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Rapidly increasing body mass index among children, adolescents and young adults in a transitioning population, South Africa, 2008–15

Rapidly increasing body mass index among children, adolescents and young adults in a... Background: There is a global epidemic of overweight and obesity; however, this rate of increase is even greater in some low- and middle-income countries (LMIC). South Africa (SA) is undergoing rapid socioeconomic and demographic changes that have triggered a rapid nutrition transition. The paper focuses on the recent rate of change of body mass index (BMI) among children, adolescents and young adults, further stratified by key soci- odemographic factors. Methods: We analysed mean BMI of 28 247 individuals (including children) from 7301 households by age and year, from anthropometric data from four national cross- sectional (repeated panel) surveys using non-linear fitted curves and associated 95% confidence intervals. Results: From 2008 to 2015, there was rapid rise in mean BMI in the 6–25 age band, with the highest risk (3–4þ BMI unit increase) among children aged 8–10 years. The increase was largely among females in urban areas and of middle-high socioeconomic standing. Prominent gains were also observed in certain rural areas, with extensive geographical heterogeneity across the country. Conclusions: We have demonstrated a major deviation from the current understanding of patterns of BMI increase, with a rate of increase substantially greater in the developing world context compared with the global pattern. This population-wide effect will have major consequences for national development as the epidemic of related non- communicable disease unfolds, and will overtax the national health care budget. Our refined understanding highlights that risks are further compounded for certain groups/ V The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 2 International Journal of Epidemiology, 2017, Vol. 0, No. 0 places, and emphasizes that urgent geographical and population-targeted interventions are necessary. These interventions could include a sugar tax, clearer food labelling, revised school feeding programmes and mandatory bans on unhealthy food marketing to children.The scenario unfolding in South Africa will likely be followed in other LMICs. Key words: Body mass index, rapid gain, nutritional transition, children, adolescents, young adults, South Africa Key Messages South Africa is experiencing exponential increases in population mean BMI within a relatively short time period , par- ticularly among female school-aged children, adolescents and young adults. The increase in mean BMI in the 8–10 year age group over a short period (7 years) is 6–7-fold higher than global BMI increases reported between 1980 and 2008. The largest mean increases in BMI over the period show strong geographical heterogeneity, with the increase great- est in urban settlements (both informal and formal) and in middle/high socioeconomic groups. Our results highlight the need for close investigation in other LMIC, where similar patterns may exist, and where there may also be unexpected increases of BMI and subsequently future explosions in non-communicable diseases, with concomitant negative outcomes for life expectancy, and health care costs. Our results demonstrate a rapid BMI change in specific age groups, thus providing a basis for public health interven- tions specifically targeting geographical areas and/or population segments, as well as potentially some more general innovative policy measures. 2 2 Introduction 0.5 kg/m per decade for women. The cost of obesity in national health budgets has risen markedly, and it has been Globally, there is a rapidly rising trend in body mass index 1–3 estimated that the cost of obesity/overweight has increased (BMI) and overweight and obesity in adults, and child- from 6.5% to 9.1% of annual medical spending in national hood obesity has emerged as one of the most serious public health budgets. A combination of these concerns regard- health issues of the 21st century. Epidemiological studies ing the potential health and economic burden of increasing have shown a substantial increase in the risk of disease BMI, has led to adiposity being included among the global with elevated BMI (i.e. severe or morbid obesity). The non-communicable disease (NCD) targets, namely to halt current and future risks of overweight/obesity include a the rise in obesity prevalence by 2025 compared with 2010 range of obesity-related non-communicable disorders, 14,15 levels. Significant health benefits and cost savings, including cardiovascular and kidney diseases, diabetes, therefore, could be achieved by reducing overweight/obes- early onset metabolic syndrome, hypertension, many can- 6–10 ity in children and adolescents as part of a programme to cers and musculoskeletal disorders. The Global BMI reduce the population distribution of BMI. Thus, infor- Mortality Collaboration has examined the relationship be- mation on whether countries are on track to achieve this tween body mass index (BMI) and all-cause mortality, target is needed to support accountability towards the glo- using individual participant data meta-analyses of 189 pro- bal NCD commitments. spective studies, involving a total of 3 951 455 partici- Southern Africa, and particularly South Africa (middle- pants. The association between both overweight and income), is undergoing rapid socioeconomic, epidemiologi- obesity with higher all-cause mortality was consistently cal and lifestyle transitions that have precipitated a rapid demonstrated across all settings. In recent years, increas- ing efforts have been made to assess BMI trends both nutritional transition. These transitions underpin the aeti- 1,12 within and across nations. A systematic review and ology of a rapidly rising trend in BMI and waist circumfer- ence (WC), as evidenced by previous overweight/obesity meta-analysis of epidemiological studies, carried out in 18,19 199 countries over three decades, indicated that in all but a studies and their associated determinants. Evidence few countries, the average BMI of the adult population from 19 other South African studies suggests a far higher increased between 1980 and 2008. The average rate of increase in childhood/adolescent obesity than the global 2 2 increase was 0.4 kg/m per decade for men and averages of 0.4 kg/m /decade for men and 0.5 kg/m /decade RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 3 for women. The rapid changes in obesity in South Africa given the confounding impact of pregnancy on weight and indicate that in only 6 years (between 2002 and 2008) ado- waist circumference. lescent obesity rates doubled, whereas in the USA this took twice as long (13 years). It can be hypothesized that this Outcome increase is progressing at a far greater rate than that observed globally both in LMIC and high-income countries BMI for adults and BMI-for-age z-scores [or equivalent (HIC), and there is a need to quantify this rate of increase BMI-for-age percentile] for children using the latest World in specific populations longitudinally. Health Organization (WHO) growth reference stand- Furthermore, previous studies have focused on adults ards. Overweight and obese cut-offs for BMI among and dichotomized obesity classification (rather than under- adults were set at 25–29.9 and 30þ kg/m , based on inter- lying BMI and WC distribution), thus neglecting the key national cut-offs. We also included additional analysis child and adolescent groups which are often more sus- of waist circumference (WC), which is presented as ceptible to rapid transitions. The aim of this study was to supplementary material (available as Supplementary data use longitudinal national survey data of anthropometric at IJE online). measurements to quantify the rapidly changing BMI distri- bution by age for the 6- to 25-year-old population group in Sociodemographic variables South Africa. We also show the rate of change of BMI from 2008 to 2015 across a range of demographic factors Sociodemographic variables include: that include gender, population group, socioeconomic sta- panel year (four survey waves); tus, rural-urban divide and geographical districts in South age (2–64 years); Africa. gender; ethnicity, classified as Black/African and non-African (Coloured, Indian/Asian, Caucasian); Methods provincial districts of South Africa; Data geotype; socioeconomic status (household income) classified into Data were taken from the four panel (cross-sectional) waves low [mean South African rand (ZAR) of 681 per month, of the South African National Income Dynamics Study (SA- 23 range of 1 to 1000], medium (mean ZAR of 1516 per NIDS), the first national panel study in South Africa. SA- month, range ZAR > 1000 to 2000) and high tertiale NIDS was undertaken by the South African Labour and (mean ZAR of 7017 per month, range > 2000 to Development Research Unit based at the School of 300,000) categories. Economics at the University of Cape Town. The surveys took place in 2008, 2010–11, 2012 and 2014–15. These are There are four major geotypes in South Africa, two named waves 1–4, respectively. A stratified, two-stage ran- rural, namely rural formal and tribal authority areas dom cluster sample design was employed to sample house- (TAAs), and two urban, namely urban formal and urban holds for inclusion at baseline, and proportionally allocated informal: stratification was based on the 52 district councils (DCs) in rural: ‘farms’; South Africa. Within each DC [primary sampling unit TAAs: ‘communally-owned land under the jurisdiction (PSU)], clusters of dwelling units were systematically drawn. of traditional leaders. Settlements within these areas are A detailed report on the methodology employed in this study villages’; is provided elsewhere. The household-level response rate urban: ‘a continuously built-up area that is established was 69% and the individual response rate within households through township establishment such as cities, towns, was 93%. The baseline SA-NIDS survey provides data for ‘townships’, small towns and hamlets’; 28 247 individuals (including children) from 7301 house- formal residential: ‘single houses, town houses, high- holds. Weight and height measurements were taken for all rise flats, scheme housing, estates’; individuals, as well as waist circumference measurements for informal residential: ‘illegal informal structures’. all adults (aged 18þ) in the household. Data analysis Study population Analyses were performed using Stata software version 13 We restricted our analysis to individuals aged 5 to 79 (StataCorp. 2013; Stata Statistical Software, Release 13, years. Pregnant women were excluded from the analysis College Station, TX]. Clustering, as well as survey design RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 4 International Journal of Epidemiology, 2017, Vol. 0, No. 0 effects, were accounted for using sample weights to cor- Table 1. Baseline characteristics of the study population rectly estimate standard error, and hence 95% confidence Characteristic n (N ¼ 21024) % intervals (CIs) around BMI point estimates. Mean BMI (and standard deviation) or median (and Gender: Male 9174 43.6 interquartile range) by age and survey round were further Female 11849 56.4 stratified by other sociodemographic variables such ethni- Unknown 1 0.0 city and gender, socioeconomic status and geotype; 95% Age group (years): confidence intervals were also calculated around point Unknown 21 0.1 estimates. < 5 2074 9.9 As the distribution of mean BMI by age was non-linear, 5–9 2482 11.8 we fitted non-linear smoothed curves using a mathematical 10–14 2664 12.7 growth curve proposed by Preece and Baines. Fitted BMI 15–24 4357 20.7 25–34 2613 12.4 curves by age and survey round were also stratified by the 35–44 2290 10.9 aforementioned sociodemographic characteristics, to visu- 45–54 1903 9.1 ally highlight changes in specific age bands by period and 55–64 1329 6.3 demographic characteristics. 65þ 1291 6.1 Ethnicity: Black/African 17309 82.3 BMI-for-age z-scores and abnormal levels among children Coloured 2678 12.7 aged 2–18 years Asian/Indian 244 1.2 We calculated BMI-for-age z-scores and BMI categories Caucasian 793 3.8 for children ages 2 to 18, using the WHO 2007 reference Geotype: standards based on height, weight, gender and age in Rural 2069 9.8 months. BMI-for-age z-scores were generated using the Tribal authority areas 9966 47.4 ‘zanthro’ and ‘zbmicat’ commands in Stata. A z-score Urban (formal) 7744 36.8 Urban (informal) 1245 5.9 ofþ 1 to 1.99 is classified as overweight, and a score of 2þ is classified as obese. Additional supplementary analyses of The following cut-offs for central overweight/obesity based on waist cir- waist circumference among adults were conducted and cumference were used for males (> 94 cm) and females (> 80cm), as currently specified in international guidelines. included in Figure A1 and Table A3, available as Supplementary data at IJE online. A waist circumference Four waves of data from 2008 to 2015 are used to show greater than 94 cm among men, and greater than 80 cm the distribution of mean BMI by age (5–79 years) in 28,29 among women, was classified as overweight/obese. Figure 1 (also see Table A1). A rapid rise in mean BMI was observed from young childhood, with a plateau around Mapping/Geographic Information Score 40–50 years of age, and thereafter a decline towards eld- Maps depicting change in BMI-for-age z-score and BMI by erly age. An increase was observed among adults aged district among children (aged 2–18 years) and adults 26–57, with a linear decrease in the gain in this group with (19–64 years), respectively, from 2008 to 2014/15 were de- increasing age. Overall, the mean gain in BMI among veloped using Map Info Professional. males from waves 1 to 4 was þ0.5 units (95% CI: 0.1 toþ 1.1) and þ1.7 units (95% CI: þ1.1 toþ 2.3) among females. Results From 2008 to 2015 there was a mean þ2 BMI increase among young children (especially aged 8–10) to young The baseline sample for all age groups in South Africa adulthood. This increase (comparing wave 4 with wave 1 (2008) comprised 21 024 subjects (out of a total of in Figure 1) in the 6–25 years age band (captured between 28 247) with anthropometric data. The baseline sample the orange lines) was statistically significant (Figure 2). comprised: 3254 (11.5%) children under 5; 4355 (15.4%) The largest gains in this age group were among the 8–10- children between the ages of 5 and 11 years; 4487 (15.9%) year-olds, with a 3 to 4 mean BMI gain over the 7-year adolescents between the ages of 12 and 18 years; 5172 period. (18.3%) young adults aged 19–29 years; 4470 (16.9%) We further unpacked the change in the proportion of middle-aged adults (30–44 years); 4333 (15.4%) adults subjects that were overweight and/or obese by age and sur- aged 45–64 years; and 1779 (6.3%) elderly adults (65þ vey wave (Figure 3). A pronounced rise (‘hump’) in the years of age) (Table 1); 76 subjects had a missing age. The proportion of children aged 5–12 who were overweight or majority of subjects were African (n ¼ 17 309, 82.3%). RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 5 Figure 1. Distribution of BMI by age and survey round. (Note: thicker lines represent the fitted non-linear curves for each wave of the same colour). Figure 2. Change in BMI by age from 2008 to 2014–15 with 95% CIs for change included. (Note: orange band highlights age bands with significant in- creases in mean BMI from 2008 to 2014–15, and grey region highlights adult age bands with increased BMI from 2008 to 2014–15, but which were not statistically significant due to low sample size). obese (BMI-for-age z-scoreþ1) was observed when com- urban-rural groups among the 8–25-year-old population, paring 2014–15 with 2008. A consistent, but less pro- but most prominently among younger female children nounced, rise in the proportion of adults (aged 19þ) who aged 8–10 years living in middle- to high-income house- were overweight and/or obese was observed when compar- holds in urban formal areas, and among lower-income ing the later survey round with the baseline (Figure 3b). households in urban informal settlements. The largest A further stratification of the change in BMI by age, gains in BMI in younger children occurred in the middle gender and ethnicity suggests that the major contributor to socioeconomic status (SES) group, followed by high SES the increasing mean BMI in younger age groups was (Figure 4b). Notably there were also BMI gains in the low among African and non-African females (Figure 4a). Gain SES group, among children aged 8–12 years. The largest in mean BMI was observed across all socioeconomic/ gains by geotype occurred in the urban sector (formal and RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 6 International Journal of Epidemiology, 2017, Vol. 0, No. 0 Figure 3. Proportion of overweight and/or obese by age and survey round. informal) in the 8–12-year age band (Figure 4c), which among in females. Medium SES households indicated the indicated aþ 4 BMI unit gain over the period. The rural highest gain in BMI in the 8–19 age band, with low SES sector did not display as large an increase in BMI in this households displaying the least gain in BMI between 5 and age band as compared with the formal sector; however, 21 years. Urban adolescents displayed greater gains in BMI there was a notable gain in BMI in the rural sector over the than rural adolescents, but between 16 and 19 years urban period (þ2 BMI units through most age bands). households in informal settlements had a lower BMI than Mean BMI change by district was heterogeneous across those in rural households. These results show that the rate the country (Figure 5; Table A2, available as Supplementary of mean BMI increase in South Africa over a 7-year period data at IJE online). The largest gains (þ2.5meanBMI in- was substantially higher than the global mean gain. crease from 2008 to 2015) were observed in parts of four of This rapid increase is especially problematic in LMICs the nine provinces, namely: KwaZulu-Natal, Free State, that are subject to both economic and lifestyle transi- North West and Limpopo. Surprisingly, the urban metropol- tions. The rapid growth in obesity is especially prevalent itan municipalities (Buffalo City, City of Cape Town, in sub-Saharan Africa (SSA), which has experienced a Ekurhuleni, eThekwini, City of Johannesburg, City of growth in gross domestic product (GDP), exposure to glo- Tshwane, Mangaung, and Nelson Mandela Bay) did not balization, greater disposable income and unprecedented register the largest gains in mean BMI over this period. levels of urban migration which result in the establishment of large urban informal settlements. A combination of these factors has driven a nutrition transition that refers to Discussion changing dietary patterns primarily driven by availability of cheap energy-dense foodstuffs, increased consumption In summary, the highest rate of BMI increase was among children aged 5 to 11 years, with a higher rate of BMI gain of saturated fats/animal proteins and growing sugary RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 7 Figure 4. Change in BMI for subjects aged 4–29 years of age (by 2-year age band) from 2008 to 2014–15. (Note: dashed lines are actual data and solid bold lines of same colour are fitted smooth non-linear curves). beverage consumption. Our results confirm rapidly rising population, as reflected in our results, exceeds those found 21,32 levels of obesity in South Africa, particularly in the urban in other South African studies. sector (formal and informal). It is significant, however, Gender appears to influence BMI gains in South Africa, that the rate of change of obesity in the younger especially in female school-aged children between 8 and RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 8 International Journal of Epidemiology, 2017, Vol. 0, No. 0 Figure 5. Map of mean BMI change by district from 2008 to 201–15 (please see Table A2 for codes and underlying data, available as Supplementary data at IJE online). 12 years of age. It is interesting to note, however, that an- ability to make an individual feel full (sated), thus resulting other South African study found that BMI for women in an ‘incomplete compensatory reduction in energy intake 2 2 40 increased from 25.8 kg/m in 1980 to> 29 kg/m in 2008 at subsequent meals after intake of liquid calories’. Despite (higher than the average global BMI gain over this period increased SSB consumption being a likely contributing factor for women ofþ 1.46), illustrating that BMI gain was for this rapid risk in BMI in this age group, it would however higher for females of all age categories. South African not operate in isolation, as the relationship between SSBs overweight/obesity differs markedly by gender, as a result and weight gain is likely confounded by the influences of of adolescent males having lower energy intakes and other dietary and lifestyle factors. engaging in higher levels of physical activity than adoles- Furthermore, overweight children and adolescents have 20,33 cent females. Behavioural and cultural phenomena a very high likelihood (70%) of becoming overweight often assume that a fat child is both healthy and happy, adults who have a significantly higher risk of non- and that they are HIV-negative. The Third South African communicable diseases35,13 that substantially reduce life National Youth Risk Behaviour Survey (YRBS) also re- expectancy outcomes and increase health care costs, as ported that only 49% of learners ate fresh fruit often, and well threatening the attainment of 2030 Sustainable only 40% ate at least a cup of vegetables a day. Other im- Development Goals. portant factors include a higher risk for urban adolescents The consequences of a sharp increase in non- because of relentless marketing, close proximity to low- communicable disease, as a result of the obesity epidemic, cost energy-dense foodstuffs and increasing disposable in- is likely to surpass smoking as a leading cause of disability- 20,34 43 come. Other potential factors include depression, adjusted years, A previous study demonstrated that the stress, noise pollution and insufficient sleep, all of which cost of care for major non-communicable diseases, such as are sometimes related to urbanization pressures, and espe- cardiovascular disease and diabetes, is and will be beyond 13,35 cially to metropolitan areas. the coping capacities of individuals, households and fami- Data on 9–10-year-old children from 12 countries, lies as well as governments (increased health care costs and including both high- and low-income groups within each lost GDP) in most African countries. The effect of the country, suggest that South African children drink sugar- increased burden on the health care systems is also likely to sweetened beverages and sports drinks more than four times overtax public and private health budgets, as well as in- per week, which was higher than any other country sur- crease externality costs and promote poorer public sector 36,37 veyed. South African adolescents, therefore, have among service. A study by Sturm et al. suggests that a rise in the the highest consumption of sugar sweetened beverages total expenditure per individual with increasing BMI from (SSBs) among children aged 9–10 years. Drinking one SSB a minimum of under ZAR 14,000 per individual in the /day increases adult likelihood of being overweight/obese by healthy BMI range of 20–24.9, and an increasing expend- 38–40 27% and child likelihood by 55%. SSBs lead to weight iture trend with acceleration when the BMI  30 to ZAR gain as result of their high added sugar content and low 17,000þ. Total expenditure for diabetes mellitus in RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 9 South Africa was between ZAR 11.5 and 20 billion in in an increase of obesity of 16% by 2017, with 20% of this 2010 (7–12% of total health expenditure), and is predicted increase likely due to SSBs. In absolute terms, this translates to rise to ZAR 14.4–26.2 billion by 2030. into an increase of 280 000 cases, with most of this burden Beyond the direct health care cost, there is also the among young South Africans. Evidence from modelling in macro-economic impact due to increased body mass, South Africa suggests that a 20% sugary beverage excise tax which cannot be discounted. This operates through the (health promotion levy) would have prominent impacts on economic burden on households through lost wages of the growing obesity epidemic. Further cost-effective strat- prime-aged adults due to disability or death; and the cost egies include more stringent food advertising regulations, at of and further intensification of the poverty cycle, in al- a projected cost of ZAR 0.90 per person. To stem the tide ready vulnerable households. Overweight/obesity is also of the fast-growing obesity epidemic among South African likely to reduce the productive life of working individuals children and adolescents, there is urgency in passing manda- and negatively impact on local GDP though absenteeism. tory bans on unhealthy food advertising to children, and Interventions to mitigate against childhood/early adoles- regulating transparent front-of pack-food labelling, in add- cent obesity will result in major savings in public health ition to the aforementioned fiscal approaches. costs. ‘According to the first South African National Health The study has several limitations. First, smaller sample and Nutrition Examination Survey (SANHANES-1), it ap- sizes and hence less precision among smaller ethnic groups. pears that the most significant increase in waist For example, few data on the SA coloured population im- circumference, as well as overweight and obesity, occur be- pacted on our ability to assess change in BMI by age group. tween 15 and 35 years of age. As this represents childbear- Second, lower response as well as greater attrition rates ing age, it may be the perfect window of opportunity to were observed in certain social strata in the NIDS survey. intervene, not only to optimise the health of the mother but The highest attrition rates occurred in wealthier population also of her offspring’. It has been estimated that the cost groups (Whites/Asians/Indian), and the lowest attrition of obesity/overweight has increased from 6.5% to 9.1% of rates in poorer households (Africans/Coloured). These annual medical spending in national health budgets. differences between respondents and non-respondents were More balanced home-cooked meals, and changes to health- taken into account using adjusted sampling weights based ier foodstuffs in school feeding programmes and tuckshops, on observed characteristics. However, one still cannot ex- 13,20,35 are urgently needed. Our data suggest that whereas clude the possibility that unobserved differences might have rural BMI has increased, the rate of increase in urban infor- biased the results of our study. Third, a relatively high num- mal and formal sectors has been greater. This suggests that ber of missing or invalid weight/height measurements there is still time to target rural areas before the prevalence (20–25%) may have introduced selection bias. Fourth, of overweight and obesity increases to ‘urban’ levels, and despite extensive interviewer training and standardization before these individuals migrate to urban areas; such tar- of study protocol, we cannot discount the effect of inter- geted prevention may be most efficient, and could have a observer variability across the different study districts. significant impact on the future obesity in South Africa. The twin scourge of undernutrition and obesity in children Conclusions and simultaneous challenge of rapid urbanization is par- ticularly prevalent in many developing countries, and in- We have demonstrated a major deviation from the current novative policy should include community-level lifestyle understanding of patterns of BMI increase, in that the rate interventions, school-based initiatives, local transport and of increase is substantially greater in the South African urban planning, physical activity and media campaigns that context than the global pattern. This refined understanding are supported by broader interventions to focus on food of BMI changes in a range of demographic and geo- marketing for children, nutritional labelling and legal (fis- graphical variables provides opportunity for more innova- cal) action. These types of policy must ensure that they tive (tailored) intervention programmes. simultaneously target both rich and poor communities. Fiscal measures (such as taxes ) represent the most cost- effective approach per head at ZAR 0.20 (in 2010). South Supplementary Data African children have an especially high consumption of Supplementary data are available at IJE online. SSBs relative to other LMIC and HIC.33,34 This is likely part of the explanation for the high observed risk in mean Acknowledgements BMI over the period in the 8–10 age demographic, based on The South African National Income Dynamics Study (SA-NIDS) our findings. Sugary drinks sales are projected to grow by was conducted by the Southern Africa Labour and Development 2.4% per year based on current trajectories, and will result Research Unit (SALDRU) based at the University of Cape Town’s RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 10 International Journal of Epidemiology, 2017, Vol. 0, No. 0 School of Economics. We thank them for making these data publicly 11. Collaboration GBM. Body-mass index and all-cause mortality: available. individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet 2016;388:776–86. Conflict of interest: None declared. 12. Ng M, Fleming T, Robinson M et al. Global, regional, and na- tional prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014;384:76681. Funding 13. Spruijt-Metz D. 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Sugar-sweetened bever- adults: A mathematical model. PloS One 2014;9:e105287. ages and weight gain in children and adults: a systematic review 54. Finn A, Leibbrandt M, Levinsohn J. Income Mobility in South and meta-analysis. Am J Clin Nutr 2013;98:1084–102. Africa: Evidence from the First Two Waves of the National 41. Lifshitz F. Obesity in children. J Clin Res Pediatr Endocrinol Income Dynamics Study. 2012. http://hdl.handle.net/11090/174 2008;1:53. (8 June 2017, date last accessed). RETRACTED http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Epidemiology Oxford University Press

Rapidly increasing body mass index among children, adolescents and young adults in a transitioning population, South Africa, 2008–15

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Oxford University Press
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Copyright © 2022 International Epidemiological Association
ISSN
0300-5771
eISSN
1464-3685
DOI
10.1093/ije/dyx263
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Abstract

Background: There is a global epidemic of overweight and obesity; however, this rate of increase is even greater in some low- and middle-income countries (LMIC). South Africa (SA) is undergoing rapid socioeconomic and demographic changes that have triggered a rapid nutrition transition. The paper focuses on the recent rate of change of body mass index (BMI) among children, adolescents and young adults, further stratified by key soci- odemographic factors. Methods: We analysed mean BMI of 28 247 individuals (including children) from 7301 households by age and year, from anthropometric data from four national cross- sectional (repeated panel) surveys using non-linear fitted curves and associated 95% confidence intervals. Results: From 2008 to 2015, there was rapid rise in mean BMI in the 6–25 age band, with the highest risk (3–4þ BMI unit increase) among children aged 8–10 years. The increase was largely among females in urban areas and of middle-high socioeconomic standing. Prominent gains were also observed in certain rural areas, with extensive geographical heterogeneity across the country. Conclusions: We have demonstrated a major deviation from the current understanding of patterns of BMI increase, with a rate of increase substantially greater in the developing world context compared with the global pattern. This population-wide effect will have major consequences for national development as the epidemic of related non- communicable disease unfolds, and will overtax the national health care budget. Our refined understanding highlights that risks are further compounded for certain groups/ V The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 2 International Journal of Epidemiology, 2017, Vol. 0, No. 0 places, and emphasizes that urgent geographical and population-targeted interventions are necessary. These interventions could include a sugar tax, clearer food labelling, revised school feeding programmes and mandatory bans on unhealthy food marketing to children.The scenario unfolding in South Africa will likely be followed in other LMICs. Key words: Body mass index, rapid gain, nutritional transition, children, adolescents, young adults, South Africa Key Messages South Africa is experiencing exponential increases in population mean BMI within a relatively short time period , par- ticularly among female school-aged children, adolescents and young adults. The increase in mean BMI in the 8–10 year age group over a short period (7 years) is 6–7-fold higher than global BMI increases reported between 1980 and 2008. The largest mean increases in BMI over the period show strong geographical heterogeneity, with the increase great- est in urban settlements (both informal and formal) and in middle/high socioeconomic groups. Our results highlight the need for close investigation in other LMIC, where similar patterns may exist, and where there may also be unexpected increases of BMI and subsequently future explosions in non-communicable diseases, with concomitant negative outcomes for life expectancy, and health care costs. Our results demonstrate a rapid BMI change in specific age groups, thus providing a basis for public health interven- tions specifically targeting geographical areas and/or population segments, as well as potentially some more general innovative policy measures. 2 2 Introduction 0.5 kg/m per decade for women. The cost of obesity in national health budgets has risen markedly, and it has been Globally, there is a rapidly rising trend in body mass index 1–3 estimated that the cost of obesity/overweight has increased (BMI) and overweight and obesity in adults, and child- from 6.5% to 9.1% of annual medical spending in national hood obesity has emerged as one of the most serious public health budgets. A combination of these concerns regard- health issues of the 21st century. Epidemiological studies ing the potential health and economic burden of increasing have shown a substantial increase in the risk of disease BMI, has led to adiposity being included among the global with elevated BMI (i.e. severe or morbid obesity). The non-communicable disease (NCD) targets, namely to halt current and future risks of overweight/obesity include a the rise in obesity prevalence by 2025 compared with 2010 range of obesity-related non-communicable disorders, 14,15 levels. Significant health benefits and cost savings, including cardiovascular and kidney diseases, diabetes, therefore, could be achieved by reducing overweight/obes- early onset metabolic syndrome, hypertension, many can- 6–10 ity in children and adolescents as part of a programme to cers and musculoskeletal disorders. The Global BMI reduce the population distribution of BMI. Thus, infor- Mortality Collaboration has examined the relationship be- mation on whether countries are on track to achieve this tween body mass index (BMI) and all-cause mortality, target is needed to support accountability towards the glo- using individual participant data meta-analyses of 189 pro- bal NCD commitments. spective studies, involving a total of 3 951 455 partici- Southern Africa, and particularly South Africa (middle- pants. The association between both overweight and income), is undergoing rapid socioeconomic, epidemiologi- obesity with higher all-cause mortality was consistently cal and lifestyle transitions that have precipitated a rapid demonstrated across all settings. In recent years, increas- ing efforts have been made to assess BMI trends both nutritional transition. These transitions underpin the aeti- 1,12 within and across nations. A systematic review and ology of a rapidly rising trend in BMI and waist circumfer- ence (WC), as evidenced by previous overweight/obesity meta-analysis of epidemiological studies, carried out in 18,19 199 countries over three decades, indicated that in all but a studies and their associated determinants. Evidence few countries, the average BMI of the adult population from 19 other South African studies suggests a far higher increased between 1980 and 2008. The average rate of increase in childhood/adolescent obesity than the global 2 2 increase was 0.4 kg/m per decade for men and averages of 0.4 kg/m /decade for men and 0.5 kg/m /decade RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 3 for women. The rapid changes in obesity in South Africa given the confounding impact of pregnancy on weight and indicate that in only 6 years (between 2002 and 2008) ado- waist circumference. lescent obesity rates doubled, whereas in the USA this took twice as long (13 years). It can be hypothesized that this Outcome increase is progressing at a far greater rate than that observed globally both in LMIC and high-income countries BMI for adults and BMI-for-age z-scores [or equivalent (HIC), and there is a need to quantify this rate of increase BMI-for-age percentile] for children using the latest World in specific populations longitudinally. Health Organization (WHO) growth reference stand- Furthermore, previous studies have focused on adults ards. Overweight and obese cut-offs for BMI among and dichotomized obesity classification (rather than under- adults were set at 25–29.9 and 30þ kg/m , based on inter- lying BMI and WC distribution), thus neglecting the key national cut-offs. We also included additional analysis child and adolescent groups which are often more sus- of waist circumference (WC), which is presented as ceptible to rapid transitions. The aim of this study was to supplementary material (available as Supplementary data use longitudinal national survey data of anthropometric at IJE online). measurements to quantify the rapidly changing BMI distri- bution by age for the 6- to 25-year-old population group in Sociodemographic variables South Africa. We also show the rate of change of BMI from 2008 to 2015 across a range of demographic factors Sociodemographic variables include: that include gender, population group, socioeconomic sta- panel year (four survey waves); tus, rural-urban divide and geographical districts in South age (2–64 years); Africa. gender; ethnicity, classified as Black/African and non-African (Coloured, Indian/Asian, Caucasian); Methods provincial districts of South Africa; Data geotype; socioeconomic status (household income) classified into Data were taken from the four panel (cross-sectional) waves low [mean South African rand (ZAR) of 681 per month, of the South African National Income Dynamics Study (SA- 23 range of 1 to 1000], medium (mean ZAR of 1516 per NIDS), the first national panel study in South Africa. SA- month, range ZAR > 1000 to 2000) and high tertiale NIDS was undertaken by the South African Labour and (mean ZAR of 7017 per month, range > 2000 to Development Research Unit based at the School of 300,000) categories. Economics at the University of Cape Town. The surveys took place in 2008, 2010–11, 2012 and 2014–15. These are There are four major geotypes in South Africa, two named waves 1–4, respectively. A stratified, two-stage ran- rural, namely rural formal and tribal authority areas dom cluster sample design was employed to sample house- (TAAs), and two urban, namely urban formal and urban holds for inclusion at baseline, and proportionally allocated informal: stratification was based on the 52 district councils (DCs) in rural: ‘farms’; South Africa. Within each DC [primary sampling unit TAAs: ‘communally-owned land under the jurisdiction (PSU)], clusters of dwelling units were systematically drawn. of traditional leaders. Settlements within these areas are A detailed report on the methodology employed in this study villages’; is provided elsewhere. The household-level response rate urban: ‘a continuously built-up area that is established was 69% and the individual response rate within households through township establishment such as cities, towns, was 93%. The baseline SA-NIDS survey provides data for ‘townships’, small towns and hamlets’; 28 247 individuals (including children) from 7301 house- formal residential: ‘single houses, town houses, high- holds. Weight and height measurements were taken for all rise flats, scheme housing, estates’; individuals, as well as waist circumference measurements for informal residential: ‘illegal informal structures’. all adults (aged 18þ) in the household. Data analysis Study population Analyses were performed using Stata software version 13 We restricted our analysis to individuals aged 5 to 79 (StataCorp. 2013; Stata Statistical Software, Release 13, years. Pregnant women were excluded from the analysis College Station, TX]. Clustering, as well as survey design RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 4 International Journal of Epidemiology, 2017, Vol. 0, No. 0 effects, were accounted for using sample weights to cor- Table 1. Baseline characteristics of the study population rectly estimate standard error, and hence 95% confidence Characteristic n (N ¼ 21024) % intervals (CIs) around BMI point estimates. Mean BMI (and standard deviation) or median (and Gender: Male 9174 43.6 interquartile range) by age and survey round were further Female 11849 56.4 stratified by other sociodemographic variables such ethni- Unknown 1 0.0 city and gender, socioeconomic status and geotype; 95% Age group (years): confidence intervals were also calculated around point Unknown 21 0.1 estimates. < 5 2074 9.9 As the distribution of mean BMI by age was non-linear, 5–9 2482 11.8 we fitted non-linear smoothed curves using a mathematical 10–14 2664 12.7 growth curve proposed by Preece and Baines. Fitted BMI 15–24 4357 20.7 25–34 2613 12.4 curves by age and survey round were also stratified by the 35–44 2290 10.9 aforementioned sociodemographic characteristics, to visu- 45–54 1903 9.1 ally highlight changes in specific age bands by period and 55–64 1329 6.3 demographic characteristics. 65þ 1291 6.1 Ethnicity: Black/African 17309 82.3 BMI-for-age z-scores and abnormal levels among children Coloured 2678 12.7 aged 2–18 years Asian/Indian 244 1.2 We calculated BMI-for-age z-scores and BMI categories Caucasian 793 3.8 for children ages 2 to 18, using the WHO 2007 reference Geotype: standards based on height, weight, gender and age in Rural 2069 9.8 months. BMI-for-age z-scores were generated using the Tribal authority areas 9966 47.4 ‘zanthro’ and ‘zbmicat’ commands in Stata. A z-score Urban (formal) 7744 36.8 Urban (informal) 1245 5.9 ofþ 1 to 1.99 is classified as overweight, and a score of 2þ is classified as obese. Additional supplementary analyses of The following cut-offs for central overweight/obesity based on waist cir- waist circumference among adults were conducted and cumference were used for males (> 94 cm) and females (> 80cm), as currently specified in international guidelines. included in Figure A1 and Table A3, available as Supplementary data at IJE online. A waist circumference Four waves of data from 2008 to 2015 are used to show greater than 94 cm among men, and greater than 80 cm the distribution of mean BMI by age (5–79 years) in 28,29 among women, was classified as overweight/obese. Figure 1 (also see Table A1). A rapid rise in mean BMI was observed from young childhood, with a plateau around Mapping/Geographic Information Score 40–50 years of age, and thereafter a decline towards eld- Maps depicting change in BMI-for-age z-score and BMI by erly age. An increase was observed among adults aged district among children (aged 2–18 years) and adults 26–57, with a linear decrease in the gain in this group with (19–64 years), respectively, from 2008 to 2014/15 were de- increasing age. Overall, the mean gain in BMI among veloped using Map Info Professional. males from waves 1 to 4 was þ0.5 units (95% CI: 0.1 toþ 1.1) and þ1.7 units (95% CI: þ1.1 toþ 2.3) among females. Results From 2008 to 2015 there was a mean þ2 BMI increase among young children (especially aged 8–10) to young The baseline sample for all age groups in South Africa adulthood. This increase (comparing wave 4 with wave 1 (2008) comprised 21 024 subjects (out of a total of in Figure 1) in the 6–25 years age band (captured between 28 247) with anthropometric data. The baseline sample the orange lines) was statistically significant (Figure 2). comprised: 3254 (11.5%) children under 5; 4355 (15.4%) The largest gains in this age group were among the 8–10- children between the ages of 5 and 11 years; 4487 (15.9%) year-olds, with a 3 to 4 mean BMI gain over the 7-year adolescents between the ages of 12 and 18 years; 5172 period. (18.3%) young adults aged 19–29 years; 4470 (16.9%) We further unpacked the change in the proportion of middle-aged adults (30–44 years); 4333 (15.4%) adults subjects that were overweight and/or obese by age and sur- aged 45–64 years; and 1779 (6.3%) elderly adults (65þ vey wave (Figure 3). A pronounced rise (‘hump’) in the years of age) (Table 1); 76 subjects had a missing age. The proportion of children aged 5–12 who were overweight or majority of subjects were African (n ¼ 17 309, 82.3%). RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 5 Figure 1. Distribution of BMI by age and survey round. (Note: thicker lines represent the fitted non-linear curves for each wave of the same colour). Figure 2. Change in BMI by age from 2008 to 2014–15 with 95% CIs for change included. (Note: orange band highlights age bands with significant in- creases in mean BMI from 2008 to 2014–15, and grey region highlights adult age bands with increased BMI from 2008 to 2014–15, but which were not statistically significant due to low sample size). obese (BMI-for-age z-scoreþ1) was observed when com- urban-rural groups among the 8–25-year-old population, paring 2014–15 with 2008. A consistent, but less pro- but most prominently among younger female children nounced, rise in the proportion of adults (aged 19þ) who aged 8–10 years living in middle- to high-income house- were overweight and/or obese was observed when compar- holds in urban formal areas, and among lower-income ing the later survey round with the baseline (Figure 3b). households in urban informal settlements. The largest A further stratification of the change in BMI by age, gains in BMI in younger children occurred in the middle gender and ethnicity suggests that the major contributor to socioeconomic status (SES) group, followed by high SES the increasing mean BMI in younger age groups was (Figure 4b). Notably there were also BMI gains in the low among African and non-African females (Figure 4a). Gain SES group, among children aged 8–12 years. The largest in mean BMI was observed across all socioeconomic/ gains by geotype occurred in the urban sector (formal and RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 6 International Journal of Epidemiology, 2017, Vol. 0, No. 0 Figure 3. Proportion of overweight and/or obese by age and survey round. informal) in the 8–12-year age band (Figure 4c), which among in females. Medium SES households indicated the indicated aþ 4 BMI unit gain over the period. The rural highest gain in BMI in the 8–19 age band, with low SES sector did not display as large an increase in BMI in this households displaying the least gain in BMI between 5 and age band as compared with the formal sector; however, 21 years. Urban adolescents displayed greater gains in BMI there was a notable gain in BMI in the rural sector over the than rural adolescents, but between 16 and 19 years urban period (þ2 BMI units through most age bands). households in informal settlements had a lower BMI than Mean BMI change by district was heterogeneous across those in rural households. These results show that the rate the country (Figure 5; Table A2, available as Supplementary of mean BMI increase in South Africa over a 7-year period data at IJE online). The largest gains (þ2.5meanBMI in- was substantially higher than the global mean gain. crease from 2008 to 2015) were observed in parts of four of This rapid increase is especially problematic in LMICs the nine provinces, namely: KwaZulu-Natal, Free State, that are subject to both economic and lifestyle transi- North West and Limpopo. Surprisingly, the urban metropol- tions. The rapid growth in obesity is especially prevalent itan municipalities (Buffalo City, City of Cape Town, in sub-Saharan Africa (SSA), which has experienced a Ekurhuleni, eThekwini, City of Johannesburg, City of growth in gross domestic product (GDP), exposure to glo- Tshwane, Mangaung, and Nelson Mandela Bay) did not balization, greater disposable income and unprecedented register the largest gains in mean BMI over this period. levels of urban migration which result in the establishment of large urban informal settlements. A combination of these factors has driven a nutrition transition that refers to Discussion changing dietary patterns primarily driven by availability of cheap energy-dense foodstuffs, increased consumption In summary, the highest rate of BMI increase was among children aged 5 to 11 years, with a higher rate of BMI gain of saturated fats/animal proteins and growing sugary RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 7 Figure 4. Change in BMI for subjects aged 4–29 years of age (by 2-year age band) from 2008 to 2014–15. (Note: dashed lines are actual data and solid bold lines of same colour are fitted smooth non-linear curves). beverage consumption. Our results confirm rapidly rising population, as reflected in our results, exceeds those found 21,32 levels of obesity in South Africa, particularly in the urban in other South African studies. sector (formal and informal). It is significant, however, Gender appears to influence BMI gains in South Africa, that the rate of change of obesity in the younger especially in female school-aged children between 8 and RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 8 International Journal of Epidemiology, 2017, Vol. 0, No. 0 Figure 5. Map of mean BMI change by district from 2008 to 201–15 (please see Table A2 for codes and underlying data, available as Supplementary data at IJE online). 12 years of age. It is interesting to note, however, that an- ability to make an individual feel full (sated), thus resulting other South African study found that BMI for women in an ‘incomplete compensatory reduction in energy intake 2 2 40 increased from 25.8 kg/m in 1980 to> 29 kg/m in 2008 at subsequent meals after intake of liquid calories’. Despite (higher than the average global BMI gain over this period increased SSB consumption being a likely contributing factor for women ofþ 1.46), illustrating that BMI gain was for this rapid risk in BMI in this age group, it would however higher for females of all age categories. South African not operate in isolation, as the relationship between SSBs overweight/obesity differs markedly by gender, as a result and weight gain is likely confounded by the influences of of adolescent males having lower energy intakes and other dietary and lifestyle factors. engaging in higher levels of physical activity than adoles- Furthermore, overweight children and adolescents have 20,33 cent females. Behavioural and cultural phenomena a very high likelihood (70%) of becoming overweight often assume that a fat child is both healthy and happy, adults who have a significantly higher risk of non- and that they are HIV-negative. The Third South African communicable diseases35,13 that substantially reduce life National Youth Risk Behaviour Survey (YRBS) also re- expectancy outcomes and increase health care costs, as ported that only 49% of learners ate fresh fruit often, and well threatening the attainment of 2030 Sustainable only 40% ate at least a cup of vegetables a day. Other im- Development Goals. portant factors include a higher risk for urban adolescents The consequences of a sharp increase in non- because of relentless marketing, close proximity to low- communicable disease, as a result of the obesity epidemic, cost energy-dense foodstuffs and increasing disposable in- is likely to surpass smoking as a leading cause of disability- 20,34 43 come. Other potential factors include depression, adjusted years, A previous study demonstrated that the stress, noise pollution and insufficient sleep, all of which cost of care for major non-communicable diseases, such as are sometimes related to urbanization pressures, and espe- cardiovascular disease and diabetes, is and will be beyond 13,35 cially to metropolitan areas. the coping capacities of individuals, households and fami- Data on 9–10-year-old children from 12 countries, lies as well as governments (increased health care costs and including both high- and low-income groups within each lost GDP) in most African countries. The effect of the country, suggest that South African children drink sugar- increased burden on the health care systems is also likely to sweetened beverages and sports drinks more than four times overtax public and private health budgets, as well as in- per week, which was higher than any other country sur- crease externality costs and promote poorer public sector 36,37 veyed. South African adolescents, therefore, have among service. A study by Sturm et al. suggests that a rise in the the highest consumption of sugar sweetened beverages total expenditure per individual with increasing BMI from (SSBs) among children aged 9–10 years. Drinking one SSB a minimum of under ZAR 14,000 per individual in the /day increases adult likelihood of being overweight/obese by healthy BMI range of 20–24.9, and an increasing expend- 38–40 27% and child likelihood by 55%. SSBs lead to weight iture trend with acceleration when the BMI  30 to ZAR gain as result of their high added sugar content and low 17,000þ. Total expenditure for diabetes mellitus in RETRACTED Downloaded from https://academic.oup.com/ije/article/47/3/942/4745803 by DeepDyve user on 18 July 2022 International Journal of Epidemiology, 2017, Vol. 0, No. 0 9 South Africa was between ZAR 11.5 and 20 billion in in an increase of obesity of 16% by 2017, with 20% of this 2010 (7–12% of total health expenditure), and is predicted increase likely due to SSBs. In absolute terms, this translates to rise to ZAR 14.4–26.2 billion by 2030. into an increase of 280 000 cases, with most of this burden Beyond the direct health care cost, there is also the among young South Africans. Evidence from modelling in macro-economic impact due to increased body mass, South Africa suggests that a 20% sugary beverage excise tax which cannot be discounted. This operates through the (health promotion levy) would have prominent impacts on economic burden on households through lost wages of the growing obesity epidemic. Further cost-effective strat- prime-aged adults due to disability or death; and the cost egies include more stringent food advertising regulations, at of and further intensification of the poverty cycle, in al- a projected cost of ZAR 0.90 per person. To stem the tide ready vulnerable households. Overweight/obesity is also of the fast-growing obesity epidemic among South African likely to reduce the productive life of working individuals children and adolescents, there is urgency in passing manda- and negatively impact on local GDP though absenteeism. tory bans on unhealthy food advertising to children, and Interventions to mitigate against childhood/early adoles- regulating transparent front-of pack-food labelling, in add- cent obesity will result in major savings in public health ition to the aforementioned fiscal approaches. costs. ‘According to the first South African National Health The study has several limitations. First, smaller sample and Nutrition Examination Survey (SANHANES-1), it ap- sizes and hence less precision among smaller ethnic groups. pears that the most significant increase in waist For example, few data on the SA coloured population im- circumference, as well as overweight and obesity, occur be- pacted on our ability to assess change in BMI by age group. tween 15 and 35 years of age. As this represents childbear- Second, lower response as well as greater attrition rates ing age, it may be the perfect window of opportunity to were observed in certain social strata in the NIDS survey. intervene, not only to optimise the health of the mother but The highest attrition rates occurred in wealthier population also of her offspring’. It has been estimated that the cost groups (Whites/Asians/Indian), and the lowest attrition of obesity/overweight has increased from 6.5% to 9.1% of rates in poorer households (Africans/Coloured). These annual medical spending in national health budgets. differences between respondents and non-respondents were More balanced home-cooked meals, and changes to health- taken into account using adjusted sampling weights based ier foodstuffs in school feeding programmes and tuckshops, on observed characteristics. However, one still cannot ex- 13,20,35 are urgently needed. Our data suggest that whereas clude the possibility that unobserved differences might have rural BMI has increased, the rate of increase in urban infor- biased the results of our study. Third, a relatively high num- mal and formal sectors has been greater. This suggests that ber of missing or invalid weight/height measurements there is still time to target rural areas before the prevalence (20–25%) may have introduced selection bias. Fourth, of overweight and obesity increases to ‘urban’ levels, and despite extensive interviewer training and standardization before these individuals migrate to urban areas; such tar- of study protocol, we cannot discount the effect of inter- geted prevention may be most efficient, and could have a observer variability across the different study districts. significant impact on the future obesity in South Africa. The twin scourge of undernutrition and obesity in children Conclusions and simultaneous challenge of rapid urbanization is par- ticularly prevalent in many developing countries, and in- We have demonstrated a major deviation from the current novative policy should include community-level lifestyle understanding of patterns of BMI increase, in that the rate interventions, school-based initiatives, local transport and of increase is substantially greater in the South African urban planning, physical activity and media campaigns that context than the global pattern. This refined understanding are supported by broader interventions to focus on food of BMI changes in a range of demographic and geo- marketing for children, nutritional labelling and legal (fis- graphical variables provides opportunity for more innova- cal) action. These types of policy must ensure that they tive (tailored) intervention programmes. simultaneously target both rich and poor communities. Fiscal measures (such as taxes ) represent the most cost- effective approach per head at ZAR 0.20 (in 2010). South Supplementary Data African children have an especially high consumption of Supplementary data are available at IJE online. SSBs relative to other LMIC and HIC.33,34 This is likely part of the explanation for the high observed risk in mean Acknowledgements BMI over the period in the 8–10 age demographic, based on The South African National Income Dynamics Study (SA-NIDS) our findings. 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Journal

International Journal of EpidemiologyOxford University Press

Published: Jun 1, 2018

Keywords: body mass index procedure; adolescent; child; south africa; young adult

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