sample size was given as 2200. Each EA included approximately 20 households (size of the clusters), and the number of subjects was therefore rounded to be multiples of 20. Another requirement was that there should be at least 50 subjects in each stratum. The results of these adjustments are presented in the column B of Table 1. The total sample size increased to 2440. To accommodate the national department of health’s tender requirement for high-risk populations within the proposed framework of a nationally representative sample, an additional over-sampling (25%) was required. The results of the additional over-sampling are presented in column C of Table 1. The total target sample size was now 3120 children. In the NFCS, 93% of the 3120 subjects’ target was met, which gave a sample of 2894 (with 2570 children having anthropometric measurements and sociodemographic data). It was therefore necessary to adjust the sample, by calculating weights based on these adjustments, to the original intended 2200 subjects. The first step in adjusting the sample was to calculate the base weight, which is the component of the sample weight that accounts for the differential probabilities of selection. This is defined as the inverse of the inclusion probability of the individual in the sample [19]. In this case, the base weights included adjustments for a minimum stratum size of 50 subjects, the requirement that the stratum size had to be a multiple of 20, and over-sampling for high-risk areas. The base weights are presented in column D of Table 1. The next step in calculating the final weights involved poststratification, to adjust the sample weights of the responding subjects so that the totals over various demographic categories matched known population totals [19]. The poststratification cells included age categories, and the known population totals were derived from the 1996 census figures. This adjustment provided for sampling frame inad-
6
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 1 Detailed account of the sampling procedures and weightings used in secondary dietary analysis of the National Food Consumption Survey in South Africa Province
Urban/rural
D
Age (y) groups
E
F
G
H
I
J
KZ
Urban
194
200
260
0.7462
260
320
0.7875
402
400
500
0.804
Rural
15
60
80
0.1875
EC
Urban
127
120
160
0.7938
EC
Rural
214
220
280
0.7643
NP
Urban
28
60
80
NP
Rural
211
220
280
0.7536
WC
Urban
215
220
280
0.7679
WC
Rural
24
60
80
0.3
NW
Urban
62
60
80
0.775
NW
Rural
115
120
160
0.7188
MP
Urban
59
60
80
0.7375
MP
Rural
95
100
120
0.7917
FS
Urban
100
100
120
0.8333
FS
Rural
44
60
80
0.55
NC
Urban
31
60
80
0.3875
NC
Rural
12
60
80
0.15
1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8 1–3 4–6 7–8
0.3662 0.3902 0.2436 0.3610 0.3910 0.2480 0.3793 0.3912 0.2295 0.3660 0.3944 0.2395 0.3471 0.3975 0.2554 0.3481 0.3957 0.2562 0.3546 0.3938 0.2516 0.3494 0.3911 0.2595 0.3651 0.3892 0.2457 0.3859 0.3797 0.2344 0.3581 0.3896 0.2523 0.3625 0.3866 0.2509 0.3644 0.3900 0.2456 0.3636 0.3871 0.2493 0.3463 0.3933 0.2604 0.3435 0.3947 0.2618 0.3576 0.3828 0.2595 0.3848 0.3924 0.2227
0.7325 1.1645 1.3501 0.7775 0.9660 2.1550 0.7820 1.4560 2.5627 0.1445 0.2958 0.5988 0.7111 0.8414 1.1183 0.9429 0.8822 1.0344 0.5516 0.5012 0.8804 0.6195 0.7570 1.3038 0.7072 0.8997 1.1485 0.3087 0.3142 0.3750 0.6938 0.8626 1.0429 0.5147 0.8233 1.6027 1.3436 1.3536 2.0702 0.8857 0.8553 1.4802 0.6660 0.7284 1.8597 0.3686 0.6432 0.8862 0.3261 0.5934 0.5747 0.1283 0.1744 0.8908
2440
3120
97 65 35 117 102 29 195 108 36 38 20 6 62 60 29 79 96 53 18 22 8 119 109 42 111 93 46 30 29 15 32 28 15 81 54 18 16 17 7 39 43 16 52 54 14 41 27 13 34 20 14 36 27 3 2570
0.981694 1.560725 1.809386 0.987317 1.226708 2.736542 0.972661 1.810945 3.187471 0.770603 1.577787 3.193561 0.895846 1.06002 1.408839 1.233679 1.154269 1.353387 1.576124 1.432066 2.515539 0.822124 1.004585 1.730177 0.921007 1.171654 1.495748 1.02909 1.047454 1.250075 0.895279 1.113076 1.345661 0.716131 1.145504 2.229787 1.821769 1.835435 2.807043 1.118756 1.080316 1.869683 0.799208 0.874039 2.231648 0.670228 1.169435 1.611226 0.841449 1.531381 1.48308 0.855211 1.162814 5.938814
2200
95.2 101.4 63.3 115.5 125.1 79.4 189.7 195.6 114.7 29.3 31.6 19.2 55.5 63.6 40.9 97.5 110.8 71.7 28.4 31.5 20.1 97.8 109.5 72.7 102.2 109.0 68.8 30.9 30.4 18.8 28.6 31.2 20.2 58.0 61.9 40.1 29.1 31.2 19.6 43.6 46.5 29.9 41.6 47.2 31.2 27.5 31.6 20.9 28.6 30.6 20.8 30.8 31.4 17.8 3120
71 76 47 91 99 62 153 157 92 5 6 4 44 51 32 74 85 55 10 11 7 74 82 55 78 84 53 9 9 6 22 24 16 42 44 29 21 23 15 34 37 24 35 39 26 15 17 12 11 12 8 5 5 2 2200
KZ
Rural
252
GP
Urban
GP
Total
A
B
C
0.35
A, proposed self-weighting minimum sample size per province; B, sample adjusted to be a multiple of 20 and a minimum sample size of 50; C, additional over-sampling to include high-risk populations; D, base weights (A/C); E, ratio according to 1996 national census (children ages 1 to 8 y); EC, Eastern Cape; F, distribution by age according to design (E ⫻ C); FS, Free State; G, actual surveyed values (realization for anthropometric data); GP, Gauteng; H, realization weight (F/G); I, final weight (H ⫻ D); J, final weighted totals; KZ, Kwazulu-Natal; MP, Mpumalanga; NC, Northern Cape; NP, Northern Province; NW, North West; WC, Western Cape.
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
7
Table 2 Percentage of South African children with height-for-age standard deviation scores (z scores) within different categories of the NCHS standards based on weighted sampling Height-for-age zscore category
Non-weighted* sample size % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 SD to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 SD to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1288)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
288
1019
290
1016
1304
1309
1198
975
440
30.6 25.6 17.67 33.47 25.7 18.43 32.94 44.9 39.84 49.97 2.8 0.73 4.91 1.0 0.00 2.40
16.0 14.5 12.28 16.77 27.1 23.14 31.02 46.3 42.14 50.52 8.7 5.98 11.50 3.3 1.85 4.82
19.3 19.7 13.10 26.24 33.4 28.76 38.3 37.7 32.05 43.46 6.2 3.06 9.29 3.0 1.08 4.94
25.3 23.6 20.52 26.76 28.3 24.94 31.71 40.2 36.67 43.72 5.2 3.70 6.71 2.6 1.33 3.93
26.5 23.8 20.91 26.80 28.0 24.87 31.21 40.7 37.49 43.93 4.9 3.57 6.31 2.4 1.27 3.64
16.7 15.7 13.33 18.02 28.5 25.18 31.81 44.4 40.81 48.01 8.2 5.86 10.47 3.3 2.02 4.50
25.5 24.4 21.95 26.85 27.7 24.62 30.70 37.2 34.44 40.04 6.9 5.22 8.55 3.8 2.55 5.08
20.7 19.7 16.49 22.95 27.6 24.63 30.50 43.0 39.47 46.57 6.4 4.67 8.08 3.3 1.86 4.77
13.0 11.3 8.29 14.27 30.3 25.31 35.39 50.4 44.68 56.15 7.0 3.94 10.14 0.9 0.00 2.01
National† (n ⫽ 2200)
2570 21.6 19.3 17.49 21.16 28.3 25.99 30.58 42.8 40.32 45.20 6.7 5.33 8.12 2.9 2.04 3.76
CI, confidence interval; NCHS, National Center for Health Statistics; SD, standard deviation [21] * Non-weighted sample for comparison. † Weighted sample with complete set of anthropometric and sociodemographic data.
equacies and non-responses. The results of this process are reflected in columns E to J of Table 1. The final totals, calculated separately for each age group, within each stratum (province, urban versus rural) is reflected in the column J of Table 1. Tables 2 to 4 present weighted and nonweighted sample sizes, with the final weighted sample comprising 2200 children. From the results of the sample sizes presented in Tables 2 to 6, it is apparent that some under-representation was present in the original 7- to 8-y-old group. This was most likely due to some, or many, of these children being in school during the survey. It was not always possible to visit the schools and the households within the allotted time frame. With weighting, the sample size of this group increased. Further, the over-sampled high-risk groups, which were found mainly in rural areas and farms, decreased. Despite these adjustments, the weighted and non-weighted findings were similar in most categories, with 1% to 3% differences in 90% of categories. Survey methodology A detailed description of the methodology has been published elsewhere [12]. All anthropometric measurements were made in accordance with recommended techniques [20].
Statistical analysis Data were analyzed with SAS 845 for Windows (SAS Institute, Cary, NC, USA), and National Center for Health Statistics (NCHS) percentiles was used to classify children into categories of nutritional status [21]. This was achieved by converting the data ⫺2 standard deviations (SD) or greater than 2 SD of the NCHS 50th percentiles of heightfor-age (HAZ), weight-for-age (WAZ), and weight-forheight (WHZ) z scores. BMI cutoff points, as recommended by the International Obesity Task Force, for overweight and obesity by gender between ages 2 and 18 y, defined to pass through BMI between 25 and 30 kg/m2 at age 18 y, obtained by averaging data from Brazil, Great Britain, Hong Kong, The Netherlands, Singapore, and United States were used to determine the prevalence of overweight and obesity in the sample [18]. The data were also analyzed by age group, by urban versus rural domain, and by area of residence (farms, tribal, formal urban, and informal urban). Multivariate analyses were conducted with sociodemographic variables to predict determinants of overweight, obesity, and stunting in the sample. The chi-square test was used to test for associations between outcome (stunting and overweight) and area of residence, urban versus rural status, and age. Odds ratios (ORs) were calculated to determine the odds of the proba-
8
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 3 Percentage of South African children with weight-for-age standard deviation scores (z scores) lying within different categories of the NCHS standards based on weighted sampling Weight-for-age z-score category
Weighted sample size† Non-weighted sample size* % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms
Formal urban
Informal urban
Tribal
Rural
Urban
1–3 y
4–6 y
7–8 y
108 288 18.1 13.7 8.93 18.51 30.8 25.58 36.07 49.2 41.71 56.72 3.3 1.16 5.47 2.9 0.10 5.77
946 1019 7.8 6.9 5.23 8.51 24.3 19.87 28.71 53.0 49.57 56.50 10.7 8.14 13.22 5.1 2.70 7.54
272 290 7.6 8.0 4.13 11.95 26.9 21.10 32.62 56.1 50.65 61.55 4.6 1.77 7.52 4.3 2.63 6.08
874 1016 11.3 10.6 8.32 12.86 29.6 26.29 32.93 54.1 50.83 57.45 3.6 2.45 4.67 2.1 1.20 3.02
982 1304 12.8 10.9 8.85 13.01 29.7 26.72 32.77 53.6 50.52 56.67 3.5 2.51 4.55 2.2 1.34 3.05
1218 1309 7.7 7.1 5.57 8.69 24.9 21.15 28.58 53.7 50.72 56.72 9.3 7.11 11.55 4.9 3.01 6.89
795 1198 12.4 11.4 9.50 13.33 26.6 23.93 29.21 49.4 46.50 52.40 8.0 6.14 9.86 4.6 3.24 5.92
861 975 8.8 8.0 6.16 9.84 26.6 22.96 30.21 55.5 52.19 58.89 6.7 4.87 8.45 3.2 1.73 4.70
544 440 7.7 6.4 3.99 8.73 28.5 23.77 33.15 56.9 51.83 61.92 5.0 2.79 7.28 3.3 0.86 5.67
National†
2200 2570 10.3 8.8 7.57 10.09 27.0 24.61 29.48 53.7 51.53 55.80 6.7 5.45 8.03 3.7 2.59 4.85
CI, confidence interval; NCHS, National Center for Health Statistics [21]; SD, standard deviation * Non-weighted sample for comparison. † Weighted total sample with complete set of anthropometric and sociodemographic data.
bility of the outcome (stunting and overweight) measured against exposure variables such as level of education and head of household. The Mantel-Haenzel test was used to test for associations between exposure variables and outcome (stunting and overweight) after adjusting for the effect of the confounders’ age, gender, and location (urban versus rural). Bonferroni’s test was used to show differences between pairs of means and their P values, and the F test of model effects used weighted data and took stratified data into consideration. Confidence intervals (CIs) were calculated by taking the complex sampling design into account [20]. The PROC SURVEY MEANS, which is part of the SAS program, was used to calculate confidence intervals. This takes into consideration the stratified nature of the data, where 18 strata were identified, with nine provinces, urban and rural, within each province. EAs within each stratum are seen as clusters. The data in this report are presented for the weighted sample, and non-weighted data are sometimes presented for comparison.
Results Stunting, a reflection of long-term undernutrition (ⱕ⫺2 SD HAZ), was, at the national level, prevalent in 19.3% of the children, with the highest prevalence in those 1 to 3 y
old (24.4%), in rural areas (23.8%), and particularly on commercial farms (25.6%; Table 2). Compared with the original published survey data [12], the largest difference in the prevalence of stunting was among children who lived on commercial farms (30.6% of non-weighted versus 25.6% of the weighted data), areas that had originally been oversampled. Low WAZ values, a reflection of underweight (ⱕ⫺2 SD WAZ), was, at the national level, prevalent in 8.8% of children, with the highest prevalence in those 1 to 3 y old (11.4%), in rural areas (10.9%), and particularly among those living on commercial farms (13.7%; Table 3). The effects of the original over-sampling were noted in the commercial farm areas, with smaller differences in the other categories. At the other end of the spectrum, 6.7% of children nationally were classified as being overweight (1 to 2 SD WAZ) and 3.7% as obese (ⱖ⫹2 SD WAZ). Overweight and obesity together (ⱖ⫹1 SD WAZ) was 10.4%, with the highest prevalence in children living in formal urban areas (Table 3). In terms of WHZ values, at the national level 3.3% of children could be regarded as wasted (ⱕ⫺2 SD WHZ), whereas 12.4% were overweight (1 to 2 SD WHZ) and 6.6% were obese (ⱖ2 SD WHZ; Table 4). Overall, the prevalence of overweight and obesity (BMI ⱖ 1 SD WHZ) was 19%. Urban areas had the highest rates (20.7%). The mean BMI values of the children by age and area of
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
9
Table 4 Percentage of South African children with weight-for-height standard deviation scores (z scores) within different categories of the NCHS standards based on weighted sampling Weight-for height z-score category
Non-weighted sample size* % ⱕ⫺2.0 SD* % ⱕ⫺2.0 SD† Lower 95% CI† Upper 95% CI† ⫺2 to ⫺1 SD† Lower 95% CI† Upper 95% CI† ⫺1 to 1 SD† Lower 95% CI† Upper 95% CI† 1 to 2 SD† Lower 95% CI† Upper 95% CI† ⱖ2 SD† Lower 95% CI† Upper 95% CI†
Domain analysis by area of residence†
Domain analysis by urban/rural†
Domain analysis by age group†
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1218)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
288
1019
290
1016
1304
1309
1198
975
440
4.3 3.06 0.72 5.40 18.67 12.29 25.05 66.22 61.14 71.31 8.49 4.49 12.48 3.56 0.44 6.67
2.6 2.12 1.22 3.02 13.71 10.83 16.58 61.29 57.67 64.92 14.21 11.23 17.19 8.67 6.25 11.09
2.1 1.99 0.10 3.88 11.61 8.58 14.65 73.16 67.52 78.80 7.71 4.81 10.61 5.53 3.13 7.93
5.1 5.08 3.40 6.76 14.70 12.45 16.94 62.80 59.45 66.15 12.42 10.46 14.38 5.00 3.52 6.47
4.9 4.86 3.33 6.39 15.13 13.01 17.25 63.18 60.08 66.27 11.99 10.15 13.83 4.84 3.54 6.13
2.4 2.09 1.28 2.90 13.24 10.94 15.53 63.95 60.61 67.29 12.75 10.20 15.31 7.97 5.93 10.01
4.0 3.44 2.42 4.47 13.83 11.80 15.85 61.98 58.92 65.03 13.37 11.20 15.54 7.39 5.87 8.90
3.4 3.30 2.03 4.57 13.47 11.11 15.82 63.96 61.05 66.87 13.39 11.24 15.54 5.88 4.13 7.63
3.4 3.20 1.38 5.03 15.43 11.47 19.39 65.43 60.29 70.56 9.47 6.21 12.72 6.47 3.33 9.62
National† (n ⫽ 2200)
2570 3.7 3.3 2.52 4.14 14.1 12.51 15.65 63.6 61.32 65.89 12.4 10.80 14.03 6.6 5.32 7.82
CI, confidence interval; NCHS, National Center for Health Statistics [21]; SD, standard deviation * Non-weighted sample for comparison. † Weighted sample with complete set of anthropometric and sociodemographic data.
residence (Table 5) were significantly different between areas of residence (formal urban and tribal, P ⫽ 0.041), between urban and rural areas (P ⫽ 0.005), and between age groups (P ⬍ 0.001). Nearly 20% of children (17.1%) had a BMI of at least 25 kg/m2 (combined overweight and obe-
sity; Table 6). The groups with the highest prevalence of overweight were children living in urban areas (18.6%) and those ages 1 to 3 y (23.8%). Of interest, only 13.4% children living in informal urban areas were overweight compared with 15.8% of children in tribal areas. Of further interest
Table 5 Descriptive statistics on BMI of South African children based on the weighted National Food Consumption Survey sample Statistics
Domain analysis by area of residence Farms (n ⫽ 108)
Mean BMI Variance of the mean Median Lower 95% CI Upper 95% CI Minimum Maximum Tests of significance†
Formal urban (n ⫽ 946)
15.9 16.3 0.03 0.01 15.7 15.9 15.57 16.06 16.29 16.52 10.7 11.1 25.0 29.7 P ⫽ 0.0409‡
Domain analysis by urban/rural
Domain analysis by age group 1–3 y (n ⫽ 975)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 1218)
16.2 0.01 15.8 15.94 16.38 12.0 27.4
15.9 0.01 15.7 15.77 16.08 10.4 29.4
15.9 16.3 0.01 0.01 15.7 15.9 15.78 16.07 16.06 16.45 10.4 11.1 29.4 29.7 P ⫽ 0.0052§
4–6 y (n ⫽ 861)
16.8 15.7 0.01 0.01 16.5 15.5 16.63 15.54 16.91 15.86 11.1 10.4 27.4 23.9 P ⱕ 0.0001储
National* (n ⫽ 2200)
7–8 y (n ⫽ 544) 15.8 0.02 15.4 15.54 16.06 11.6 29.7
16.1 0.01 15.8 15.99 16.23 10.4 29.7
BMI, body mass index; CI, confidence interval; SD, standard deviation * Weighted samples used Korn and Graubard, 2002 [19]. † Two methods were used to establish significant differences: Bonferroni’s test (weighted data without stratification of data) and regression model (F test of model effects; weighted data with stratification of data). ‡ F test of model effects: significant difference between formal urban and other areas. § Bonferroni’s test: significant difference between urban and rural areas. 储 Bonferroni’s test: significant difference between children ages 1 to 3 y and those ages 4 to 6 y and between children ages 1 and 3 y and those ages 7 to 8 y.
10
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
Table 6 Percentage South African children with BMI values according to BMI standards储 for 1- to 8 y-old children (based on the weighted sample) BMI cutoff points
% ⬍25 Lower 95% CI Upper 95% CI 25–30 Lower 95% CI Upper 95% CI % ⱖ30 Lower 95% CI Upper 95% CI % ⱖ25§ Lower 95% CI Upper 95% CI Chi-square* Chi-square†
Domain analysis by area of residence*
Domain analysis by urban/rural*
Domain analysis by age group*
Farms (n ⫽ 108)
Formal urban (n ⫽ 946)
Informal urban (n ⫽ 272)
Tribal (n ⫽ 874)
Rural (n ⫽ 982)
Urban (n ⫽ 128)
1–3 y (n ⫽ 795)
4–6 y (n ⫽ 861)
7–8 y (n ⫽ 544)
89.2 84.50 93.97 7.2 3.17 11.29 3.5 0.77 6.30 10.8 6.03 15.50 0.0066 0.0054
79.9 75.80 83.99 13.9 10.97 16.88 6.2 4.40 7.96 20.1 16.01 24.19
86.6 83.20 89.98 7.5 5.04 10.00 5.9 3.15 8.63 13.4 10.02 16.80
84.2 81.86 86.48 12.1 10.06 14.12 3.7 2.55 4.93 15.8 13.52 18.14
84.7 82.60 86.85 11.6 9.63 13.49 3.7 2.64 4.79 15.3 13.15 17.40 0.0257 0.0392
81.4 77.94 84.85 12.5 9.97 15.01 6.1 4.55 7.67 18.6 15.15 22.06
76.2 73.38 79.13 16.0 13.70 18.23 7.8 6.07 9.49 23.7 20.87 26.62 ⬍0.0001 ⬍0.0001
84.2 81.25 87.16 12.0 9.59 14.37 3.8 2.50 5.12 15.8 12.84 18.75
90.5 87.31 93.63 6.5 4.22 8.89 3.0 1.13 4.83 9.5 6.37 12.69
National (n ⫽ 2200)
82.9 80.77 84.99 12.1 10.45 13.70 5.0 4.07 6.02 17.1 15.00 19.23
BMI, body mass index; CI, confidence interval; SD, standard deviation * Chi-square P value for testing for associations, using weighted values [19], between BMI groupings, area of residence, urban versus rural, and age groups † Chi-square P value for testing for associations, using non-weighted values, between BMI groupings, area of residence, urban versus rural, and age groups § Refers to overweight and obesity combined. 储 Reference 18; international cutoff points for body mass index for overweight and obesity by gender between 2 and 18 y, defined to pass through BMI of 25 and 30 kg/m2 at age 18, obtained by averaging data from Brazil, Great Britain, Hong Kong, Netherlands, Singapore, and United States.
(Tables 4 and 6) is the comparative similarity in the percentages for overweight children as defined by WHZ and BMI criteria, respectively. The prevalence of combined overweight and obesity as determined by BMI [18] were lower but similar when compared with those determined by NCHS standards for WHZ [21] (Table 6). The variables (OR with 95% CI; adjusted for age, gender, and urban versus rural residence; Table 7) associated with having a BMI greater than 25 kg/m2 (overweight) or a HAZ value less than ⫺2 SD (stunted) were as follows. Factors found to be protective against stunting were having a married mother (OR ⫽ 0.84, CI ⫽ 0.70 to 0.99), having a father as the head of the household (OR ⫽ 0.83, CI ⫽ 0.69 to 0.99), living in a formal house (OR ⫽ 0.78, CI ⫽ 0.66 to 0.93), having a flush toilet (OR ⫽ 0.78, CI ⫽ 0.62 to 0.99), and having a television in the house (OR ⫽ 0.62, CI ⫽ 0.52 to 0.74). Factors that contributed to an increased risk of becoming stunted were a grandmother being the head of the household (OR ⫽ 1.23, CI ⫽ 1.03 to 1.48), the mother and/or caregiver having only primary school education (OR ⫽ 1.31;CI ⫽ 1.10 to 1.56), an informal house as a dwelling (shack; OR ⫽ 1.29, CI ⫽ 1.02 to 1.66), fewer than three rooms in the house (OR ⫽ 1.32, CI ⫽ 1.11 to 1.56), using paraffin for cooking (OR ⫽ 1.24, CI ⫽ 1.04 to 1.48), and absence of a refrigerator in the house (OR ⫽ 1.42, CI ⫽ 1.19 to 1.69). With regard to being overweight (and obese combined; BMI ⱖ 25 kg/m2), the following factors were associated with an increased risk of being overweight: residing in a traditional or mud house (OR ⫽ 1.44, CI ⫽ 1.09 to 1.89),
having a flush toilet (OR ⫽ 1.78, CI ⫽ 1.31 to 2.41), and having a HAZ value less than ⫺2 SD (stunted; OR ⫽ 1.80, CI ⫽ 1.48 to 2.20). Factors that contributed to a decreased risk of being overweight were having an outside drop toilet (OR ⫽ 0.57, CI ⫽ 0.34 to 0.95), using paraffin for cooking (OR 0.78, CI ⫽ 0.63 to 0.97), having no refrigerator (OR ⫽ 0.68, CI ⫽ 0.50 to 0.84), having no stove in the house (OR ⫽ 0.79, CI ⫽ 0.64 to 0.98), a mother having only a primary education (OR ⫽ 0.76, CI ⫽ 0.61 to 0.95), and a WAZ score less than ⫺2 SD (underweight; OR ⫽ 0.16, CI ⫽ 0.07 to 0.35).
Discussion Over the past two decades, there have been many studies that have probed for determinants of undernutrition in South African children [12,22–24]. These studies have been particularly important because 31% of underweight children in a rural area of Limpopo Province (previously known as the Northern Province) were found to have an overweight (BMI ⱖ 25 kg/m2) mother or caregiver [25]. Similarly, nearly 50% of mothers and/or caregivers of stunted and underweight children in a rural area of North West Province were found to be overweight [22]. This phenomenon has been found in other developing countries, including China, Russia, and Brazil [14]. Clearly, similar circumstances or factors, whether environmental, behavioral, or individual, favor the development of underweight and overweight in the same household [14 –26].
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
11
Table 7 Odds ratios of children in the weighted sample with BMI values ⱖ25 and less than minus 2 standard deviations (z scores) for height-for-age (adjusted for age and gender and urban/rural residence)
1. Mother is married 2. Person responsible for food preparation: Mother Grandmother 3. Decides on foods purchased: Mother Grandmother 4. Responsible for feeding child: Mother Grandmother 5. Head of household: Father Mother Grandmother 6. Type of house: Brick/cement Mud/traditional Tin/plank/wood 7. ⬎6 people living in house 8. ⬍3 rooms in house 9. Source of water: Own tap Communal tap 10. Type of toilet: Flush Bucket/pot 11. Fuel for cooking is paraffin 12. No refrigerator in house 13. No stove in house 14. Television in the house 15. Mother has a primary education 16. Mother is unemployed 17. Education level of caregiver (primary or none) 18. Father is unemployed 19. HAZ ⬍ ⫺2 SD 20. WAZ ⬍ ⫺2 SD
BMI ⱖ 25 kg/m2 (overweight)
HAZ ⱕ ⫺2SD (stunted)
OR
CI
OR
CI
0.92
0.77–1.11
0.84*
0.70–0.99
1.01 1.01
0.83–1.22 0.81–1.26
0.98 0.95
0.82–1.17 0.77–1.17
1.02 1.03
0.85–1.23 0.84–1.26
0.87 1.01
0.74–1.04 0.84–1.22
1.02 1.14
0.84–1.23 0.91–1.42
0.92 1.02
0.78–1.10 0.83–1.26
0.90 0.78 1.19
0.75–1.09 0.56–1.08 0.97–1.45
0.83* 0.84 1.23*
0.69–0.99 0.62–1.13 1.03–1.48
0.97 1.44* 0.68* 0.92 1.10
0.79–1.19 1.09–1.89 0.50–0.91 0.76–1.10 0.91–1.33
0.78* 1.15 1.29* 0.95 1.32*
0.66–0.93 0.93–1.43 1.01–1.65 0.80–1.12 1.11–1.56
1.15 0.78*
0.89–1.47 0.61–0.99
0.96 1.08
0.78–1.18 0.89–1.30
1.78* 0.57* 0.78*
1.31–2.41 0.34–0.95 0.63–0.97
0.78* 1.29 1.24*
0.62–0.99 0.90–1.86 1.04–1.48
0.68* 0.79* 1.17 0.76*
0.55–0.84 0.64–0.98 0.95–1.43 0.61–0.95
1.42* 1.10 0.62* 1.31*
1.19–1.69 0.92–1.32 0.52–0.74 1.10–1.56
1.04 0.75*
0.86–1.25 0.59–0.95
1.15 1.25*
0.97–1.36 1.04–1.50
1.01 1.80* 0.16*
0.78–1.31 1.48–2.20 0.07–0.35
1.02
0.82–1.27
BMI, body mass index; CI, 95% confidence interval; HAZ, height-for-age z score; OR, odds ratio; WAZ, weight-for-age z score. * Mantel-Haenszel method.
Tladinyane [22] compared childcare practices in mothers or caregivers of stunted and underweight children with those of well-nourished children ages 1 to 4 y in a village of North West Province of South Africa. It was found that poor hygiene practices and the mother’s level of education were the major determinants for development of undernutrition. A similar case-control study was undertaken in stunted versus non-stunted children in an urban area of Gauteng [23]. A positive association was found between stunting and certain childcare practices, including the unfair allocation of household food to its younger members and non-participation of fathers in child care. The NFCS (non-weighted
sample) examined risk factors for underweight and stunting in children ages 1 to 9 y [12]. The most influential determinants were low education levels of the mother or caregiver and economically related indicators such as having an informal housing structure and few rooms in the house, poor or inadequate water and sanitation, low income and unemployment, and no stove or refrigerator in the dwelling. Similar trends were found in the weighted sample. Most of these determinants are associated with poverty. Additional important findings from the NFCS are that the highest prevalences of stunting and underweight were found in rural areas and particularly among children ages 1 to 3 y.
12
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
In contrast, the highest prevalence of overweight was found in children ages 1 to 3 y living in urban areas. Clearly, children ages 1 to 3 y are most vulnerable to undernutrition and overnutrition. If stunting takes place at this age, it is postulated that these children may be at greater risk for being overweight [15,26]. The diet, which may be low in nutrient density, may lead to this outcome. There is a paucity of information on determinants of overweight and obesity in children in South Africa. In adults, particularly in women [27,28], determinants associated with an increased risk for overweight include black ethnicity, education level below grade 7, inactivity, and at least one overweight parent. In these same women, age has been positively associated with BMI and with waist circumference [27,28]. Because obesity affects nearly 30% of black urban women [11], it is clearly important to identify risk factors that might be environmentally controlled or manipulated by means of interventions for the prevention or treatment of overweight and obesity. In developing countries other than South Africa, such determinants of overweight among adults vary greatly across countries, races, and cultural groups. In Bahrain, for instance, marriage, a large family, and ownership of a car have been positively associated with obesity, whereas level of education was not in either gender [29]. A study undertaken in Egypt reported that obesity among female university students was associated with obesity during childhood, obesity in one or both parents, limited physical activity, food intake between meals, and long afternoon napping [30]. Additional studies have identified risk factors for obesity, which include older age, being married, parental obesity, low level of education, increased number of meals eaten, eating between meals, high family income, large number of servants, decreased number of people living at home, long periods of watching television, and inactivity in the workplace [31–33]. Since the early 1990s, there have been several studies that have shown an association between early stunting and overweight at a later stage, particularly in those countries undergoing the nutritional transition [34 –37]. Because the health and economic consequences of obesity are so debilitating and prevalent in the South African population, it is important to determine whether undernutrition in young children may in the long term lead to overweight or obese older children and adults. This is of national importance because the current focus and budget of health services addresses primarily the prevention and treatment of undernutrition rather than a predisposition to overweight and obesity. Although the mechanism by which stunting may predispose to overweight or obesity remains largely undefined, it has been postulated that nutritional stunting is associated with impaired fat oxidation [38]. Another postulate is that nutritionally stunted children may have an impaired regulation of energy intake, which increases the risk for obesity [39]. Further, data from Brazil appear to indicate that mild
stunting might be associated with a greater susceptibility to the effects of high-fat diets [40]. In this regard, excess weight gain among lower-income groups is believed to be the result of a change from a traditional “healthy” diet to a high-fat diet in addition to decreased levels of physical activity [14,41]. Whatever the reason, there appears to be a coexistence of stunting and overweight in low-income populations undergoing the nutritional transition. Martorell et al. [42] measured levels of overweight and obesity in preschool children from developing countries to determine trends in weight status. They found that overweight was more common in urban areas and particularly among girls. Moreover, gross national product was related negatively to stunting and positively to overweight. In countries where maternal and child malnutrition exist alongside rapid economic development, abdominal obesity and associated chronic diseases seem likely to increase [43]. A greater central fat distribution has also been found in children stunted early in childhood [14,44]. In the present study, we examined the NFCS data to identify determinants of overweight and stunting in children. With few exceptions, the types of determinants for these two nutritional disorders appear to be the same but directionally opposite in affording risk for a given nutritional disorder. For example, mothers or caregivers who have a lower level of education conferred a significantly lower risk for overweight but a significantly higher risk for stunting in their children. Conversely, having a flush toilet in the house conferred a high risk of overweight and a low risk of stunting, probably due to an association with economic status. The most important and relevant predictor of overweight is having a low HAZ score (ⱕ⫺2 SD). Stunted children have nearly twice the risk of being overweight (BMI ⱖ 25 kg/m2). Conversely, having a WAZ score no greater than ⫺2 SD confers a significantly lower risk for overweight. These associations have aptly been described: “If under- and overweight can occur in the same household, common underlying causes of both conditions may be identified” [14, p. 2595]. When examining the type of determinants of stunting and overweight in the context of poverty and rapid nutritional transition, it becomes decidedly important that neither nutritional disorder be treated in isolation but should, by necessity, be afforded equal priority in terms of health resources. The cumulative effect of the double burden of apparently related nutritional disorders in the developing world needs to be evaluated to develop satisfactory interventions for their prevention and management. References [1] Bourne LT, Lambert EV, Steyn K. Where does the black population of South Africa stand on the nutrition transition? Public Health Nutr 2001;5(1A):157– 62. [2] Bourne LT, Langenhoven ML, Steyn K, Jooste PL, Nesamvuni AE, Laubscher JA. The food and meal pattern in the urban African
N.P. Steyn et al. / Nutrition 21 (2005) 4 –13
[3]
[4]
[5]
[6]
[7] [8] [9] [10]
[11] [12]
[13]
[14]
[15]
[16]
[17]
[18]
[19] [20] [21]
[22]
[23]
population of the Cape Peninsula, South Africa: the BRISK Study. Cent Afr J Med 1994;40:140 – 8. Steyn NP, Senekal M, Brits S, Alberts M, Mashego T, Nel JH. Weight and health status of black female students. S Afr Med J 2000;90:146 –52. Steyn NP, Senekal M, Brits S, Nel JH. Urban and rural differences in dietary intake, weight status and nutrition knowledge of black female students. Asia Pac J Clin Nutr 2000;9:53–9. Fourie J, Steyn K. Review of research and identification of essential health research priorities. Chronic diseases of lifestyle in South Africa. MRC technical report. Tygerberg, Cape Town: South African Medical Research Council; 1995. Obesity. Preventing and managing the global epidemic. Report of a WHO consultation. WHO technical report series 894. Geneva: World Health Organization; 2000. Dietz WH. The obesity epidemic in young children. BMJ 2001;322: 313– 4. Childhood obesity: an emerging public-health problem. Lancet 2001; 357:1989. Yanovski JA, Yanovski SZ. Treatment of pediatric and adolescent obesity. JAMA 2003;289:1851–3. Rudolf MCJ, Sahota P, Barth JH, Walker J. Increasing prevalence of obesity in primary school children: cohort study. BMJ 2001;322: 1094 –5. Statistics South Africa. Stats in brief 2000. Pretoria: Department of Statistics; 2000. Labadarios D (editor), Steyn NP, Maunder E, MacIntyre U, Swart R, Gericke G, Huskisson J, Dannhauser A, Vorster HH, Nesmvuni AE. The National Food Consumption Survey (NFCS): children aged 1–9 years, South Africa, 1999. Pretoria: Department of Health; 2000. Available at: www.sahealthinfo.org/nutrition module/nfcs/chapter 2 methodology. Accessed June 21, 2004. Hoffman DJ, Sawaya AL, Verreschi I, Tucker KL, Roberts SB. Why are nutritionally stunted children at increased risk of obesity? Studies of metabolic rate and fat oxidation in shantytown children from São Paulo, Brazil. Am J Clin Nutr 2000;72:702–7. Doak C, Adair LS, Monteiro C, Popkin BM. Overweight and underweight coexist within households in Brazil, China and Russia. J Nutr 2000;130:2965–71. Popkin BM, Richards MK, Montiero CA. Stunting is associated with overweight in children of four nations that are undergoing the nutrition transition. J Nutr 1996;26:3009 –16. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med 1993;22:167–77. Diet, nutrition and the prevention of chronic diseases. Report of a Joint WHO/FAO Expert Consultation. WHO technical report series 916. Geneva: World Health Organization; 2003. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–3. Korn EL, Graubard BI. Analysis of health surveys. New York: John Wiley & Sons; 2002. Lee RD, Nieman DC. Nutritional assessment. Oxford: Brown and Benchmark; 1993. National Center for Health Statistics. NCHS growth curves for children. Birth to 18 years. DHEW publication PHS 78-1650. Hyattsville, MD: US Department of Health, Education and Welfare; 1977. Tladinyane P. The difference in factors influencing childcare between others/caregivers of undernourished and well nourished children aged 1– 4 years in Oukasie (Brits), North West Provence (masters thesis). Pretoria: MEDUNSA; 2003. Agboka JA. Quality of care practices of mothers/caregivers and stunting of children between the ages of 12 and 24 months in Gauteng (masters dissertation). Pretoria: Department of Food and Hospitality
[24]
[25]
[26] [27]
[28]
[29] [30] [31]
[32] [33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41] [42]
[43] [44]
13
Management, Faculty of Natural Sciences, Technickon Pretoria; 2001. Amissah A. Inadequate dietary intakes as a cause stunting among young children in rural South Africa (masters dissertation). Pretoria: Department of Food and Hospitality Management, Faculty of Natural Sciences, Technickon Pretoria; 2001. Steyn NP, Nel JH, Tichelaar HY, Prinsloo JF, Dhansay MA, Oelofse A, Benade AJS. Malnutrition in Pedi preschool children, their siblings and caretakers. S Afr J Clin Nutr 1994;7:12– 8. Popkin BM. The nutrition transition and obesity in the developing world. J Nutr 2001;131:871S–3. Senekal M, Steyn NP, Mashego T-AB, Nel JH. Evaluation of body shape, eating disorders and weight management related parameters in black female students of rural and urban origins. S Afr J Psychol 2001;31:45–53. Senekal M, Steyn NP, Nel JH. Factors associated with overweight/ obesity in economically active South African populations. Ethn Dis 2003;13:109 –16. al-Mannai A, Dickerson JW, Morgan JB, Khalfan H. Obesity in Bahraini adults. J R Soc Health 1996;116:30 – 40. Amine EK, Samy M. Obesity among female university students in the United Arab Emirates. J R Soc Health 1996;116:91– 6. Musaiger AO, Radwan HM. Social and dietary factors associated with obesity in university female students in United Arab Emirates. J R Soc Health 1995;115:96 –9. Al-Isa AN. Dietary and socio-economic factors associated with obesity among Kuwaiti college men. Br J Nutr 1999;82:369 –74. Vioque J, Torres A, Quiles J. Time spent watching television, sleep duration and obesity in adults living in Valencia, Spain. Int J Obes 2000;24:1683– 8. Walker SP, Gaskin PS, Powell CA, Bennett FI. The effects of birth weight and postnatal linear growth retardation on body mass index, fatness and fat distribution in mid and late childhood. Public Health Nutr 2002;5:391– 6. Sawaya AL, Dallal G, Solymos G, et al. Obesity and malnutrition in a Shantytown population in the city of Sao Paulo, Brazil. Obes Res 1995;3(suppl 2):107S–15. Florencio TM, Ferreira HS, da Franca AP, Cavalcante JC, Sawaya AL. Obesity and undernutrition in a very-low-income population in the city of Maceio, northeastern Brazil. Br J Nutr 2001;86:277– 84. Walker SP, Gaskin PS, Powell CA, Bennett FI, Forrester TE, Grantham-McGregor S. The effects of birth weight and postnatal linear growth retardation on blood pressure at age 11–12 years. J Epidemiol Community Health 2001;55:394 – 8. Hoffman DJ, Sawaya AL, Verreschi I, Tucker KL, Poberts SB. Why are nutritionally stunted children at increased risk of obesity? Studies of metabolic rate and fat oxidation in shantytown children from Sao Paulo, Brazil. Am J Clin Nutr 2000;72:702–7. Hoffman DJ, Roberts SB, Verreschi I, Martins PA, de Nascimento C, Tucker KL, Sawaya AL. Regulation of energy intake may be impaired in nutritionally stunted children from the shantytowns of Sao Paulo, Brazil. J Nutr 2000;130:2265–70. Sawaya AL, Grillo LP, Verreschi I, da Silva AC, Roberts SB. Mild stunting is associated with higher susceptibility to the effects of high fat diets: studies in a shantytown population in Sao Paulo, Brazil. J Nutr 1998;128(suppl 2):415S–20. Kain J, Vio F, Albala C. Obesity trends and determinant factors in Latin America. Cad Saude Publica 2003;19(suppl 1):77S– 86. Martorell R, Kettel KL, Hughes ML, Grummer-Strawn LM. Overweight and obesity in preschool children from developing countries. Int J Obes Relat Metab Disord 2000;24:959 – 67. James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res 2001;9(suppl 4):228S–33. Schroeder DG, Martorell R, Flores R. Infant and child growth and fatness and fat distribution in Guatemalan adults. Am J Epidemiol 1999;149:177– 85.