To assess space-time trends in malnutrition and associated risk factors among children (<5 years) in South Africa.
Multiround national panel survey using multistage random sampling.
National, community based.
Community-based sample of children and adults. Sample size: 3254 children in wave 1 (2008) to 4710 children in wave 5 (2017).
Stunting, wasting/thinness and obesity among children (<5). Classification was based on anthropometric (height and weight) z-scores using WHO growth standards.
Between 2008 and 2017, a larger decline nationally in stunting among children (<5) was observed from 11.0% to 7.6% (p=0.007), compared with thinness/wasting (5.2% to 3.8%, p=0.131) and obesity (14.5% to 12.9%, p=0.312). A geographic nutritional gradient was observed with obesity more pronounced in the east of the country and thinness/wasting more pronounced in the west. Approximately 73% of districts had an estimated wasting prevalence below the 2025 target threshold of 5% in 2017 while 83% and 88% of districts achieved the necessary relative reduction in stunting and no increase in obesity respectively from 2012 to 2017 in line with 2025 targets. African ethnicity, male gender, low birth weight, lower socioeconomic and maternal/paternal education status and rural residence were significantly associated with stunting. Children in lower income and food-insecure households with young malnourished mothers were significantly more likely to be thin/wasted while African children, with higher birth weights, living in lower income households in KwaZulu-Natal and Eastern Cape were significantly more likely to be obese.
While improvements in stunting have been observed, thinness/wasting and obesity prevalence remain largely unchanged. The geographic and sociodemographic heterogeneity in childhood malnutrition has implications for equitable attainment of global nutritional targets for 2025, with many districts having dual epidemics of undernutrition and overnutrition. Effective subnational-level public health planning and tailored interventions are required to address this challenge.
Uses data from a nationally representative repeated panel data at individual/household level over a 10-year period (five survey waves).
Employed a fully Bayesian space-time shared component model to produce more stable estimates of malnutrition burden at provincial and district levels among children under 5 years of age in South Africa.
Panel design allows assessment of change in malnutrition burden within the same individuals/households observed at multiple time points.
Missing or invalid weight/height measurements may have introduced selection bias if not missing at random, and may thus have affected both the internal validity and the representativeness of the findings.
As primary panel study was not designed/powered for provincial and lower geographic-level analysis, we cannot discount the resultant impact on precision/random variability when analysing at provincial/district level (administrative tier just below province) and further stratification by sociodemographic correlates.
Despite reductions in malnutrition 150.8 million children (22.2%) under 5 are stunted and a further 50.5 million children are wasted.
Progress to tackle all forms of child malnutrition remains much too slow.
We include a Strengthening the Reporting of Observational studies in Epidemiology statement
Data were taken from the five panel (cross-sectional) waves of the South African National Income Dynamics Study (SA-NIDS)
We restricted our analysis to children <5 years of age.
We calculated HA and body mass index (BMI)-for-age (BA) z-scores using the WHO 2007 growth standards.
To identify relevant inequalities, undernutrition and obesity indicators were stratified temporally (survey year), geographically (province and residence location type: urban informal settlements, urban formal, tribal/rural) and by important sociodemographic categories (gender: female/male; ethnicity: Black/African, coloured, Indian/Asian, White/Caucasian; maternal: age; education status; BMI; household socioeconomic status (SES) (income) classified into quantiles (1=lowest, 5=highest)).
Analyses were performed using Stata software V.15 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp). Given the multistage random sampling design of the primary study, clustering and survey design effects were accounted for using sample weights to estimate SE and 95% CIs around mean anthropometric z-score point estimates, both overall and stratified by other sociodemographic variables such as ethnicity and gender, SES and residence location type. Extrapolated population totals of malnourished children (<5) by yearly age were estimated using the survey weights.
We assessed for the presence of univariate and bivariate spatial autocorrelations for the three anthropometric classifications using Moran’s I statistics. This analysis was performed using GeoDa.
We employed Bayesian spatial-temporal modelling approach in an attempt to stabilise estimates at district level given that the primary sampling design was not developed to provide point estimates at this level of geographic disaggregation and resultant zero prevalence estimates for particular districts and waves. We choose a Bayesian spatial-temporal formulation to model each of the anthropometric outcomes independently using an autoregressive approach. We employed a Bayesian hierarchical binomial model that simultaneously attempts to estimate the stable spatial and temporal structured patterns and as well as from these stable components using an unstructured space-time interaction term.
Let Y1ij, Y2ij and Y3ij be the numbers of stunted, thin and obese children, respectively, for the ith area and jth period, i=1,…, I, j=1,…, J and nij the total number of children sampled in a given area and period. We assumed that Y1ij, Y2ij and Y3ij follow binomial distributions, that is, Y1ij~binomial (n1ij, π1ij), Y2ij~binomial (n2ij, π2ij), Y3ij~binomial (n3ij, π3ij), i=1,…,53, j=1,…,5, where π is the risk (prevalence) of stunting, thinness or obesity in region i in period j. We define the logit of the prevalence for a given anthropometric outcome as follows:
where α1–3 are the overall baseline risk (intercept) for each nutritional outcome, ϕ1–3 the spatial random effects, assume intrinsic Gaussian conditionally autoregressive distributions
To aid the interpretation of prevalence point estimates in line with WHO 2025 nutritional targets we also estimated exceedance probabilities associated with the target thresholds for each nutritional outcome, namely: 40% reduction in stunting from 2012 to 2015, reduce and maintain wasting to <5% by 2025 and no increase in obesity by 2025.
Survey weighted prevalences were applied to sample size totals by district and panel to obtain a survey weighted numerator count for each outcome (Y1ij, Y2ij, Y3ij above) from the binomial distribution. The space-time models were fitted in WinBUGS using Markov chain Monte Carlo (MCMC) simulation and non-informative priors. The full WinBUGS model code is provided in the
Survey weighted two-way tabulations of key sociodemographic covariates, year and child nutritional status were performed to produce correctly weighted prevalence estimates. Tests of independence for complex survey data (weighted Pearson’s χ2 test) were used to assess the significance of bivariate associations between malnutrition burden and year as well as sociodemographic covariates.
As this was a data analysis using secondary data from a national community-based panel survey, the development of the research question was not informed by the study subjects. Likewise, we could not involve study participants in the design of this study. Study participants were not involved in conduct of the primary study. Results will be disseminated in the form of peer-reviewed article as well as through presentation to senior members of our National Department of Health and KwaZulu-Natal Department of Health.
The sample of children <5 years of age in the 7301 households included in the SA-NIDS survey increased from 3254 children at baseline (2008) to 4710 children in wave 5 (2017) (
Sociodemographic characteristics of sampled children by survey round
Variable | Category | Wave 1: | Wave 2: 2010/2011 | Wave 3: | Wave 4: 2014/2015 | Wave 5: 2017 |
n (%) | n (%) | n (%) | n (%) | n (%) | ||
Age (years) | <1 | 661 (20.3) | 517 (14.6) | 652 (17) | 886 (19.7) | 813 (17.3) |
1–1.99 | 661 (20.3) | 621 (17.5) | 691 (18) | 875 (19.5) | 909 (19.3) | |
2–2.99 | 670 (20.6) | 751 (21.2) | 764 (19.9) | 863 (19.2) | 996 (21.1) | |
3–3.99 | 642 (19.7) | 840 (23.7) | 826 (21.5) | 914 (20.3) | 992 (21.1) | |
4–4.99 | 620 (19.1) | 820 (23.1) | 909 (23.7) | 960 (21.3) | 1000 (21.2) | |
Gender | Male | 1640 (50.4) | 1773 (50) | 1856 (48.3) | 2173 (48.3) | 2325 (49.4) |
Female | 1614 (49.6) | 1770 (49.9) | 1986 (51.7) | 2322 (51.6) | 2385 (50.6) | |
Ethnicity* | African | 2723 (83.7) | 3047 (85.9) | 3307 (86.1) | 3898 (86.7) | 4048 (85.9) |
Coloured | 429 (13.2) | 423 (11.9) | 455 (11.8) | 532 (11.8) | 523 (11.1) | |
Asian/Indian | 32 (1) | 26 (0.7) | 24 (0.6) | 30 (0.7) | 0 (0) | |
White | 70 (2.2) | 53 (1.5) | 56 (1.5) | 29 (0.6) | 0 (0) | |
Birth weight | LBW (<2.5 kg) | 249 (7.7) | 267 (7.5) | 364 (9.5) | 459 (10.2) | 460 (9.8) |
NBW (≥2.5 kg) | 2401 (73.8) | 2553 (71.9) | 3110 (80.9) | 3605 (80.1) | 3563 (75.6) | |
HBW (≥4 kg) | 105 (3.2) | 99 (2.8) | 121 (3.1) | 156 (3.5) | 157 (3.3) | |
Non-HBW (<4 kg) | 2545 (78.2) | 2721 (76.7) | 3353 (87.3) | 3908 (86.9) | 3866 (82.1) | |
Missing BW | 604 (18.6) | 729 (20.5) | 368 (9.6) | 434 (9.6) | 687 (14.6) | |
Low monthly household income | <R2500 | 1737 (53.4) | 1804 (50.8) | 1660 (43.2) | 1484 (33) | 1202 (25.5) |
≥R2500 | 552 (17) | 1014 (28.6) | 1686 (43.9) | 2749 (61.1) | 3109 (66) | |
Child hungry in the last year (food security)† | Never | 2148 (66) | N/A | |||
Seldom | 333 (10.2) | |||||
Sometimes | 583 (17.9) | |||||
Often | 149 (4.6) | |||||
Always | 35 (1.1) | |||||
Province | Eastern Cape | 437 (13.4) | 442 (12.5) | 437 (11.4) | 545 (12.1) | 545 (11.6) |
Free State | 163 (5) | 171 (4.8) | 200 (5.2) | 244 (5.4) | 242 (5.1) | |
Gauteng | 274 (8.4) | 346 (9.7) | 381 (9.9) | 455 (10.1) | 538 (11.4) | |
KwaZulu-Natal | 1057 (32.5) | 1076 (30.3) | 1188 (30.9) | 1449 (32.2) | 1534 (32.6) | |
Limpopo | 293 (9) | 348 (9.8) | 423 (11) | 497 (11) | 471 (10) | |
Mpumalanga | 231 (7.1) | 257 (7.2) | 283 (7.4) | 307 (6.8) | 356 (7.6) | |
North-West | 226 (6.9) | 240 (6.8) | 269 (7) | 293 (6.5) | 296 (6.3) | |
Northern Cape | 243 (7.5) | 224 (6.3) | 258 (6.7) | 316 (7) | 322 (6.8) | |
Western Cape | 330 (10.1) | 344 (9.7) | 367 (9.6) | 368 (8.2) | 368 (7.8) | |
Environment | Rural formal | 324 (10) | 350 (9.9) | 343 (8.9) | 389 (8.6) | 449 (9.5) |
Tribal authority area | 1583 (48.6) | 1526 (43) | 1801 (46.9) | 2154 (47.9) | 2135 (45.3) | |
Urban formal | 1133 (34.8) | 1221 (34.4) | 1319 (34.3) | 1498 (33.3) | 1702 (36.1) | |
Urban informal | 214 (6.6) | 228 (6.4) | 257 (6.7) | 303 (6.7) | 317 (6.7) | |
Mother BMI | Underweight | 85 (2.6) | 78 (2.2) | 58 (1.5) | 98 (2.2) | 135 (2.9) |
Normal | 1010 (31) | 1105 (31.1) | 1250 (32.5) | 1373 (30.5) | 1485 (31.5) | |
Overweight | 734 (22.6) | 850 (24) | 962 (25) | 1054 (23.4) | 1053 (22.4) | |
Obese | 932 (28.6) | 987 (27.8) | 1054 (27.4) | 1377 (30.6) | 1382 (29.3) | |
Missing | 493 (15.2) | 529 (14.9) | 518 (13.5) | 596 (13.3) | 655 (13.9) | |
Mother age | <20 | 234 (7.2) | 238 (6.7) | 259 (6.7) | 316 (7) | 322 (6.8) |
20–24 | 807 (24.8) | 872 (24.6) | 971 (25.3) | 1100 (24.5) | 1062 (22.5) | |
25–34 | 1213 (37.3) | 1413 (39.8) | 1566 (40.8) | 1853 (41.2) | 2004 (42.5) | |
35–44 | 583 (17.9) | 581 (16.4) | 633 (16.5) | 682 (15.2) | 772 (16.4) | |
45+ | 81 (2.5) | 92 (2.6) | 82 (2.1) | 86 (1.9) | 98 (2.1) | |
Missing | 336 (10.3) | 353 (9.9) | 331 (8.6) | 461 (10.2) | 452 (9.6) | |
Mother education | None | 131 (4) | 115 (3.2) | 76 (2) | 48 (1.1) | 81 (1.7) |
Primary | 505 (15.5) | 419 (11.8) | 405 (10.5) | 387 (8.6) | 97 (2.1) | |
Secondary | 1871 (57.5) | 2265 (63.8) | 2654 (69.1) | 3176 (70.6) | 3130 (66.5) | |
Tertiary | 132 (4.1) | 141 (4) | 172 (4.5) | 240 (5.3) | 707 (15) | |
Missing | 615 (18.9) | 609 (17.2) | 535 (13.9) | 647 (14.4) | 695 (14.8) |
*139 misclassified or missing in 2017.
†Only included in wave 1 questionnaire.
BMI, body mass index; BW, birth weight; HBW, high birth weight; LBW, low birth weight; N/A, not applicable; NBW, normal birth weight.
Between 2008 and 2017, the prevalence of stunting among children aged under 5 years decreased from 11.0% to 7.6% (p=0.007) (
Burden of stunting, thinness/wasting and obesity among children by age and survey round
Survey wave | Age (years) | n (valid HAZ) | n (stunted) | Prop: stunted* | Estimated population stunted | n (valid BMIZ) | n (thin/wasted) | Prop: thinness† | Estimated population thinness | n (obese) | Prop: obese‡ | Estimated population obese |
2008 | 0 | 220 | 31 | 0.14 | 153 648 | 180 | 21 | 0.12 | 133 882 | 32 | 0.1 | 107 783 |
1 | 419 | 29 | 0.08 | 91 903 | 386 | 24 | 0.06 | 66 566 | 76 | 0.22 | 253 021 | |
2 | 453 | 62 | 0.15 | 159 241 | 419 | 10 | 0.03 | 34 613 | 70 | 0.14 | 148 357 | |
3 | 489 | 55 | 0.11 | 111 595 | 470 | 19 | 0.04 | 39 715 | 67 | 0.17 | 176 235 | |
4 | 498 | 48 | 0.09 | 93 391 | 461 | 25 | 0.05 | 52 031 | 34 | 0.08 | 80 282 | |
0–5 | 2079 | 225 | 0.11 | 591 550 | 1916 | 99 | 0.05 | 277 743 | 279 | 0.14 | 778 865 | |
2010/2011 | 0 | 75 | 24 | 0.33 | 289 420 | 69 | 7 | 0.1 | 88 499 | 22 | 0.39 | 340 820 |
1 | 236 | 20 | 0.06 | 63 995 | 215 | 11 | 0.07 | 69 776 | 52 | 0.29 | 299 127 | |
2 | 340 | 61 | 0.22 | 267 019 | 314 | 17 | 0.06 | 76 344 | 72 | 0.22 | 270 818 | |
3 | 427 | 52 | 0.11 | 130 531 | 402 | 20 | 0.03 | 39 208 | 78 | 0.16 | 195 314 | |
4 | 422 | 62 | 0.17 | 205 730 | 394 | 19 | 0.03 | 39 494 | 65 | 0.17 | 208 842 | |
0–5 | 1500 | 219 | 0.16 | 862 302 | 1394 | 74 | 0.05 | 265 877 | 289 | 0.21 | 1 159 133 | |
2012 | 0 | 271 | 59 | 0.2 | 181 464 | 250 | 38 | 0.2 | 179 118 | 55 | 0.19 | 169 192 |
1 | 544 | 78 | 0.13 | 132 310 | 538 | 27 | 0.08 (0.05, 0.13) | 80 862 | 138 | 0.23 | 234 062 | |
2 | 629 | 72 | 0.1 | 116 230 | 629 | 49 | 0.05 | 55 866 | 147 | 0.23 | 269 508 | |
3 | 710 | 82 | 0.11 | 142 259 | 692 | 29 | 0.03 | 43 898 | 102 | 0.15 | 191 943 | |
4 | 771 | 112 | 0.16 | 221 293 | 762 | 30 | 0.03 (0.0, 0.05) | 43 556 | 118 | 0.18 | 250 658 | |
0–5 | 2925 | 403 | 0.13 | 762 303 | 2871 | 173 | 0.06 | 328 768 | 560 | 0.19 | 1 112 487 | |
2014/2015 | 0 | 434 | 74 | 0.12 | 144 201 | 421 | 37 | 0.1 | 123 211 | 78 | 0.17 | 197 209 |
1 | 801 | 53 | 0.06 | 67 916 | 801 | 24 | 0.03 | 39 657 | 169 | 0.23 | 266 780 | |
2 | 785 | 65 | 0.08 | 85 985 | 781 | 16 | 0.02 | 16 222 | 128 | 0.16 | 170 803 | |
3 | 853 | 82 | 0.08 | 89 857 | 845 | 24 | 0.04 | 40 865 | 79 | 0.12 | 133 857 | |
4 | 899 | 67 | 0.06 | 77 887 | 897 | 19 | 0.02 | 30 376 | 56 | 0.06 | 82 300 | |
0–5 | 3772 | 341 | 0.08 | 441 281 | 3745 | 120 | 0.04 | 213 012 | 510 | 0.14 | 834 444 | |
2017 | 0 | 372 | 50 | 0.13 | 125 347 | 357 | 32 | 0.12 | 121 396 | 70 | 0.18 | 174 538 |
1 | 760 | 55 | 0.08 | 95 527 | 742 | 23 | 0.03 | 42 416 | 146 | 0.23 | 285 123 | |
2 | 833 | 63 | 0.07 | 94 807 | 830 | 20 | 0.03 | 43 976 | 130 | 0.15 | 191 812 | |
3 | 875 | 77 | 0.08 | 99 890 | 872 | 14 | 0.02 | 30 726 | 77 | 0.07 | 88 889 | |
4 | 900 | 59 | 0.05 | 57 363 | 899 | 23 | 0.03 | 29 923 | 47 | 0.06 | 63 912 | |
0–5 | 3740 | 304 | 0.08 | 445 295 | 3700 | 112 | 0.04 | 223 236 | 470 | 0.13 | 758 650 | |
At last observation | 0–5 | 10 711 | 1049 | 0.09 | 1 397 020 | 10 467 | 391 | 0.04 | 560 806 | 1438 | 0.14 | 2 048 650 |
*HAZ ≤−2 SD.
†BMI-for-age z-score ≤−2 SD.
‡BMI-for-age z-score ≥+2 SD.
§Significance tests (survey weighted logistic regression) among children 0–5: stunting (2017 vs 2008) p=0.007; thinness/wasting (2017 vs 2008) p=0.131; obesity (2017 vs 2008) p=0.312.
BMI, body mass index; BMIZ, BMI-for-age z-score; HAZ, height-for-age z-score.
In 2008, the highest prevalence of stunting was estimated in the Free State (18%), followed by Eastern Cape (14.8%) and Limpopo (14.0%). By 2017, the highest prevalence of stunting was still observed in Free State (10%), followed by Northern Cape (9.6%) and Limpopo (8.5%) (
Bayesian posterior median smoothed prevalence of stunting by province (and wave, A) and district-level prevalence (equal intervals, 2017, B) among children <5 years. BCI, Bayesian credibility interval.
North-West province had the highest burden of thinness/wasting in 2008 (10.1%), followed by Gauteng (9.5%) and Western Cape (8.2%) (
Bayesian posterior median smoothed prevalence of thinness/wasting by province (and wave, A) and district-level prevalence (equal intervals, 2017, B) among children <5 years. BCI, Bayesian credibility interval.
In 2008, the highest posterior median smoothed prevalence of obesity was estimated in Eastern Cape (22.5%), followed by KwaZulu-Natal (18.3%) and Western Cape (18.1%) (
Bayesian posterior median smoothed prevalence of obesity by province (and wave, A) and district-level prevalence (equal intervals, 2017, B) among children <5 years. BCI, Bayesian credibility interval.
A post-hoc sample size (power) analysis is presented in online supplementary material 10. A bivariate analysis of demographic, maternal, socioeconomic and household factors at individual nutritional status level suggests that African ethnicity (p<0.001), male gender (p=0.002), low birth weight (LBW) (p<0.001), residing in lower SES household (p<0.001), province of residence (p=0.012), lower maternal/paternal education status (p<0.001 and p=0.020, respectively) and residence in a rural/tribal authority area (p<0.001) were significantly associated with stunting (
Demographic, socioeconomic and maternal factors associated with nutritional status among children under 5 years, 2008–2017
Variable | Category | Stunted | P value | Thin/wasted | P value | Obese | P value | |||
Yes (% col) | No (% col) | Yes (% col) | No (% col) | Yes (% col) | No (% col) | |||||
Ethnicity | African | 0.939 | 0.871 |
| 0.885 | 0.879 | 0.823 | 0.931 | 0.870 |
|
Coloured | 0.053 | 0.074 | 0.076 | 0.072 | 0.052 | 0.076 | ||||
Asian/Indian | 0.003 | 0.012 | 0.015 | 0.011 | 0.004 | 0.013 | ||||
White | 0.006 | 0.039 | 0.025 | 0.037 | 0.014 | 0.041 | ||||
Gender | Male | 0.562 | 0.496 |
| 0.514 | 0.501 | 0.686 | 0.523 | 0.498 | 0.178 |
Female | 0.438 | 0.504 | 0.486 | 0.499 | 0.477 | 0.502 | ||||
Birth weight | LBW (<2.5 kg) | 0.148 | 0.098 |
| 0.13 | 0.098 | 0.163 | 0.072 | 0.104 |
|
NBW (≥2.5 kg) | 0.852 | 0.903 | 0.87 | 0.902 | 0.928 | 0.896 | ||||
HBW (≥4 kg) | Not applicable | Not applicable | 0.056 | 0.04 |
| |||||
Non-HBW (<4 kg) | 0.944 | 0.96 | ||||||||
Income quantile | Lowest | 0.294 | 0.199 |
| 0.234 | 0.203 | 0.481 | 0.226 | 0.2 | 0.422 |
Low | 0.205 | 0.187 | 0.214 | 0.188 | 0.203 | 0.186 | ||||
Middle | 0.183 | 0.200 | 0.169 | 0.201 | 0.18 | 0.204 | ||||
High | 0.197 (0.1579, 0.243) | 0.186 | 0.184 | 0.191 | 0.182 | 0.192 | ||||
Highest | 0.122 | 0.229 | 0.2 | 0.218 | 0.209 | 0.218 | ||||
Low monthly household income | <R2500 | 0.566 | 0.417 |
| 0.488 | 0.423 |
| 0.481 | 0.416 |
|
≥R2500 | 0.434 | 0.583 | 0.512 | 0.577 | 0.519 | 0.584 | ||||
Child hungry in the last year (food security)* | Never | 0.689 | 0.697 | 0.505 | 0.512 | 0.704 |
| 0.707 | 0.693 | 0.645 |
Seldom | 0.127 | 0.096 | 0.111 | 0.097 | 0.076 | 0.102 | ||||
Sometimes | 0.126 | 0.155 | 0.317 | 0.148 | 0.154 | 0.155 | ||||
Often | 0.054 | 0.043 | 0.052 | 0.042 | 0.052 | 0.041 | ||||
Always | 0.004 (0.0011, 0.0144) | 0.009 | 0.007 | 0.009 | 0.011 | 0.009 | ||||
Province | Eastern Cape | 0.165 | 0.132 |
| 0.075 | 0.137 |
| 0.19 | 0.124 |
|
Free State | 0.066 | 0.050 | 0.032 | 0.052 | 0.045 | 0.052 | ||||
Gauteng | 0.188 | 0.236 | 0.298 | 0.231 | 0.173 | 0.246 | ||||
KwaZulu-Natal | 0.218 | 0.227 | 0.161 | 0.228 | 0.293 | 0.212 | ||||
Limpopo | 0.143 | 0.109 | 0.129 | 0.113 | 0.074 | 0.121 | ||||
Mpumalanga | 0.085 | 0.083 | 0.096 | 0.082 | 0.074 | 0.085 | ||||
North-West | 0.055 | 0.05 | 0.06 | 0.05 | 0.038 | 0.053 | ||||
Northern Cape | 0.022 | 0.023 | 0.033 | 0.022 | 0.011 | 0.025 | ||||
Western Cape | 0.06 | 0.091 | 0.116 | 0.086 | 0.103 | 0.084 | ||||
Environment | Rural/tribal authority | 0.519 | 0.451 |
| 0.429 | 0.46 | 0.647 | 0.466 | 0.457 | 0.111 |
Urban informal | 0.122 | 0.101 | 0.1 | 0.102 | 0.133 | 0.097 | ||||
Urban formal | 0.359 | 0.448 | 0.47 | 0.437 | 0.402 | 0.446 | ||||
Mother BMI | Underweight | 0.041 | 0.022 |
| 0.051 | 0.023 |
| 0.019 | 0.025 | 0.135 |
Normal | 0.397 | 0.344 | 0.418 | 0.348 | 0.327 | 0.356 | ||||
Overweight | 0.268 | 0.273 | 0.249 | 0.272 | 0.26 | 0.273 | ||||
Obese | 0.294 | 0.361 | 0.282 | 0.357 | 0.395 | 0.346 | ||||
Mother age | <20 | 0.073 | 0.048 | 0.156 | 0.112 | 0.047 |
| 0.057 | 0.049 | 0.121 |
20–24 | 0.219 | 0.230 | 0.258 | 0.23 | 0.265 | 0.224 | ||||
25–34 | 0.468 | 0.491 | 0.398 | 0.492 | 0.472 | 0.49 | ||||
35–44 | 0.215 | 0.210 | 0.213 | 0.211 | 0.191 | 0.214 | ||||
45+ | 0.025 | 0.021 | 0.019 | 0.022 | 0.015 | 0.023 | ||||
Mother education | None | 0.023 | 0.018 |
| 0.025 | 0.019 |
| 0.025 | 0.018 | 0.568 |
Primary | 0.121 | 0.072 | 0.132 | 0.071 | 0.067 | 0.075 | ||||
Secondary | 0.799 | 0.796 | 0.715 | 0.802 | 0.803 | 0.798 | ||||
Tertiary | 0.057 | 0.114 | 0.129 | 0.108 | 0.105 | 0.11 | ||||
Father education | None | 0.003 | 0.003 |
| 0.005 | 0.003 | 0.960 | 0.002 | 0.003 |
|
Primary | 0.646 | 0.56 | 0.565 | 0.556 | 0.584 | 0.551 | ||||
Secondary | 0.275 | 0.389 | 0.382 | 0.387 | 0.318 | 0.398 | ||||
Tertiary | 0.077 | 0.048 | 0.048 | 0.055 | 0.097 | 0.047 |
Statistically significant associations highlighted in bold.
*Only included in wave 1 questionnaire.
BMI, body mass index; HBW, high birth weight; LBW, low birth weight; NBW, normal birth weight.
The present study illustrates that while stunting has declined among South African children over the last 10 years, wasting and obesity appear largely unchanged, suggesting that development and public health interventions have had a variable impact. Stunting prevalence appears relatively evenly spread across South Africa, but obesity burden is more pronounced in the east of the country, whereas thinness/wasting is more pronounced in the west. In terms of progress towards WHO 2025 nutritional targets, 14 of 52 (27%) districts had an estimated wasting prevalence still exceeding 5% prevalence in 2017 as well as 17% (9/52) and 12% (6/52) districts not attaining the relative reduction in stunting prevalence required or with an increase in obesity prevalence respectively from 2012 to 2017. A further concerning pattern observed was the increasing prevalence of obesity in children under the age of 2 years. Key sociodemographic factors associated with malnutrition status were identified which likely underpins the spatial patterns (and heterogeneity) observed across the country. African children with lower birth weights residing in lower income households in rural areas with less educated mothers and fathers were particularly more likely to be stunted. Children in lower income, food-insecure households with malnourished young mothers appeared particularly more likely to be thin/wasted while African children, with higher birth weights, living in lower income households in KwaZulu-Natal and Eastern Cape were also more likely to be obese. Furthermore, low household income appeared to be positively associated with all three nutritional types. Declining childhood stunting rates from 2008 to 2017 may well have resulted from government initiatives to support food security and child health (among other things), but our findings of distinct geographic and sociodemographic variability in undernutrition and obesity rates suggest that tackling malnutrition in South Africa is complex. Models and targets for nationally driven intervention need to be carefully specified according to local environments and socioeconomic profiles.
Two previous studies in South Africa among primary school-aged children dating back 25+ years (1993 and 1994, respectively) used cross-sectional data,
This is also the first spatial-temporal Bayesian-shared component analysis of malnutrition trends among children in South Africa using geographically representative repeated panel data over a 10-year period. The current study focusing on children under 5 years of age suggests that there is prominent geographic heterogeneity in malnutrition burden in South Africa in this youngest age group. This is in line with findings from other settings in Africa that have documented similar spatial heterogeneity
Undernutrition and overnutrition status appeared positively associated with lower household income classification. This finding of stunting and wasting disproportionately affecting the poor has been often demonstrated.
Our findings suggest that children with LBW (due to preterm delivery, fetal/intrauterine growth restriction or a combination of the two) were significantly more likely to be stunted than normal weight babies and this has been demonstrated in many other low and middle-income settings
Obesity in children has a complex aetiology that includes a wide range of socioeconomic, demographic, environmental and cultural variables,
Lastly and contextually, body mass is culturally influenced in South Africa, and the high level of obesity in KwaZulu-Natal and Eastern Cape may at least in part be a result of cultural beliefs that associate overweight with wealth and good health.
To our knowledge this is the first spatial-temporal analysis of malnutrition trends among children under 5 years of age in South Africa. We used standardised anthropometric measurements of children and their mothers from nationally representative repeated panel data over a 10-year period. The panel nature of the design allows assessment of change in malnutrition burden within the same individuals/households observed at multiple time points. A further strength was the implementation of a fully Bayesian space-time shared component model to produce more stable joint estimates of malnutrition by province, district and year.
The study has several limitations. First, missing or invalid weight/height measurements (especially in wave 2, and among infants—
Estimating the cost of child malnutrition in South Africa is extremely complicated and no locally determined cost data exist. Data from the USA suggest that the incremental lifetime direct medical cost for a 10-year-old obese child relative to a 10-year-old normal weight child ranges from US$12 660 to US$19 630.
Our findings suggest the need to implement evidence-based child health strategies and policy (eg, further social grant support to vulnerable and impoverished households) that is tailored to specific geographies and socially disadvantaged subpopulations. A higher prevalence of child thinness/wasting among younger mothers (<25) in poorer, food-insecure household highlights the importance of policies that enable younger mothers to adequately care for their children in all settings. Integrated nutrition programmes in LMICs have had a substantial impact on child nutrition and health via a combination of multisector-targeted interventions.
The heterogeneity of malnutrition is a feature of spatial inequality and rapid urbanisation that has manifested in widening levels of inequality in South Africa’s districts and a need to reassess where nutrition programmes need to be further decentralised to the highest risk municipalities and local communities to maximise effectiveness. This work provides the first district-level ranking of childhood overweight, thinness/wasting and stunting and allows a differentiated proactive tailored intervention to be developed for each municipal district. The dual epidemic of undernutrition and overweight/obesity requires differential geographical policy inputs in metropolitan areas and districts across the rural-urban divide. The current and future health cost of malnutrition among South African children is likely substantial based on previous costing estimates. There is an urgent need to address nutrition problems among preschool-aged children in South Africa and other LMICs. Effective public health planning and geographically/contextually tailored interventions are required at subnational level to address this challenge. The analytical framework employed in this study we believe will have definite utility in other settings.