Article

Estimating the burden of disease attributable to excess body weight in South Africa in 2000

University of Cape Town, Kaapstad, Western Cape, South Africa
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde (Impact Factor: 1.63). 08/2007; 97(8 Pt 2):683-90.
Source: PubMed

ABSTRACT

To estimate the burden of disease attributable to excess body weight using the body mass index (BMI), by age and sex, in South Africa in 2000.
World Health Organization comparative risk assessment (CRA) methodology was followed. Re-analysis of the 1998 South Africa Demographic and Health Survey data provided mean BMI estimates by age and sex. Population-attributable fractions were calculated and applied to revised burden of disease estimates. Monte Carlo simulation-modeling techniques were used for the uncertainty analysis.
South Africa.
Adults >or= 30 years of age.
Deaths and disability-adjusted life years (DALYs) from ischaemic heart disease, ischaemic stroke, hypertensive disease, osteoarthritis, type 2 diabetes mellitus, and selected cancers.
Overall, 87% of type 2 diabetes, 68% of hypertensive disease, 61% of endometrial cancer, 45% of ischaemic stroke, 38% of ischaemic heart disease, 31% of kidney cancer, 24% of osteoarthritis, 17% of colon cancer, and 13% of postmenopausal breast cancer were attributable to a BMI >or= 21 kg/m2. Excess body weight is estimated to have caused 36,504 deaths (95% uncertainty interval 31,018 - 38,637) or 7% (95% uncertainty interval 6.0 - 7.4%) of all deaths in 2000, and 462,338 DALYs (95% uncertainty interval 396,512 - 478,847) or 2.9% of all DALYs (95% uncertainty interval 2.4 - 3.0%). The burden in females was approximately double that in males.
This study shows the importance of recognizing excess body weight as a major risk to health, particularly among females, highlighting the need to develop, implement and evaluate comprehensive interventions to achieve lasting change in the determinants and impact of excess body weight.

Full-text

Available from: Julia Goedecke
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For many decades underweight and undernutrition have
dominated public health concerns around nutrition and health.
It is only during the past 10 years that the health problems
associated with overweight and overnutrition have gained
global recognition.
1
This is despite rising trends in obesity
being widely observed in many regions over the past 25
years;
2,3
Breslow’s classic article (1952) that raised concern
about substantial proportions of overweight Americans and
drew attention to the ‘close association’ between overweight
and excessive mortality from several chronic diseases;
4
and 80-
year-old evidence of individuals issued with insurance during
1925 - 1934 being charged higher premiums because of being
overweight.
4
Historically, excess body weight has been regarded as a
‘Western’ problem associated with affluence, but it is now
also recognised as a leading risk factor for disease in middle-
income countries, and is of emerging importance in low-
income countries.
5
In a study
6
examining patterns of adult
female overweight and underweight in developing regions,
overweight was found to exceed underweight in more than
half of the world’s developing countries. Global trends
in diet, moving from more traditional diets to those with
increased refined foods, high in free sugar and saturated fat,
in combination with reduced physical activity,
2
are believed
to have led to a considerable obesity epidemic. Globally
at present, an estimated 1.1 billion adults are overweight
1
(including 312 million who are obese
1
), accounting for
approximately 26% of the world population.
7
The 1998 South Africa Demographic and Health Survey
(1998 SADHS)
8
provided the first nationally representative
anthropometric data measured in adults. In a region of the
world where obesity is uncommon,
1
high levels of excess body
weight were observed among South Africans, particularly
women. The mean body mass indexes (BMIs) in adult
women and men 15 years were 27.3 kg/m
2
and 23.4 kg/m
2
respectively. High proportions of adult women (56%) and men
(29%) were overweight or obese. Some of the poorer provinces
had similarly high rates, with the lowest observed in Limpopo
(44% of women and 22% of men). The prevalence of obesity
was particularly high among women (30%), being higher in the
urban (33%) than non-urban (25%) areas.
8
Estimating the burden of disease attributable to excess body
weight in South Africa in 2000
Jané Joubert, Rosana Norman, Debbie Bradshaw, Julia H Goedecke, Nelia P Steyn, Thandi Puoane and the South African
Comparative Risk Assessment Collaborating Group
Burden of Disease Research Unit, South African Medical Research Council,
Tygerberg, Cape Town
Jané Joubert, MA, MA
Rosana Norman, PhD
Debbie Bradshaw, DPhil (Oxon)
University of Cape Town/South African Medical Research Council Research Unit for
Exercise Science and Sports Medicine, Department of Human Biology, Newlands,
Cape Town
Julia H Goedecke, PhD
Chronic Diseases of Lifestyle Unit, South African Medical Research Council,
Tygerberg, Cape Town
Nelia P Steyn, PhD
School of Public Health, University of the Western Cape, Bellville, Cape Town
Thandi Puoane, PhD
Corresponding author: J Joubert (jane.joubert@mrc.ac.za)
Objective. To estimate the burden of disease attributable to
excess body weight using the body mass index (BMI), by age
and sex, in South Africa in 2000.
Design. World Health Organization comparative risk
assessment (CRA) methodology was followed. Re-analysis of
the 1998 South Africa Demographic and Health Survey data
provided mean BMI estimates by age and sex. Population-
attributable fractions were calculated and applied to revised
burden of disease estimates. Monte Carlo simulation-modelling
techniques were used for the uncertainty analysis.
Setting. South Africa.
Subjects. Adults 30 years of age.
Outcome measures. Deaths and disability-adjusted life years
(DALYs) from ischaemic heart disease, ischaemic stroke,
hypertensive disease, osteoarthritis, type 2 diabetes mellitus,
and selected cancers.
Results. Overall, 87% of type 2 diabetes, 68% of hypertensive
disease, 61% of endometrial cancer, 45% of ischaemic stroke,
38% of ischaemic heart disease, 31% of kidney cancer, 24% of
osteoarthritis, 17% of colon cancer, and 13% of postmenopausal
breast cancer were attributable to a BMI 21 kg/m
2
. Excess
body weight is estimated to have caused 36 504 deaths (95%
uncertainty interval 31 018 - 38 637) or 7% (95% uncertainty
interval 6.0 - 7.4%) of all deaths in 2000, and 462 338 DALYs
(95% uncertainty interval 396 512 - 478 847) or 2.9% of all
DALYs (95% uncertainty interval 2.4 - 3.0%). The burden in
females was approximately double that in males.
Conclusions. This study shows the importance of recognising
excess body weight as a major risk to health, particularly
among females, highlighting the need to develop, implement
and evaluate comprehensive interventions to achieve lasting
change in the determinants and impact of excess body weight.
S Afr Med J 2007; 97: 683-690.
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Some similarities but also some diversity in dietary intake
exist among the country’s historically defined four population
groups. Whites, Indians and coloureds were found to consume
a typical Western diet, with high fat (> 30% of energy intake
(E)), low carbohydrate (< 55% E), low fibre and high free
sugar intake (> 10% E).
9,10
Among black Africans, there are
two distinct types of eating patterns: the rural population still
follows a mainly traditional diet high in carbohydrates (> 65%
E), low in fat (< 25% E) and sugar (< 10% E), and moderately
high in fibre,
11
whereas urban black Africans have adopted the
Western diet pattern.
12
Lack of data on trends in overall energy
intake and diet make it difficult to assess their role in the
high levels of excess body weight. However, the low levels of
physical activity among South African adults must be expected
to contribute to excess body weight.
13
It is widely acknowledged that excess body weight is
associated with increased risk of disease.
14
Obesity has been
specified by the International Statistical Classification of
Diseases as a disease in its own right.
15
The World Health
Organization (WHO) Comparative Risk Assessment Study
(Global CRA)
15
estimates that in adults aged 30 years,
increases in BMI above 21 kg/m
2
are associated with an
estimated 58% of type 2 diabetes mellitus (T2DM), 21%
of ischaemic heart disease (IHD), 39% of hypertensive
disease, 23% of ischaemic stroke, 12% of colon cancer, 8% of
postmenopausal breast cancer, 32% of endometrial cancer,
and 13% of osteoarthritis. The disease burden associated with
excess body weight has not previously been investigated
in South Africa. This study therefore aimed to estimate the
burden of disease attributable to excess body weight by age
and sex in South Africa for 2000.
Methods
WHO CRA methodology
2,16
was used to estimate the disease
burden attributable to this particular risk factor by comparing
current local health status with a theoretical minimum
counterfactual with the lowest possible risk. The population-
attributable fraction (PAF) was determined by the prevalence
of exposure to the risk factor in the population and the relative
risk (RR) of disease occurrence given exposure. Exposure
to excess body weight was measured using the BMI, which
standardises weight according to height.
Through analysis of numerous datasets and critical
assessments of studies of BMI-associated health hazards, James
et al.
15
concluded that a universal mean BMI of 21 kg/m
2
be
used as optimal for both sexes throughout the world. This is
similar to the lower limit of the normal weight range (21.0 -
23.0 kg/m
2
) proposed by the WHO Technical Consultation on
Obesity.
15
The theoretical minimum risk distribution of BMI
was assumed to follow a normal distribution with 21.0 ± 1.0
kg/m
2
(mean ± standard deviation (SD)). The BMI distribution
in the South African population was assumed to be normal
with parameters obtained from the 1998 SADHS for each age
and sex group.
8
There are health hazards associated with both
low and high BMIs,
17
but this study is only concerned with
risks of high BMI, or excess body weight.
Associated health outcomes quantified in our study were
those for which sufficient causal evidence was found in
the Global CRA,
15
and are listed in Table I. A number of
conditions likely to be causal, including gallbladder cancer,
dermatitis, menstrual disorders, infertility, breathlessness, back
pain, gallstones, and psychological effects such as reactive
depression and social isolation,
15,18
were not quantified because
of lack of sufficient evidence of the magnitude of the hazardous
effect, or difficulty of comparability on an international basis.
2,15
The RRs, also presented in Table I, were obtained from reviews
and meta-analyses by the high BMI expert group of the Global
CRA.
15
For cardiovascular risks, this drew substantially on
the meta-analysis done by the Asia-Pacific Cohort Studies
Collaboration (APCSC)
19
using 33 cohorts with over 310
000 participants. For our study, hazard ratios were obtained
from re-analyses of the APCSC data, reflecting a smoother
estimate of the attenuation of risks across age (S Vander
Hoorn, University of Auckland, New Zealand – personal
communication, 2005). A recent APCSC study indicates lower
RRs per BMI unit for diabetes than what was used in the
Global CRA. These, however, were not used in this analysis as
they were derived for incidence of T2DM.
20
Customised MS Excel spreadsheets based on templates
used in the Clinical Trial Research Unit at the University of
Auckland (S Vander Hoorn – personal communication, 2005)
as well as Australian studies (T Vos, University of Queensland,
Australia – personal communication, 2005) were used to
calculate the attributable burden using a discrete version of
the general potential impact fraction (see below), taking into
account continuous risk factor disease exposures compared
with a theoretical minimum distribution (conferring the lowest
possible risk) on a categorical scale.
where n = the number of exposure categories; P
i
= the
proportion of the population in exposure category i; RR
i
= the RR for exposure category i; and P’
i
= the proportion
of population in exposure category i in the counterfactual
distribution. Calculations for categories of single BMI units
were done.
The PAFs were applied to the revised estimates of the
burden of disease in South Africa for the selected health
outcomes, measured in deaths, years of life lost (YLL), years
lived with disability (YLD), and disability-adjusted life years
(DALYs).
22
Not all health outcomes of the Global CRA match
the conditions in the South African National Burden of Disease
study (see explanatory note at bottom of Table III). In addition,
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total stroke deaths, YLL, YLD and DALYs were adjusted by
the proportion of fatal and non-fatal ischaemic stroke by age,
using stroke subtype data for AFR-E,
23
since the South African
estimates do not distinguish between these subtypes.
Monte Carlo simulation-modelling techniques were used
to present uncertainty ranges around point estimates that
reflect all the main sources of uncertainty in the calculations.
We used the @RISK software 4.5 for Excel,
24
which allows
multiple recalculations of a spreadsheet, each time choosing a
value from distributions defined for input variables. A normal
distribution was specified around mean BMI and standard
errors (SEs) by age and sex. For the RR input variables we
specified a normal distribution, with the natural logarithm of
the published RR estimates as the mean and the SEs calculated
from the natural logarithms of the 95% confidence intervals
(CIs). SEs from the re-analyses of the APCSC data were used
for the cardiovascular outcomes (S Vander Hoorn, University
of Auckland, New Zealand – personal communication, 2005).
For osteoarthritis and T2DM, 95% CIs were estimated using
the variation or the difference in the excess risk, relative to
the size of the excess risk for related outcomes for which
these data were available. For each of the output variables
(namely attributable burden as a percentage of total burden in
South Africa, 2000), 95% uncertainty intervals were calculated
bounded by the 2.5th and 97.5th percentiles of the 2000
iteration values generated.
Results
The 1998 SADHS
8
data show a wide distribution of observed
BMI values for men and women (Fig. 1), with high proportions
of the population having values higher than the optimal mean
of 21.0 kg/m
2
. Table II shows that the mean BMI (± SE) for both
men and women in each age category was well above the level
of 21 kg/m
2
, and declined with increasing age. The mean BMI
for adults 30 years was 28.7 ± 0.14 kg/m
2
for women and 24.1
± 0.11 kg/m
2
for men. According to WHO classifications, 27.3%
of men and 29.1% of women 30 years were overweight (25 kg/
m
2
BMI < 30 kg/m
2
), and 11.0% of men and 38.6% of women
were obese (BMI 30 kg/m
2
).
Attributable fractions for all diseases were higher for women
than men (Table III). For T2DM and cardiovascular outcomes,
PAFs were highest in the 30 - 44-year age group and decreased
with advancing age, while cancer-related outcomes and
osteoarthritis in males and females peaked in the 45 - 59- and 60
- 69-year age groups respectively. PAFs for hypertensive disease
were considerably higher than for IHD and ischaemic stroke.
Table II. Mean ± standard errors of BMI (kg/m
2
) by age and sex from the 1998 South Africa Demographic and Health Survey
30 - 44 yrs 45 - 59 yrs 60 - 69 yrs 70 - 79 yrs 80+ yrs 30+ yrs
Males 24.3 ± 0.2 25.3 ± 0.3 24.5 ± 0.3 24.7 ± 0.4 22.4 ± 0.6 24.1 ± 0.11
Females 28.6 ± 0.2 29.5 ± 0.2 29.5 ± 0.3 27.5 ± 0.5 25.9 ± 0.9 28.7 ± 0.14
i=0
Table I. Health outcomes and relative risks associated with 1 kg/m
2
increase in BMI by age and sex
Health outcome and Males (age in years) Females (age in years)
ICD 10 Code
21
30 - 44 45 - 59 60 - 69 70 - 79 80+ 30 - 44 45 - 59 60 - 69 70 - 79 80+
Ischaemic heart disease* 1.13 1.09 1.07 1.05 1.02 1.13 1.09 1.07 1.05 1.02
(I20-I25)
Hypertensive disease* 1.22 1.17 1.14 1.11 1.07 1.22 1.17 1.14 1.11 1.07
(I10-I13)
Ischaemic stroke* 1.14 1.10 1.07 1.05 1.03 1.14 1.10 1.07 1.05 1.03
(I63)
Type 2 diabetes mellitus 1.36 1.24 1.18 1.27 1.27 1.47 1.34 1.21 1.20 1.20
(E11)
Osteoarthritis 1.04 1.04 1.04 1.04 1.04 1.04 1.04 1.04 1.04 1.04
(M15-M19)
Breast cancer 1.00 1.00 1.00 1.00 1.00 1.00 1.03 1.03 1.03 1.03
(C50)
Colon cancer 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03
(C18)
Endometrial cancer 1.00 1.00 1.00 1.00 1.00 1.10 1.10 1.10 1.10 1.10
(C54-C55)
Kidney cancer 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06
(C64)
Source: Adapted from James et al., 2004.
15
*Cardiovascular outcomes: Re-analysis of the APCSC including data from 33 cohorts from 8 Asia-Pacific countries, excluding the first 3 years of follow-up, and adjusted for age, sex,
cohort and smoking habits (Stephen Vander Hoorn, University of Auckland, New Zealand – personal communication, 2005). T2DM: Japan (Yoshike, as cited in James et al., 2004
15
) and
Denmark (Drivsholm et al., as cited in James et al., 2004
15
).
Osteoarthritis: United States of America (Must et al., as cited in James et al., 2004
15
). Cancers: Europe (Bergström et al., as
cited in James et al., 2004
15
).
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PAFs were highest for T2DM, with
94% of the burden in females and
75% in males attributable to a BMI
above 21 kg/m
2
.
Applying the PAFs to the
estimates of burden, excess body
weight accounted for 11 579
male and 24 924 female deaths,
constituting 4.2% of total male and
10.1% of total female deaths in South
Africa in 2000 (Table IV). As most
excess body weight-related deaths
and non-fatal events occur in middle
and older age, the YLL, compared
with deaths, comprise a smaller
proportion of the total (2.4% of total
male YLL, 5% of total female YLL).
The proportion of DALYs due to
excess body weight was twice as
high in women (3.9%) than
men (1.9%). In females, T2DM
accounted for the highest number of
deaths (N = 7 620) and DALYs
(N = 96 098), followed by hyper-
tensive disease. In contrast, IHD
accounted for most attributable
deaths (N = 4 106) and DALYs
(N = 52 843) in males, followed
by T2DM and then hypertensive
disease. Cancers accounted for
4.4% of the total burden in females,
compared with 1.1% in males (Fig.
2). A large part of this difference is
likely accounted for because 2 of
the 4 related cancers analysed were
female-specific.
The age distribution of deaths
attributable to excess body weight
by disease outcome and sex are
presented in Fig. 3, highlighting the
higher total burden in women. In
men, most deaths occurred in the
45 - 59-year age group and then
declined with increasing age. In
women, deaths peaked in the 60 -
69-year age group and continued to
take a high toll in older age groups.
Table III. Population-attributable fractions (PAFs), expressed as a percentage, for selected health outcomes by age and sex, South Africa, 2000
Persons
Males (age in years) Females (age in years) (age in years)
Health outcome 30 - 44 45 - 59 60 - 69 70 - 79 80+ 30+ 30 - 44 45 - 59 60 - 69 70 - 79 80+ 30+ 30+
Ischaemic heart disease 44.7 40.3 24.0 18.1 4.2 32.3 68.3 59.4 47.2 29.1 11.2 45.0 37.7
Hypertensive disease 68.5 65.5 45.1 39.4 14.7 56.4 88.8 83.9 75.9 58.8 37.8 73.8 67.6
Ischaemic stroke 48.3 43.8 26.5 20.4 5.1 35.8 72.0 63.3 51.2 32.5 13.5 52.2 45.4
Type 2 diabetes mellitus 88.7 79.6 55.7 75.8 47.9 75.3 99.6 98.3 90.4 83.8 78.7 93.8 86.9
Osteoarthritis 15.3 18.4 14.8 15.7 7.9 16.5 28.1 30.4 30.8 25.3 21.0 29.1 24.0
Postmenopausal breast cancer - - - - - - - 15.6 23.8 19.3 15.8 13.3 13.2
Colon cancer 11.4 13.9 11.2 11.9 5.9 12.1 21.6 23.5 23.8 19.3 15.8 21.8 17.3
Endometrial cancer - - - - - - 59.0 62.3 63.1 55.3 48.5 60.5 60.5
Kidney cancer 22.8 27.2 21.7 23.1 11.8 24.3 39.9 42.8 43.3 36.5 30.9 40.8 31.1
Note: PAFs for colon cancer were applied to the combined colon and rectum cancer burden to match the South African National Burden of Disease 2000 list. PAFs for endometrial cancer were applied to the corpus uteri burden, and
PAFs for T2DM were applied to the total diabetes burden. PAFs for postmenopausal breast cancer in the 45 - 59-year age group were adjusted downwards by 33% to allow for premenopausal women in the age group.
Fig. 1. Distribution of BMI by age group in males and females 30 years, 1998 SADHS.
We created a graph excluding aggregated BMI intervals
Note: please don't tick smooth line option
BMI prevalence from South African Demographic and Health Survey 1998
Figure 1. Distribution of BMI by age group in males and females, SADHS 1998
SEX MALE
grouped
BMI
values in
kg/m
square
30-44 45-59 60-69 70-79 80+
<19 14.7% 13.2% 12.1% 13.8% 23.9%
19-<20 9.1% 6.8% 5.4% 7.8% 7.5%
20-<21 8.7% 7.6% 10.4% 6.0% 9.0%
21-<22 10.8% 9.3% 7.6% 7.4% 11.9%
22-<23 9.3% 7.3% 9.5% 9.7% 7.5%
23-<24 8.1% 6.3% 6.9% 9.2% 9.0%
24-<25 6.7% 8.6% 8.2% 8.8% 4.5%
25-<26 6.1% 5.9% 9.9% 6.9% 6.0%
26-<27 5.9% 6.1% 7.1% 5.5% 6.0%
27-<28 5.3% 7.2% 5.0% 6.5% 3.0%
28-<29 3.8% 5.8% 3.0% 3.2% 3.0%
29-<30 3.1% 2.9% 4.1% 1.4% 6.0%
30-<31 2.5% 3.4% 3.0% 2.8% 1.5%
31-<32 1.6% 3.2% 1.1% 3.7% 0.0%
32-<33 1.1% 2.2% 2.4% 2.8% 0.0%
33-<34 1.4% 1.3% 0.9% 0.9% 0.0%
34-<35 0.6% 1.0% 0.7% 1.4% 1.5%
35-<36 0.6% 0.4% 0.9% 0.9% 0.0%
36-<37 0.2% 0.3% 0.4% 0.0% 0.0%
37-<38 0.3% 0.7% 0.7% 1.4% 0.0%
38-<39 0.1% 0.1% 0.7% 0.0% 0.0%
39-<40 0.2% 0.0% 0.0% 0.0% 0.0%
40+ 0.1% 0.4% 0.2% 0.0% 0.0%
Group Total
100.0% 100.0% 100.0% 100.0% 100.0%
SEX FEMALE
grouped
BMI
values in
kg/m
square
30-44 45-59 60-69 70-79 80+
<19 5.2% 4.5% 6.1% 10.3% 12.2%
19-<20 3.1% 2.6% 3.7% 3.0% 6.5%
20-<21 4.5% 3.5% 5.2% 6.3% 5.7%
21-<22 5.4% 4.3% 4.1% 4.8% 5.7%
22-<23 5.3% 5.2% 5.6% 7.3% 8.9%
23-<24 5.6% 4.8% 5.6% 5.0% 4.1%
24-<25 6.8% 4.5% 6.0% 6.3% 5.7%
25-<26 6.8% 6.4% 5.2% 5.8% 6.5%
26-<27 5.4% 5.9% 5.6% 4.0% 4.9%
27-<28 5.9% 5.6% 5.3% 8.0% 7.3%
28-<29 6.8% 5.6% 4.0% 6.5% 4.1%
29-<30 5.2% 5.4% 5.1% 6.0% 3.3%
30-<31 5.4% 5.1% 5.8% 4.5% 4.9%
31-<32 4.3% 5.7% 4.8% 3.3% 5.7%
32-<33 3.6% 4.5% 4.4% 4.3% 1.6%
33-<34 3.1% 4.1% 5.1% 2.5% 0.8%
34-<35 2.3% 3.3% 2.3% 1.8% 0.0%
35-<36 2.4% 3.0% 2.1% 2.5% 3.3%
36-<37 2.3% 2.7% 2.0% 1.0% 0.8%
37-<38 2.2% 2.3% 2.3% 1.5% 0.8%
38-<39 1.8% 2.0% 1.9% 1.3% 0.0%
39-<40 1.6% 1.6% 0.7% 1.3% 3.3%
40+ 4.9% 7.5% 7.4% 3.3% 4.1%
Group Total
100.0% 100.0% 100.0% 100.0% 100.0%
Males
0%
2%
4%
6%
8%
10%
12%
14%
19-<20
20-<21
21-<22
22-<23
23-<24
24-<25
25-<26
26-<27
27-<28
28-<29
29-<30
30-<31
31-<32
32-<33
33-<34
34-<35
35-<36
36-<37
37-<38
38-<39
39-<40
BMI in kg/m
2
Proportion
Females
0%
2%
4%
6%
8%
10%
12%
14%
19-<20
20-<21
21-<22
22-<23
23-<24
24-<25
25-<26
26-<27
27-<28
28-<29
29-<30
30-<31
31-<32
32-<33
33-<34
34-<35
35-<36
36-<37
37-<38
38-<39
39-<40
BMI in kg/m
2
Proportion
30-44 45-59 60-69 70-79 80+
Note: Graphs exclude aggregated BMI intervals <19 and >40
We created a graph excluding aggregated BMI intervals
Note: please don't tick smooth line option
BMI prevalence from South African Demographic and Health Survey 1998
Figure 1. Distribution of BMI by age group in males and females, SADHS 1998
SEX MALE
grouped
BMI
values in
kg/m
square
30-44 45-59 60-69 70-79 80+
<19 14.7% 13.2% 12.1% 13.8% 23.9%
19-<20 9.1% 6.8% 5.4% 7.8% 7.5%
20-<21 8.7% 7.6% 10.4% 6.0% 9.0%
21-<22 10.8% 9.3% 7.6% 7.4% 11.9%
22-<23 9.3% 7.3% 9.5% 9.7% 7.5%
23-<24 8.1% 6.3% 6.9% 9.2% 9.0%
24-<25 6.7% 8.6% 8.2% 8.8% 4.5%
25-<26 6.1% 5.9% 9.9% 6.9% 6.0%
26-<27 5.9% 6.1% 7.1% 5.5% 6.0%
27-<28 5.3% 7.2% 5.0% 6.5% 3.0%
28-<29 3.8% 5.8% 3.0% 3.2% 3.0%
29-<30 3.1% 2.9% 4.1% 1.4% 6.0%
30-<31 2.5% 3.4% 3.0% 2.8% 1.5%
31-<32 1.6% 3.2% 1.1% 3.7% 0.0%
32-<33 1.1% 2.2% 2.4% 2.8% 0.0%
33-<34 1.4% 1.3% 0.9% 0.9% 0.0%
34-<35 0.6% 1.0% 0.7% 1.4% 1.5%
35-<36 0.6% 0.4% 0.9% 0.9% 0.0%
36-<37 0.2% 0.3% 0.4% 0.0% 0.0%
37-<38 0.3% 0.7% 0.7% 1.4% 0.0%
38-<39 0.1% 0.1% 0.7% 0.0% 0.0%
39-<40 0.2% 0.0% 0.0% 0.0% 0.0%
40+ 0.1% 0.4% 0.2% 0.0% 0.0%
Group Total
100.0% 100.0% 100.0% 100.0% 100.0%
SEX FEMALE
grouped
BMI
values in
kg/m
square
30-44 45-59 60-69 70-79 80+
<19 5.2% 4.5% 6.1% 10.3% 12.2%
19-<20 3.1% 2.6% 3.7% 3.0% 6.5%
20-<21 4.5% 3.5% 5.2% 6.3% 5.7%
21-<22 5.4% 4.3% 4.1% 4.8% 5.7%
22-<23 5.3% 5.2% 5.6% 7.3% 8.9%
23-<24 5.6% 4.8% 5.6% 5.0% 4.1%
24-<25 6.8% 4.5% 6.0% 6.3% 5.7%
25-<26 6.8% 6.4% 5.2% 5.8% 6.5%
26-<27 5.4% 5.9% 5.6% 4.0% 4.9%
27-<28 5.9% 5.6% 5.3% 8.0% 7.3%
28-<29 6.8% 5.6% 4.0% 6.5% 4.1%
29-<30 5.2% 5.4% 5.1% 6.0% 3.3%
30-<31 5.4% 5.1% 5.8% 4.5% 4.9%
31-<32 4.3% 5.7% 4.8% 3.3% 5.7%
32-<33 3.6% 4.5% 4.4% 4.3% 1.6%
33-<34 3.1% 4.1% 5.1% 2.5% 0.8%
34-<35 2.3% 3.3% 2.3% 1.8% 0.0%
35-<36 2.4% 3.0% 2.1% 2.5% 3.3%
36-<37 2.3% 2.7% 2.0% 1.0% 0.8%
37-<38 2.2% 2.3% 2.3% 1.5% 0.8%
38-<39 1.8% 2.0% 1.9% 1.3% 0.0%
39-<40 1.6% 1.6% 0.7% 1.3% 3.3%
40+ 4.9% 7.5% 7.4% 3.3% 4.1%
Group Total
100.0% 100.0% 100.0% 100.0% 100.0%
Males
0%
2%
4%
6%
8%
10%
12%
14%
19-<20
20-<21
21-<22
22-<23
23-<24
24-<25
25-<26
26-<27
27-<28
28-<29
29-<30
30-<31
31-<32
32-<33
33-<34
34-<35
35-<36
36-<37
37-<38
38-<39
39-<40
BMI in kg/m
2
Proportion
Females
0%
2%
4%
6%
8%
10%
12%
14%
19-<20
20-<21
21-<22
22-<23
23-<24
24-<25
25-<26
26-<27
27-<28
28-<29
29-<30
30-<31
31-<32
32-<33
33-<34
34-<35
35-<36
36-<37
37-<38
38-<39
39-<40
BMI in kg/m
2
Proportion
30-44 45-59 60-69 70-79 80+
Note: Graphs exclude aggregated BMI intervals <19 and >40
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Discussion
The prevalence of excess body weight in adult South Africans 30
years or older was high, particularly among women. Between 397
000 and 479 000 DALYs were attributable to excess body weight
in the year 2000. Compared with other risk factors investigated
in the South African CRA study, excess body weight ranked fifth
in terms of both deaths and DALYs. Among women, double
the number of deaths and DALYs were attributed to excess
body weight than in men. Excess body weight accounted for 1
in every 10 female deaths in the country. This gender difference
is generally not seen in developed countries, where mean BMI
tends to be similar in males and females. The proportions of
T2DM, cardiovascular conditions and selected cancers attributed
to excess body weight in South Africa are higher than global
estimates,
15
particularly in the women.
The present study found that the largest proportion of deaths
attributable to excess body weight occurred in the 45 - 59-year
age group. This results from the age structure of the population,
as well as the fact that the relationship between obesity and its
co-morbidities is generally stronger among younger adults (<
55 years of age).
25
It emphasises that young adults, who are less
likely to be conscious of and concerned about associated health
effects, need to be alerted to the early onset of such effects. The
attenuation of the RR with age is common, and is likely to be
Table IV. Burden of disease attributable to excess body weight in males, females and persons 30 years, South Africa, 2000
Males Females Persons
Health outcome Deaths YLLs YLDs DALYs Deaths YLLs YLDs DALYs Deaths YLLs YLDs DALYs
Colon cancer 124 1 221 62 1 283 277 2 431 168 2 598 402 3 652 230 3 881
Postmenopausal breast 0 0 0 0 498 4 968 379 5 347 498 4 968 379 5 347
cancer
Endometrium cancer 0 0 0 0 367 3 776 1 012 4 788 367 3 776 1 012 4 788
Kidney cancer 49 524 31 555 53 584 67 651 103 1 108 98 1 206
Type 2 diabetes mellitus 3 197 35 863 9 636 45 499 7 620 76 224 19 875 96 098 10 817 112 086 29 511 141 597
Ischaemic heart disease 4 106 49 370 3 472 52 843 4 980 48 628 5 100 53 729 9 086 97 999 8 573 106 571
Ischaemic stroke 1 845 21 769 5 050 26 819 4 255 44 563 10 920 55 483 6 099 66 333 15 969 82 302
Hypertensive disease 2 258 26 803 735 27 538 6 873 64 584 1 454 66 037 9 131 91 386 2 189 93 575
Osteoarthritis 0 0 6 358 6 358 1 1 16 709 16 711 1 1 23 068 23 069
Total burden 11 579 135 550 25 345 160 895 24 924 245 760 55 684 301 443 36 504 381 310 81 029 462 338
95% uncertainty interval
Upper 12 765 149 709 28 412 176 641 27 004 256 996 60 528 313 961 38 637 395 583 86 299 478 847
Lower 9 560 107 797 20 611 129 847 20 274 194 609 46 751 246 916 31 018 321 202 70 487 396 512
% of total burden 4.2% 2.4% 0.9% 1.9% 10.1% 5.0% 2.0% 3.9% 7.0% 3.6% 1.5% 2.9%
95% uncertainty interval
Upper 4.7% 2.6% 1.0% 2.1% 10.9% 5.2% 2.2% 4.1% 7.4% 3.7% 1.6% 3.0%
Lower 3.5% 1.9% 0.8% 1.5% 8.2% 4.0% 1.7% 3.2% 6.0% 3.0% 1.3% 2.4%
YLL = years of life lost (premature death); YLD = years lived with disability; DALYs = disability-adjusted life years.
Fig. 2. Burden of disease attributable to excess body weight in males and females 30 years, South Africa, 2000.
DALY pie chart
Osteoarthrit
Cancer Total
Males 45499 52843 26819 27538 6358 1838 160895
Females 96098 53729 55483 66037 16711 13385 301443
Attributable DALYs = 160 895
Males
Hypertensive
disease
17.1%
Osteoarthritis
4.0%
Ischaemic stroke
16.7%
Cancer
1.1%
Type 2 diabetes
28.3%
Ischaemic heart
disease
32.8%
Hyper
dise
21.
O
Ischa
Attributable DALYs = 301 443
Females
Hypertensive
disease
21.9%
Osteoarthritis
5.5%
Ischaemic stroke
18.4%
Cancer
4.4%
Type 2 diabetes
31.9%
Ischaemic heart
disease
17.8%
Attributable DALYs = 301 443
Females
Hypertensive
disease
21.9%
Osteoarthritis
5.5%
Ischaemic stroke
18.4%
Cancer
4.4%
Type 2 diabetes
31.9%
Ischaemic heart
disease
17.8%
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related to competing risks at older age, but further research
would be useful.
It is important to note that in South Africa, despite high levels
of excess body weight, undernutrition, including nutritional
deficiencies, is still prevalent, and remains a major cause of death
and disability in children.
26
Indeed, a complex picture emerges
of the co-existence of over- and undernutrition in the same
community, and even in the same household.
27
As childhood
obesity is a strong precursor of obesity in adulthood,
28
the
prevalence of excess body weight in South African children is of
growing concern. A recent national study in children aged 1 - 9
years showed the prevalence of underweight (< 2SD weight for
age) to be 12%, and stunting (< 2SD height for age) 22%, whereas
prevalence of combined overweight and obesity (age-adjusted
BMI > 25) was 17%, with higher prevalence in urban areas.
27
Among school-going adolescents aged 12 - 19 years in 2002, 7%
of male and 25% of female learners were overweight or obese.
29
US-based studies have shown that the fast-food industry
markets heavily to children and adolescents;
30
portion sizes and
the caloric content of fast foods have increased appreciably;
30
fast-food restaurants cluster in areas within a short walking
distance from schools;
30
snacks, fast foods and sweets dominate
food advertisements viewed by children;
31
and television
advertising influences food and beverage preferences, purchase
requests, and beliefs of children.
32
Research has shown that the
school environment plays a vital role in shaping children’s health
behaviours.
33
Many developed countries are responding, and
for example the European Charter on Counteracting Obesity
34
highlights that special attention is needed for children. However,
much less attention has been given to this in developing
countries, and South Africa clearly needs to give this some
consideration.
Global responses to address health threats associated with
excess body weight include the World Health Assembly’s Global
Strategy on Diet, Physical Activity and Health,
35
and the work
of the Public Health Approaches to the Prevention of Obesity
Working Group of the International Obesity Task Force (IOTF)
36
which has identified targets for action, action principles and
action recommendations for the prevention of obesity. In South
Africa the National Department of Health (DOH) has included
specific objectives in the Integrated Nutrition Programme aimed
at reducing obesity in the female population to 25% by 2007. The
DOH has also formulated a series of clinical guidelines for the
prevention and management of overweight, and has launched
the National Food-Based Dietary Guidelines.
37
Programmes
have been initiated in the private sector: major health insurers
have introduced wellness programmes that encourage ongoing
health-risk appraisal, including the measurement of BMI and
body fat levels. Continuing professional education on the
prevention and management of excess body weight has been
provided to doctors, nurses and allied health professionals.
37
However, schoolchildren are neglected as a target group, and
there are no obesity programmes yet at schools. Recently,
Government engaged in promoting physical activity for health.
The Department of Education and Department of Sport and
Recreation developed a policy framework on physical activity,
while the Ministry of Sport and Recreation issued a White
Paper and the Sport and Recreation Act, important instruments
promoting the theme of ‘Getting the nation to play’.
37
In addition,
physical activity for health has been promoted at a population
level through the ‘Vuka! South Africa, Move for your Health’
campaign initiated by the Department of Health.
An array of socio-economic, environmental, behavioural
and cultural factors contribute to increased levels of excess
body weight.
2
In South Africa one such cultural factor may be
the acceptance and perceived advantages of being overweight
among many black African women, associating an over-weight
body image with dignity, respect, wealth, strength, happiness and
health, as well as with being treated well by their husbands.
38,39
Weight loss associated with HIV and AIDS has added to the
complexity of perceived body image.
38
A recent study
40
among
female community health workers shows that lack of knowledge
on nutrition and the health risk of high fat intake in combination
with easy access to cheap, unhealthy food, particularly in urban
settings, limit the ability to make healthy food choices. These
studies highlight the importance of the Global Strategy
35
aimed
at developing an enabling environment for action that will lead
to more healthy diets and increased physical activity. They also
point to the need for research to identify reasons for the country’s
particularly high levels of excess body weight and associated
burden in females.
Rodgers et al.
5
refers to trials that have recorded beneficial
health effects through weight reduction achieved by a
combination of personal interventions, including dietary
counselling and therapy involving decreased daily calorie intake
and a reduction in saturated and total fats. These measures
may be complemented by behavioural strategies around stress
management, social support, self-monitoring of eating habits,
Fig. 3. Deaths attributable to excess body weight in South Africans, by age
and sex, 2000.
0
1000
2000
3000
4000
5000
6000
7000
8000
30-44 45-59 60-69 70-79 80+ 30-44 45-59 60-69 70-79 80+
Males Females
Age in years
Annual attributable deaths
Type 2 diabetes Cardiovascular diseases Cancers
Female deaths = 24 924Male deaths = 11 579
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and problem solving. However, overall, the effects of lifestyle
modification to reduce weight and maintain such weight loss, are
relatively poor.
5
Many studies have found that weight returns to
baseline levels after several years. Dietician-led treatments, brief
training interventions, inpatient care, shared care, and reminder
systems may be worth further investigation.
41
Randomised
controlled trials with pharmacological agents for weight loss
have suggested modest weight-loss effects.
5
Although surgery
may not be regarded as a solution in developing countries, trials
on persons with BMI > 35 kg/m
2
have demonstrated about
23 - 37 kg more weight loss than conventional treatment, and
that this loss was maintained for 8 years.
42
Population-wide
initiatives to address the root causes of CVD, including the
societal determinants of high salt and saturated fat intake, high-
energy diets, and decreasing levels of physical activity should
complement the management of individuals with high absolute
risk of CVD also taking into account high blood pressure and
high cholesterol.
5
A population-based intervention in China, the
Tianjin Project, showed a significant reduction in sodium intake
in men, and after 5 years, decreasing rates of hypertension and
obesity.
43
However, Flegal et al.
44
indicated that relatively little
is known about effective prevention and management of excess
body weight on a population-wide basis. Research is needed
to develop and evaluate multi-level interventions that are
appropriate for different settings in South Africa.
This is the first study to quantify the adverse health outcomes
associated with excess body weight in South Africa. However,
the study has some limitations. This article does not consider
the joint effects of the cluster of risk factors that share a common
causal pathway in the development of CVD and T2DM. BMI
was used as a measure of excess body weight, and although
BMI correlates highly with body fatness,
45
it does not distinguish
between weight associated with lean mass and fat mass. Central
obesity, measured by waist circumference or waist-to-hip
ratio, may be a better predictor of CVD than BMI or total body
fatness.
46
While the IOTF suggests that BMI (as used in the
Global CRA as a proxy for body fatness) is the most appropriate
simple indicator of weight-for-height relating to health outcomes
at a population level,
15
there is a need for further epidemiological
data based on more sensitive measures.
Conclusions and recommendations
It is concluded that excess body weight results in a substantial
burden of death, premature death and disability in adults in
South Africa, and may be expected to grow with continued
development. Moreover, South African studies show concerning
levels of excess body weight in children. While some action
has been taken to counter the associated burden, excess body
weight is likely to continue contributing to ill health in both poor
and wealthier sectors of the population, and there is a need for
increasing the priority given to such action.
Considering reviews and recommendations by the Disease
Control Priorities Project, interventions that are likely to counter
the burden from this risk factor include educating people
on energy balance and healthy food choices (in particular in
schools, the workplace and health care providers); marketing
that promotes healthy food choices (including clear labelling
of energy content for all packaged foods, including fast foods
where reasonable); healthy advertising (including standards that
limit the promotion of foods high in refined starch, sugar, and
saturated and trans-fats to children); improving availability and
reducing the cost of healthy foods; improving the processing
and manufacturing of food (including replacing unhealthy with
healthy fats and oils, fortifying foods, and setting standards for
the amount of sodium in processed foods); modifying town,
road and building designs to promote safe walking, cycling, and
the use of stairs, and to improve access to public transportation;
implementing policies with an economic incentive for
healthier choices; and surveillance systems to monitor relevant
indicators.
5,47
Challenges in South Africa are expected to include
the social acceptance of being overweight; issues regarding
food security, pricing and availability of healthier foods; food
labelling, advertising and marketing; and sustaining intervention
efforts.
It is, however, acknowledged that high levels of excess body
weight in a population is a complex issue that raises complex
questions about reshaping public policy across a number of
sectors to deal effectively with the manifestations of the risk
factor and the forces that shape it.
48
No country yet has been
successful in reversing obesity trends,
48
pointing to these
complexities. While it seems natural to recommend further
research in seeking successful interventions, it is acknowledged
that there is difficulty in deciding whether evidence must
precede interventions, or whether policy changes must happen
alongside seeking such evidence through research. It is therefore
clear that strong leadership is needed in South Africa to guide
action around the prevention and management of this risk factor
and its determinants and impact, particularly in the presence of
other cardiovascular risk factors.
Colleagues at the Burden of Disease Research Unit of the South
African Medical Research Council, Pam Groenewald, Nadine
Nannan, Beatrice Nojilana, Desiréé Pieterse, and Michelle Schneider
are thanked for their valuable contribution to the South African
Comparative Risk Assessment Project. Dr Lize van der Merwe and
Ms Ria Laubscher of the MRC Biostatistics Unit made contributions
via their statistical expertise. Ms Elize de Kock was responsible
for the arrangements of the Expert Cluster Workshops and Core
Working Group Meetings, and is thanked for administrative
support throughout the project. Ms Karin Barnard is thanked for her
assistance in checking figures. Dr Carlene Lawes, Dr Stephen Vander
Hoorn and Dr Anthony Rodgers from the University of Auckland,
New Zealand, and part of the Global and Regional Comparative
Quantification of Health Risks Assessment Team, were very helpful
in providing advice and guidance, and in obtaining relative risks
and drafts of the global review chapters. Professor Krisela Steyn of
body weight1.indd 689 11/2/07 11:09:27 AM
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the University of Cape Town and Associate Professor Theo Vos of
the University of Queensland, Australia, are thanked for external
review of the manuscript. Ms Leverne Gething is acknowledged for
editing the manuscript.
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    • "Similar to high blood pressure, the age-related differences in the PAFs can be attributed to the high prevalence of excess weight amongst young females, which increases the risk of cardiovascular disease [28]. Whilst the trend in age-and sex-specific PAFs in our study was similar to other studies313233, the absolute PAF values at younger ages were much higher in the current study even when prevalence rates of obesity were similar. A likely explanation is the difference in the " theoretical minimum risk " values applied across studies. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Rural South Africa (SA) is undergoing a rapid health transition characterized by increases in non-communicable diseases; stroke in particular. Knowledge of the relative contribution of modifiable risk factors on disease occurrence is needed for public health prevention efforts and community-oriented health promotion. Our aim was to estimate the burden of stroke in rural SA that is attributable to high blood pressure, excess weight and high blood glucose using World Health Organization's comparative risk assessment (CRA) framework. Methods: We estimated current exposure distributions of the risk factors in rural SA using 2010 data from the Agincourt health and demographic surveillance system (HDSS). Relative risks of stroke per unit of exposure were obtained from the Global Burden of Disease Study 2010. We used data from the Agincourt HDSS to estimate age-, sex-, and stroke specific deaths and disability adjusted life years (DALYs). We estimated the proportion of the years of life lost (YLL) and DALY loss attributable to the risk factors and incorporate uncertainty intervals into these estimates. Results: Overall, 38 % of the documented stroke burden was due to high blood pressure (12 % males; 26 % females). This translated to 520 YLL per year (95 % CI: 325-678) and 540 DALYs (CI: 343-717). Excess Body Mass Index (BMI) was calculated as responsible for 20 % of the stroke burden (3.5 % males; 16 % females). This translated to 260 YLLs (CI: 199-330) and 277 DALYs (CI: 211-350). Burden was disproportionately higher in young females when BMI was assessed. Conclusions: High blood pressure and excess weight, which both have effective interventions, are responsible for a significant proportion of the stroke burden in rural SA; the burden varies across age and sex sub-groups. The most effective way forward to reduce the stroke burden requires both population wide policies that have an impact across the age spectra and targeted (health promotion/disease prevention) interventions on women and young people.
    Preview · Article · Dec 2016 · BMC Public Health
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    • "In South Africa (SA), 68 % of hypertensive disease, 45 % of ischaemic stroke, 38 % of ischaemic heart disease, and 87 % of type 2 diabetes were attributed to excess body weight (BMI > 25 kg/m 2 ) in adults in 2000 [2]. As the prevalence of obesity and overweight has increased from 57 % in 2002 to 65 % in 2012 [2, 7, 8], the impact of obesity in the South African population is expected to rise considerably. Numerous factors such as community-level, social and behavioural (mainly sedentary lifestyles combined with excess energy intake) factors are implicated in the sustained obesity epidemic in SA and other populations [9][10][11]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: The obesity epidemic is associated with rising rates of cardiovascular disease (CVD) among adults, particularly in countries undergoing rapid urbanisation and nutrition transition. This study explored the perceptions of body size, obesity risk awareness, and the willingness to lose weight among adults in a resource-limited urban community to inform appropriate community-based interventions for the prevention of obesity. Method: This is a descriptive qualitative study. Semi-structured focus group discussions were conducted with purposively selected black men and women aged 35–70 years living in an urban South African township. Weight and height measurements were taken, and the participants were classified into optimal weight, overweight and obese groups based on their body mass index (Kg/m2). Participants were asked to discuss on perceived obesity threat and risk of cardiovascular disease. Information on body image perceptions and the willingness to lose excess body weight were also discussed. Discussions were conducted in the local language (isiXhosa), transcribed and translated into English. Data was analysed using the thematic analysis approach. Results: Participants generally believed that obesity could lead to health conditions such as heart attack, stroke, diabetes, and hypertension. However, severity of obesity was perceived differently in the groups. Men in all groups and women in the obese and optimal weight groups perceived obesity to be a serious threat to their health, whereas the overweight women did not. Obese participants who had experienced chronic disease conditions indicated strong perceptions of risk of obesity and cardiovascular disease. Obese participants, particularly men, expressed willingness to lose weight, compared to the men and women who were overweight. The belief that overweight is ‘normal’ and not a disease, subjective norms, and inaccessibility to physical activity facilities, negatively influenced participants’ readiness to lose weight. Conclusion: Low perception of threat of obesity to health particularly among overweight women in this community indicates a considerable challenge to obesity control. Community health education and promotion programmes that increase awareness about the risk associated with overweight, and improve the motivation for physical activity and maintenance of optimal body weight are needed.
    Full-text · Article · Apr 2016 · BMC Public Health
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    • "In South Africa (SA), 68 % of hypertensive disease, 45 % of ischaemic stroke, 38 % of ischaemic heart disease, and 87 % of type 2 diabetes were attributed to excess body weight (BMI > 25 kg/m 2 ) in adults in 2000 [2]. As the prevalence of obesity and overweight has increased from 57 % in 2002 to 65 % in 2012 [2, 7, 8], the impact of obesity in the South African population is expected to rise considerably. Numerous factors such as community-level, social and behavioural (mainly sedentary lifestyles combined with excess energy intake) factors are implicated in the sustained obesity epidemic in SA and other populations [9][10][11]. "
    Full-text · Dataset · Apr 2016
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