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The Social Determinants of Health: Globalization, Urbanization, and Overweight in the
Southern African Development Community.
By Nyovani J Madise1 and Gobopamang Letamo2
Institutional Affiliation: 1Division of Social Statistics and Demography
University of Southampton
Southampton
UK
SO17 1BJ
Email: [email protected]
2Department of Population Studies,
University of Botswana,
Private Bag UB 00705,
Gaborone,
BOTSWANA
Email: [email protected]
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The Social Determinants of Health: Globalization, Urbanization, and Overweight in the
Southern African Development Community.
Abstract
Africa is facing the dual challenge of under-nutrition and a growing burden of overweight and
obesity especially in countries that are urbanizing and globalizing fast. Overweight status increases
the risks of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension,
and diabetes mellitus. The WHO Commission on Social Determinants of Health framework has
stimulated greater desire among the public health community to understand the socioeconomic
and context of NCDs and their risk factors. We use the framework to study the confluence of
urbanization and wealth and their links with overweight and obesity in the Southern African
Development Community (SADC). The SADC has some of the highest rates of overweight and
obesity, but the region is quite diverse with high rates of malnutrition especially among children.
Data from DHS programme, along with other macro-level statistics from the Human Development
Report, WHO Global Health Observatory, and the UN are used to identify the determinants of
overweight or obesity among women. Pooled data comprising over 71,000 women were used also
to assess the influence of the country’s socioeconomic context on patterns of overnutrition. Nine
out of the 15 SADC countries have overweight or obesity prevalence of more than 30% among
adult women (20 years or older). The country-level results show that age, educational level,
household wealth, marital status, and contraceptive use are associated with the odds of being
overweight or obese. The interaction between urban/rural place of residence and household wealth
status shows three patterns: high overweight or obesity levels in urban compared with rural areas
for the poorer countries; no difference between urban and rural levels where the national
prevalence of overweight or obesity is very high; and a cross-over effect in the wealthier countries
where the rural affluent women have the highest levels of overweight or obesity. Among the macro-
level variables, only gross national income and HIV prevalence explain some of the variation in the
levels of overweight and obesity in the region.
Introduction
Sub-Saharan Africa, like many other developing regions is experiencing a dual burden of under-
and over-nutrition (Amuna and Zotor, 2008). High levels of stunting and under-nutrition especially
among children have been a characteristic of the African continent for decades, and malnutrition is
thought to be implicated in nearly 50% of the 5 million child deaths that occur every year in Africa.
Recent data from the Demographic and Health Surveys programme (DHS) suggest that sub-
Saharan Africa may be at the onset of another challenge of overweight and obesity especially
among women, and this phenomenon is most apparent in countries that are urbanizing and
globalizing fast. In 2008, it was estimated that approximately 30% of African women aged 20 years
or older were either overweight or obese (WHO, 2008a). Urbanization and growing national and
household wealth are often accompanied by a change in diet to foods with higher fat and sugar
content and a lifestyle of lower physical activity (Griffiths and Bentley, 2003; Vorster et al. 2000;
Popkin and Gordon-Larsen, 2004). Being overweight and obese is a major risk factor of many non-
communicable diseases (NCDs) including cardiovascular disease, hypertension, arthritis and
diabetes mellitus (Hulshof et al. 1991;Pena and Bacallao, 2000; Himes 2000). According to the
World Health Organisation (WHO), more than 28 million Africans will have died from non-
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communicable diseases between 2008-2018, and deaths from diabetes will have increased by 42%
(World Health Organisation, 2008b). Overweight and obesity are now receiving great attention as
the problem is fast becoming a global epidemic. The challenge for Africa is that there are still large
populations facing chronic under-nutrition especially among children, so interventions should not
just focus on one but on both.
Popkin and Gordon-Larsen (2004) coined the term “nutrition transition” to describe nutritional
patterns that have been observed from historical settlements to the present day. They suggested
that there may be five patterns that characterize the nutrition transition, but the patterns are not
restricted to periods of time. The first pattern is similar to what was observed in historical hunter-
gatherer populations and is termed “collecting food”. The diet is high in carbohydrates and fibre
and low in fat. Physical activity is very high, and consequently obesity is very rare. The second
pattern is the ‘famine’ stage, where the diet is less varied and subject to periods of acute shortage
of food. The third pattern is that of “receding famine”. This pattern is characterised by
consumption of fruits, vegetables and an increase in animal protein. There is also a shift to lower
levels of physical activity. The fourth pattern is typically characterised by modern-day western
lifestyle and accompanied growth in nutrition-related non-communicable diseases. As
households become wealthier, they adopt diets that are high in fat, sugar and refined
carbohydrates, and there is an increase in sedentary lifestyle. This stage is often accompanied by
high levels of urbanization and economic development. There is an increase in the prevalence of
obesity and degenerative diseases. The fifth pattern is when there is “behavioural change” to
prevent or delay degenerative diseases. This pattern sees a change from high fat, high sugar diets
to more fruit, vegetables, and fibre, and an increase in exercise. In most rural African countries, the
third pattern is prevalent while in some of the major cities, the fourth pattern can be seen among
more affluent urbanites. The fifth pattern is often accompanied by greater awareness of NCDs,
high disposable income and a deliberate choice towards diets low fat and greater leisure physical
activity.
The nutrition transition is closely associated with the epidemiologic transitions which starts with
high prevalence of infectious diseases but as the transition progresses, there is a shift in the
burden of disease to chronic and NCDs (Popkin and Gordon-Larsen, 2004). Similarly, the nutrition
transition’s links to the demographic transition are apparent—the first pattern relates to high fertility
and high mortality stages of the demographic transition and as societies move to the fourth stages,
fertility has started to decline. Since overweight is a major risk factor of NCDs, the underlying
socioeconomic and cultural determinants overlap significantly. Figure 1 shows some of the causes
and determinants of NCDs. They include underlying factors such as globalization and urbanization,
and modifiable factors such as physical activity, and diet. Non-modifiable factors such as age,
ethnicity, gender and hereditary factors increase the risks of NCDs. Ignoring the socioeconomic
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determinants of NCDs and their risk factors can lead to the design of ineffective interventions that
do not address the underlying causes.
Figure 1 Causes of non-communicable diseases
Adapted from WHO Global Report: Preventing Chronic Diseases: a vital investment, 2005, p.48.
The social determinants of health agenda is not new, but the Commission on Social Determinants
of Health led by Sir Michael Marmot has rejuvenated the desire to understand the context and
environment within which NCDs are emerging (WHO, 2008c). In 2010, the Regional Committee of
African Ministers of Health adopted a regional strategy for addressing the key determinants of
health in Africa in which they urged member states to initiate or intensify studies to document the
current situation with respect to the social determinants of health. In this paper we study the
confluence of urbanization and wealth and their links with overweight and obesity in the Southern
African Development Community (SADC). SADC, comprising of 15 member states (see Table 1 for
the list of members), aims to further socioeconomic cooperation (for example through liberal trade
agreements) and regional integration between its member states. The sub-region is interesting to
study because of its contrasts. The SADC has some of the highest rates of overweight and obesity
among women (for example, more than 60% of women 20 years or older in the Seychelles,
Swaziland and South Africa) but also high levels of under-nutrition especially among children (see
Table 1). For example, nearly 50% of under-five children in Madagascar and Malawi are stunted,
and in South Africa where about three-quarters of adult women are overweight or obese, about a
quarter of its under-five children are stunted. The region is also diverse in terms of its economic
development with upper-middle income countries such Botswana, Mauritius, Namibia, Seychelles,
and South Africa, and some of the poorest countries in the world with gross national incomes per
capita (PPP constant 2005 international $) of less than 1,000 (e.g. Democratic Republic of Congo,
Malawi, Mozambique, Madagascar, and Zimbabwe). Urbanization also varies significantly, ranging
from 16% in Malawi to over 60% in Botswana and South Africa. Another important characteristic of
the region is its high HIV prevalence rates.
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In this paper we seek to: 1) to examine the patterns and determinants of overweight and obesity
among women with specific focus on the confluence of urban living and household wealth status;
and 2) to assess the relative influence of the socioeconomic and political contexts of SADC
countries on the health of its women. We make the following assumptions drawn from the literature
and the nutrition transition theory:
1) household wealth is strongly associated with the risk of being overweight or obese;
2) urban women generally have higher levels of overweight and obesity compared with rural
women, but where national overweight and obesity levels are very high, this difference
diminishes;
3) the most affluent women in urban areas (or most educated) may be the first to adopt pattern 4
diet and lifestyles of the nutrition transition, and thus may have lower levels of overweight and
obesity compared with poorer rural women (or those with lower levels of education).
Data and Methods
The WHO Commission on Social Determinants of Health (CSDH) framework is useful for studying
national patterns of socioeconomic factors influencing health. The framework (see Figure 2),
acknowledges the importance of socioeconomic and political contexts such as governance,
macroeconomic policies, social and public policies, and culture and societal values. In terms of
operationalizing the framework, these country-level contextual determinants may be measured by
factors such as national wealth, literacy, life expectancy, and urbanization. The determinants
labelled as “social positions” in the framework can be thought of as those that create stratification
within society and therefore determine the power and prestige that certain groups enjoy. Variables
that can be used as indicators of this group include income or wealth status, occupation, education,
gender, and ethnicity. Intermediate determinants can be measured by material circumstances
which affect living conditions, food availability, behavioural and biological factors, psychosocial
factors, and also the health care system. There can be overlap between social position variables
and intermediate determinants (e.g. income as a stratifying variable but also as an intermediate
determinant of consumption power. Ultimately, these determinants influence health and wellbeing.
We shall use the CSDH framework as a convenient framework to identify determinants of
overweight or obesity. We do not aim to test the relevance of the framework per se.
The depend variable
Body mass index (BMI) is one of the indicators used to measure nutritional disorders including
underweight, overweight and obesity. Body mass index is measured as a ratio of weight in
kilogrammes and height in metres squared. The World Health Organization classifications for BMI
are as follows: “underweight or thin” <18.50; “normal” between 18.50 – 24.99; “overweight” BMI
≥25.00; and “obese”, BMI ≥30. Other refinements are available from WHO website, for example
thinness can be refined into several levels, as can obesity. There have been debates in the
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literature about whether these international BMI cut-offs are comparable across ethnic groups and
cultures particularly as risks of developing NCDs such as diabetes at low levels of BMI appear high
for some Asian and black ethnic groups (Shah et al., 2009;Chiu 2011). For the same BMI, there
may be different levels of body fat and its distribution by ethnicity. An expert group consultation
was convened by the WHO to resolve the debate. The group examined a range of data and on the
basis of the evidence they reached the conclusion that the international BMI cut-off points should
be used for all ethnic groups (World Health Organization Expert Consultation, 2004).
To achieve the first objective of this paper, we used logistic regression analysis on data from the
DHS conducted in SADC countries between 2006 and 2011. Angola, the Seychelles, and
Mauritius did not participate in the DHS programme during this period and the South African 2008
DHS data are restricted and thus we were unable to include them in the individual country-level
analyses. Botswana also did not participate in the DHS programme during this period but we use
comparable nationally representative data collected as part of the 2007 Botswana Family Health
Survey. We focus on adult women, since the majority of DHS do not collect anthropometry for
adult males. Additionally, there are some age-restrictions for some of the analyses. Most DHS data
on women are restricted to their reproductive ages (15-49 years). We excluded women who were
pregnant at the time of the survey and those who had given birth in the four months before the
survey since their BMI is affected by their pregnancy or post-partum state. A 5% significance level
was used for the individual country analyses.
For the second objective, the individual country level data are pooled and macro-level variables
from the Human Development Report, UNAIDS, and World Bank databases were used as
contextual data in logistic regression analysis. We also conducted ordinary least squares (OLS) of
macro-level variables using the 2009 mean BMI of all adult women (20+ years) in each country.
The mean BMI were obtained from the World Health Organisation Global Health Observatory and
included all 15 SADC countries. The estimation methodology for the mean BMIs is explained on
the WHO Global Health Observatory website (WHO, 2013). We calculated Nagelkerke’s pseudo R-
squared to assess the quality of the fit, but this statistic is not as informative as proper goodness-
of-fit statistics.
Variables Used
Social Position. For objective (1), we used the following variables: highest educational level,
marital status, religion/ethnicity and region of residence. Absolute household wealth was
measured by counting the number of assets and modern amenities in the household. These
included items such as bicycle, car, TV, telephone, piped water, toilet facility, and finished
flooring, walls, and roofing. We added “household’s sole use toilet facilities” as a particularly
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sensitive indicator among the urban poor. Household wealth was used as a continuous
variable.
Intermediate determinants: The only appropriate proxies for living conditions were place of
residence and household wealth (which includes housing conditions); for behavioural factors
we included contraceptive use and breastfeeding status for women who have given birth in the
12 months prior to the survey); for biological factors we included age and the number of
children ever born.
Socioeconomic and political context: For objective (2), where we conducted pooled
analyses and OLS, macro-level variables were obtained from the Human Development Report,
2012. These included standardised Gross National Income (GNI) per capita in purchasing
power parity (constant 2005 international dollars), life expectancy at birth, and mean years of
schooling among adults. Urbanization rates were obtained from the United Nations’ World
Urbanization Prospects, 2011 Revision. Since life expectancy in the SADC is strongly linked to
HIV prevalence, we include this in the OLS analyses.
Results
The latest data from the WHO Global Health Observatory were used to plot levels of overweight
and obesity in the whole SADC. Figure 3 shows the percentage of adult women (age 20 years or
older) in 2008 who were overweight or obese in the SADC countries. In South Africa, an estimated
74% of adult women were either overweight or obese. In Swaziland, Seychelles, Lesotho,
Botswana, and Mauritius, more than 50% of women were overweight or obese. Madagascar had
the lowest prevalence at 9%, followed by the Democratic Republic of Congo (DRC) (15%). Note
that these prevalence rates are not comparable to those reported in DHS country reports since the
DHS measures women 15-49 years only.
Individual country-specific logistic regression
The individual country results based on data for women 15-49 years show that age was the most
important factor associated with overweight or obesity, and there was almost a linear increase in
the odds for each five-year age-group that age increases (see Table 2). Compared with women
15-19 years, those 45-49 years had between 2 (DRC) and 11 times (Botswana) the odds of being
overweight or obese. In Madagascar, women between 35 and 44 years of age had the highest
odds (7-7.8) of being overweight or obese compared with teenage women. The association
between being overweight or obese and women’s highest level of education was positive and
statistically significant in 8 out of the 11 countries (exceptions are Botswana, Lesotho, and
Madagascar). Compared with women without any education, those with tertiary education had
between 47% (Mozambique) and 221% (Tanzania) higher odds of being overweight. In Swaziland,
the highest odds of being overweight or obese were observed among women with secondary
education compared with women with no education (odds ratio=1.69).
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Marital status was significantly associated with the odds of being overweight or obese in all 11
countries. Except for Zambia, currently married women had the highest odds of being overweight
compared with never married women. The odds ratios ranged from 1.25 in Swaziland to 2.15 in
Madagascar. In Zambia, formerly married women had 1.5 higher odds of being overweight or
obese compared with never married women. The number of children ever born was significant in
four out of 11 countries. For each increase in the number of children ever born, there was a 5-9%
increase in the odds of being overweight or obese in these four countries. Use of contraception,
especially hormonal methods, was associated with higher odds of being overweight or obese
compared with non-use of contraception (odds ratios ranged between 1.31 in Namibia to 1.9 in
DRC). The only country where this association was not significant is Madagascar. We also
controlled for breastfeeding status and the results showed lower odds of being overweight or
obese among women who were breastfeeding at the time of the survey.
To turn to the main variables of specific interest for the first objective—urban or rural place of
residence and household wealth, both were highly significant in 9 out of 11 countries. Generally,
the odds of being overweight or obese were higher for urban dwellers and wealthier women. Only
in Lesotho and Swaziland was place of residence not statistically significant. This confirms our first
and second assumptions, that household wealth is significantly associated with the likelihood of
being overweight or obese and that in those countries where overweight and obesity levels are
very high, the difference between urban and rural areas is small. We also tested the interaction
between these two variables. The interaction between place of residence and wealth status was
statistically significant in five countries (Botswana, Madagascar, Namibia, Tanzania, and Zambia)
and of borderline insignificance in two (Malawi and Zimbabwe). To interpret the interactions, we
plotted the fitted probabilities of being overweight or obese by urban/rural residence and household
wealth for each country. These plots are shown in Figure 4 and since the aim is to observe and
compare the patterns across countries rather than the levels, the plots do not have the same y-axis
scale.
From these analyses we observe the patterns.
a) The first group of countries are Malawi, Mozambique and DRC and they portray a pattern
that we expected for poorer countries. In these countries, we found higher rates of
overweight or obesity among urban women compared with rural women and higher levels
of BMI for the more affluent.
b) The second groups of countries are the five where the interaction between place of
residence and household wealth was statistically significant (Botswana, Madagascar,
Namibia, Tanzania, and Zambia). In these countries, there is a mix of upper middle income
countries (e.g. Botswana, Namibia) and countries that have recently moved into lower-
middle income (e.g. Zambia, Tanzania). Madagascar is very unusual in that its gross nation
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income per capita and overall level of overweight and obesity should place it in the first
group of countries. This group of countries had a cross-over pattern where at lower levels
of household wealth, urban women had higher BMI on average, but at higher levels of
wealth, rural women had the highest BMIs. This appears to support the third assumption
that the wealthiest in urban areas could be the trendsetters for behavioural change to lower
levels of BMI.
c) Group 3 is of the two countries (Lesotho and Swaziland) where there was no difference in
the odds of being overweight or obese by place of residence. In the two countries, overall
levels of overweight and obesity are very high.
Other results from the individual country analyses (not shown) indicated the presence of significant
regional differences in all countries. Regions can be indicators of social position (since in virtually
all of the countries, regional development is uneven) or they can be proxies of ethnicity. Religion
(catholic, protestant, muslim, and other) was statistically significant in two countries only
(Swaziland and Zimbabwe). Nagelkerke’s statistics show roughly that about 30% of the variation is
explained by the model in Namibia and for the other countries, these ranges from 12% to 27%.
Pooled analysis
To address objective (2), we pooled the individual women’s data and merged these data with
macro-level variables (GNI per capita, urbanization, life expectancy, HIV prevalence, and mean
years of schooling). We used forward selection procedure and since we had a large sample
(71,853) we tested significance at 1% level. There was a very strong correlation between “country”
and each of these variables such that once “country” was controlled for, none of the macro-level
variables were significant in explaining the variation between women. The results of this logistic
regression, showing “country” as the only macro-level variable and the significant individual level
variables are shown in Table 3. They confirm the strong association between the odds of being
overweight or obese and age, the highest educational level, urban/rural residence, household
wealth, marital status, and contraceptive use. The number of children ever born was not significant,
and breastfeeding status was excluded as it had not been collected in Botswana. The interaction
between urban/rural residence and household wealth was highly significant. The diagram to
illustrate this interaction is in Figure 4 (see the last panel of Figure 4). This portrays the cross-over
pattern whereby wealthier rural women had the highest prevalence of overweight and obesity, on
average.
Our final analyses were to fit ordinary least squares with the mean BMI in 2009 of each of the 15
SADC countries as the dependent variable and regressed this against the macro-level variables in
Table 1. Backward selection stepwise regression was used. The results (Table 4) show that only
standardized national wealth and adult HIV prevalence were statistically significant. Wealthier
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nations and those with high HIV prevalence were also likely to be the countries with the highest
levels of overweight or obesity.
Discussion
In this paper we drew from the nutrition transition theory and the WHO’s CSDH framework to
examine patterns of overweight or obesity in the SADC region. Our results confirmed the wide
variation in the nutritional status of women in the SADC but also showed of strong within-country
variations. The likelihood of being overweight or obese is strongly linked to age, household wealth,
urban/rural residence, and educational attainment. However, the relationship between urban/rural
residence and household wealth varies between the countries, with interesting patterns of higher
overweight and obesity rates among more affluent rural women in some countries. The public
health concerns of being overweight or obese cannot be over-emphasised. Overweight or obesity
increases individuals’ risks of NCDs diseases and premature death (Himes 2000; Hulshof 1991;
Popkins 1998). In 2008, the World Health Organisation estimated that 28 million Africans will die of
chronic conditions over the next ten years and that deaths from diabetes will increase by 42%
(WHO, 2008a).
The relationship between marital status and overweight or obesity may be linked to age, whereby
the majority of older women are married. However, we can also not rule out factors such as
southern African cultural preferences for “bigger” women (Matoti Mvalo,2011) or being overweight
being seen as a sign of affluence (Bentley et al. 2005). Contraceptive use, especially in respect of
hormonal methods (pill, injectable, and norplant) was associated with increased odds of being
overweight in nearly all of the countries. Studies on contraceptive use often report of women
stating ‘weight gain’ as a side effect of hormonal contraception. While our study does not show
causation, the results are consistent with finding from other parts of the continent (Sule and Shittu,
2005).
The urbanization-wealth-overweight relationship is complex, and often requires nuanced analysis.
Pena and Bacallao (2000) discuss the phenomenon of obesity, urbanization and the links with
socioeconomic status in Latin America and the Caribbean where the urban poor are particularly at
risk of obesity. They indicate the presence of cross-over patterns in the relationship between
obesity and socioeconomic status and claim that one of the reasons for the increase in obesity
among the urban poor is the high prices of healthier foods such as fruit and vegetables. Evidence
of large relative increases in levels of obesity and overweight among the urban poor in Africa have
been reported by Ziraba et al. (2009) who compared over time the changes in overweight and
obesity among urban women. However, our study did not find higher overweight levels among the
urban poor relative to the urban affluent populations. A key message from our results and the
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literature is that sub-groups that have traditionally been thought to have lower BMI ( e.g. urban
poor or rural women) are emulating the patterns in the fourth stage of the nutrition transition where
high fat, low vegetable diets and more sedentary lifestyles dominate. Among the urban affluent,
some sub-groups may be thought to have adopted the fifth pattern of the nutrition transition in that
for the same levels of wealth, they appear to have lower BMIs than their rural counterparts.
For the SADC region, HIV prevalence deserves mention since this may have a bearing on desired
body sizes of women. Studies have reported of women linking overweight status with being HIV
negative and weight loss being viewed with suspicion in this region where despites efforts,
HIV/AIDS is still a stigmatising condition. Matoti-Mvala, in his work on perceptions of body size and
its association with HIV/AIDSs in an urban township in Cape Town found that more than one-third
of the 500 odd women that participated in the study preferred being overweight, and majority of the
women associated “thinness” with being infected with the HIV virus. Similar findings were also
reported by Bentley et al, who in their study looking at maternal nutrition among HIV-positive
breastfeeding women, found that larger body sizes were associated with good health and absence
of disease. Clearly, public health messages of healthy BMI regardless of HIV status need to be
emphasised, and perceptions of “larger equals good health” need to be addressed.
We highlight some limitations. Firstly, as many other researchers have reported, measuring wealth
using data from the DHS is imperfect. In this paper we chose to use absolute wealth instead of
wealth quintiles which measure relative wealth within a country. The assets and modern amenities
used to assess wealth status are heavily biased towards urban populations, so we admit to
perhaps not being able to distinguish rural populations adequately. In the 2007 Botswana Family
Health Survey, there are assets and amenities appropriate for rural communities (e.g. farming
equipment and boats), which helped to create a more meaningful wealth variable. Secondly, the
absence of individual-level data from the more affluent countries including the small islands is a
real shame.
Conclusion
In conclusion, this study makes a contribution in highlighting the growing phenomenon on
overweight and obesity in southern Africa. Nine out of the 15 SADC countries have overweight or
obesity prevalence of more than 30% among adult women (20 years or older) which shows that
this problem is quickly becoming an epidemic. As nations and households become wealthier and
urbanization increases, there is a tendency to adopt unhealthy diets and sedentary lifestyles. Thus,
public health messages need to address this quickly. Although not the specific focus of this paper,
the agenda of malnutrition is not yet completed in the region, and so interventions that address
both spectrums of nutrition disorders should be adopted.
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Acknowledgements:
We acknowledge the support of the ACP EDULINK grant, contract no: 2008/197619 and to
members of the STARND –EDULINK partnership for their comments on earlier drafts.
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http://www.who.int/gho/ncd/methods/en/index.html. Accessed on 14th August 2013.
Ziraba A.K., Fotso, J.C., Ochako, R (2009). Overweight and obesity in urban Africa: A problem
of the rich or the poor? BMC Public Health, 9:465
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Table 1 Fifteen member states of the SADC Region and selected indicators
Country
Percent of all women 20+ years with BMI >=25,
2008
Percent of children < 5
years who are stunted, 2007-
2010
GNI per capita (PPP
constant 2005 international$)
% Urban 2011
Estimated HIV
prevalence rate (%) in
2011
Life Expectancy
at birth
Mean years of
schooling (adults)
Angola 31 29 4,812 58.4 2.1 51.5 4.7
Botswana 52 31 13,102 61 23.4 53 8.9
Democratic Republic of Congo
15 46 319 35 1.3 48.7 3.5
Lesotho 58 39 1879 28 23.3 48.8 5.9
Madagascar 9 49 828 33 0.3 67 5.2
Malawi 24 48 774 16 10 54.8 4.2
Mauritius 52
13,300 42 1 73.5 7.2
Mozambique 28 44 906 31 11.5 51 1.2
Namibia 45 30 5,973 38 13.4 62.6 6.2
Seychelles 64 8 22,615 53 < 1% 73.8 9.4
South Africa 74 24 9,594 62 17.3 53.4 8.5
Swaziland 68 40 5,104 21 26 48.9 7.1
Tanzania 26 43 1,383 27 5.8 58.9 5.1
Zambia 26 46 1,358 39 12.5 49.4 6.7
Zimbabwe 40 32 424 39 14.9 52.7 7.2
Source for BMI data: WHO Global Health Observatory; Sources HIV Data :UNAIDS; DNI, Schooling, life expectancy from HDR 2012
Source: Urbanization data: World Urbanization prospects, 2011 Revisions, UN Dept of Economic and Social Affairs
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16
Table 2. Individual country logistic regression results. Odds ratios of being overweight or obese by selected characteristics
Characteristic
Age-group OR 95% CI N OR 95% CI N OR 95% CI N OR 95% CI N
15-19 1.00 324 1.00 838 1.00 879 1.00 1651
20-24 1.88 (1.27, 2.77) 1056 1.18 (0.78, 1.78) 762 1.52 (1.17, 1.99) 677 3.45 (1.93, 6.23) 1099
25-29 3.23 (2.19, 4.75) 1054 1.52 (0.98, 2.37) 593 2.35 (1.75, 3.16) 524 4.84 (2.67, 8.78) 1088
30-34 4.17 (2.81, 6.19) 853 2.11 (1.32, 3.38) 488 3.34 (2.42, 4.62) 450 6.37 (3.52, 11.51) 998
35-39 6.10 (4.04, 9.21) 697 1.96 (1.17, 3.28) 410 3.87 (2.38, 5.17) 391 7.79 (4.26, 14.23) 877
40-44 8.88 (5.76, 13.67) 511 2.62 (1.52, 4.53) 399 3.51 (2.72, 5.52) 330 7.13 (3.82, 13.29) 795
45-49 11.13 (7.03, 17.61) 409 1.89 (1.15, 2.55) 310 4.76 (3.15, 7.17) 342 5.77 (3.00, 11.09) 645
Children ever born 1.01 (0.97, 1.06) 4904 1.02 (0.97, 1.08) 3800 0.98 (0.93, 1.03) 3593 1.04 (0.99, 1.09) 7153
Urban/rural
Rural 1.00 1685 1.00 1909 1.00 2675 1.00 5141
Urban 1.90 (1.29, 2.79 3219 1.72 (1.15, 2.55) 1891 0.82 (0.53, 1.25) 918 2.08 (1.27, 3.43) 2012
Education
None 1.00 336 1.00 786 1.00 48 1.00 1446
primary 1.01 (0.76, 1.35) 1078 0.99 (0.72, 1.36) 1425 0.94 (0.51, 1.72) 1830 1.25 (0.85, 1.84) 3214
Secondary 1.17 (0.86, 1.58) 2737 1.28 (0.89, 1.82) 1589 1.21 (0.67, 2.29) 1547 1.43 (0.93, 2.18) 2289
Higher 1.09 (0.78, 1.52) 753 1.93 (1.06, 3.51) 112 1.76 (0.86, 3.57) 168 1.27 (0.67, 2.31) 204
Marital Status
Never married 1.00 2343 1.00 1091 1.00 1273 1.00 1487
Currently married 1.49 (1.30, 1.70) 2371 1.95 (1.34, 2.85) 2316 1.86 (1.48, 2.33) 1848 2.15 (1.33, 3.53) 4674
Formerly married 1.46 (1.06, 2.01) 190 1.66 (1.06, 2.65) 393 1.23 (0.92, 1.64) 472 2.07 (1.21, 3.53) 992
Breastfeeding N/A
No 1.00 2641 1.00 3095 1.00 5464
Yes 0.71 (0.54, 0.93) 1159 0.83 (0.66, 1.05) 498 0.62 (0.46, 0.83) 1689
Contraceptive use
Not using 1.00 1113 1.00 2898 1.00 2314 1.00 4589
Hormonal method 1.43 (1.17, 1.76) 891 1.86 (1.02, 3.41) 62 1.44 (1.18, 1.76) 749 1.17 (0.90, 1.50) 1528
Other methods 1.14 (0.97, 1.34) 2900 1.31 (1.02, 1.67) 840 1.17 (0.94, 1.45) 530 0.92 (0.70, 1.21) 1036
Absolute wealth 1.14 (1.09, 1.18) 4904 1.16 (1.01, 1.34) 3800 1.16 (1.12, 1.21) 3593 1.36 (1.28, 1.44)
Interaction: Wealth and
urban residence 0.95 (0.90, 0.99) 1.00 (0.86, 1.16) 1.02 (0.95, 1.10) 0.89 (0.83, 0.97)
Nagelkerke's R square 0.17 0.15 0.22 0.22
Lesotho 2009Botswana 2007 DRC 2007 Madagascar 2009
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Table 2 continued
Characteristic Namibia 2007
Age-group OR 95% CI N OR 95% CI N OR 95% CI N OR 95% CI N
15-19 1.00 1494 1 2524 1.00 1982 1.00 1133
20-24 1.29 (0.92, 1.82) 1089 1.68 (1.35, 2.08) 1876 2.56 (2.01, 3.24) 1553 1.80 (1.46, 2.20) 838
25-29 2.10 (1.46, 3.02) 1143 2.83 (2.27, 3.56) 1834 3.99 (3.13, 5.08) 1301 2.51 (1.96, 3.21) 615
30-34 2.34 (1.59, 3.46) 866 4.03 (3.18, 5.11) 1578 5.97 (4.65, 7.65) 1208 3.95 (2.98, 5.24) 545
35-39 2.50 (1.66, 3.80) 728 4.91 (3.82, 6.31) 1425 7.87 (6.03, 10.28) 929 5.51 (3.99, 7.61) 452
40-44 3.13 (2.00, 4.90) 550 5.97 (4.56, 7.82) 1063 8.34 (6.27, 11.09) 881 7.05 (4.91, 10.14) 416
45-49 2.75 (1.72, 4.40) 535 7.22 (5.44, 9.57) 1018 8.30 (6.09, 11.30) 687 5.52 (3.79, 8.04) 372
Children ever born 1.02 (0.98, 1.07) 6405 1.00 (0.97, 1.03) 11318 1.09 (1.05, 1.13) 8541 1.09 (1.04, 1.14) 4371
Urban/rural
Rural 1.00 5468 1.00 6348 1.00 4696 1.00 3017
Urban 1.99 (1.32, 2.99) 937 1.35 (1.06, 1.72) 4970 1.81 (1.42, 2.32) 3845 1.11 (0.75, 1.64) 1354
Education
None 1.00 937 1.00 3034 1.00 647 1.00 348
primary 1.48 (1.18, 1.87) 4261 1.35 (1.17, 1.56) 5497 1.45 (1.15, 1.83) 2265 1.4 (1.07, 1.82) 1430
Secondary 1.91 (1.42, 2.57) 1118 1.44 (1.19, 1.75) 2558 1.93 (1.57, 2.51) 5148 1.69 (1.29, 2.21) 2236
Higher 2.04 (1.16, 3.58) 89 1.47 (1.04, 2.07) 229 2.66 (1.96, 3.62) 478 1.41 (0.98, 2.02) 357
Marital Status
Never married 1.00 1461 1.00 2604 1.00 4963 1.00 2225
Currently married 1.47 (1.05, 2.07) 4007 1.41 (1.17, 1.68) 6971 1.41 (1.24, 1.62) 2956 1.25 (1.05, 1.49) 1739
Formerly married 1.42 (0.99, 2.06) 937 1.19 (0.97, 1.46) 1743 1.05 (0.84, 1.30) 619 1.09 (0.83, 1.44) 407
Breastfeeding
No 1.00 4343 1 8352 1.00 7489 1.00 3819
Yes 0.73 (0.61, 0.87) 2062 0.83 (0.72, 0.96) 2966 0.82 (0.69, 0.99) 1052 0.94 (0.76, 1.15) 552
Contraceptive use
Not using 1.00 3763 1 9259 1.00 4324 1.00 2581
Hormonal method 1.34 (1.11, 1.61) 1537 1.33 (1.15, 1.54) 1360 1.31 (1.14, 1.50) 2118 1.37 (1.13, 1.65) 823
Other methods 1.00 (0.82, 1.22) 1105 1.02 (0.83, 1.25) 699 1.27 (1.11, 1.45) 2099 1.28 (1.07, 1.52) 967
Absolute wealth 1.17 (1.13, 1.22) 6405 1.2 (1.16, 1.25) 11318 1.18 (1.14, 1.21) 1.10 (1.07, 1.14) 4371
Interaction: Wealth
and urban residence 0.95 (0.89, 1.01) 1.00 (0.96, 1.04) 0.93 (0.89, 0.96) 0.99 (0.93, 1.04)
Nagelkerke's R square 0.12 0.27 0.30 0.24
Swaziland 2006Mozambique 2011Malawi 2010
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Table 2 continued
Characteristic
Age-group OR 95% CI N OR 95% CI N OR 95% CI N
15-19 1.00 1959 1.00 1368 1.00 1701
20-24 1.54 (1.21, 1.95) 1416 1.57 (1.16, 2.11) 1051 1.46 (1.16, 1.83) 1378
25-29 2.23 (1.71, 2.91) 1206 1.94 (1.41, 2.68) 1025 2.72 (2.15, 3.46) 1297
30-34 3.21 (2.43, 4.24) 1096 2.87 (2.02, 4.08) 795 3.32 (2.58, 4.29) 1047
35-39 3.41 (2.53, 4.58) 1054 2.91 (1.96, 4.31) 587 4.05 (3.09, 5.32) 874
40-44 3.39 (2.46, 4.66) 900 2.98 (1.95, 4.57) 494 5.48 (4.08, 7.36) 672
45-49 3.43 (2.43, 4.83) 795 4.31 (2.75, 6.73) 455 6.22 (4.50, 8.61) 598
Children ever born 0.99 (0.96, 1.38) 8426 1.06 (1.02, 1.11) 5775 1.05 (1.01, 1.10) 7567
Urban/rural
Rural 1.00 6138 1.00 3063 1.00 4742
Urban 2.77 (2.16, 3.55) 2288 2.75 (1.98, 3.82) 2712 1.86 (1.32, 2.63) 2825
Education
None 1.00 1481 1.00 575 1.00 190
primary 1.23 (1.03, 1.46) 4816 1.59 (1.16, 2.17) 2967 1.22 (0.87, 1.72) 2155
Secondary 1.62 (1.64, 1.33) 2086 1.88 (1.34, 2.64) 1917 1.48 (1.05, 2.11) 4871
Higher 3.21 (1.56, 6.53) 43 2.58 (1.67, 3.94) 316 1.85 (1.22, 2.81) 351
Marital Status
Never married 1.00 2563 1.00 1779 1.00 2131
Currently married 1.97 (1.57, 2.47) 4863 1.30 (0.99, 1.71) 3189 1.40 (1.14, 1.71) 4293
Formerly married 1.82 (1.40, 2.34) 1000 1.51 (1.11, 2.04) 807 1.23 (0.98, 1.53) 1143
Breastfeeding
No 1.00 6369 1.00 4131 1.00 6242
Yes 0.70 (0.60, 0.81) 2077 0.67 (0.55, 0.82) 1644 0.72 (0.62, 0.85) 1325
Contraceptive use
Not using 1.00 5979 1.00 3769 1.00 4185
Hormonal method 1.34 (1.14, 1.59) 1226 1.35 (1.10, 1.66) 994 1.33 (1.16, 1.53) 2667
Other methods 1.17 (0.99, 1.38) 1221 1.19 (0.97, 1.46) 1012 1.15 (0.95, 1.39) 715
Absolute wealth 1.22 (1.17, 1.27) 8426 1.21 (1.15, 1.27) 5775 1.13 (1.09, 1.16)
Interaction: Wealth and
urban residence 0.91 (0.86, 0.96) 0.92 (0.86, 0.97) 0.95 (0.91, 1.00)
Nagelkerke's R square 0.22 0.22 0.21
Zambia 2007 Zimbabwe 2010Tanzania 2010
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Characteristic
Age-group OR 95% CI N
15-19 1.00 15853
20-24 1.69 (1.56, 1.83) 12795
25-29 2.8 (2.58, 3.04) 11680
30-34 3.99 (3.67, 4.34) 9924
'35-39 5.00 (4.59, 5.46) 8424
40-44 6.06 (5.54, 6.64) 7011
45-49 6.76 (6.15, 7.42) 6166
Urban/rural
Rural 1.00 44882
Urban 2.02 (1.85, 2.21) 26971
Education
None 1.00 9828
primary 1.4 (1.30, 1.50) 30938
Secondary 1.68 (1.55, 1.81) 27986
Higher 1.74 (1.56, 1.94) 3101
Marital Status
Never married 1.00 21770
Currently married 1.33 (1.25, 1.41) 39199
Formerly married 1.33 (1.23, 1.43) 10884
Contraceptive use
Not using 1.00 44774
Hormonal method 1.42 (1.34, 1.49) 13955
Other methods 1.15 (1.09, 1.21) 13124
Absolute wealth 1.20 (1.19, 1.21) 71853
Interaction: Wealth and urban
residence 0.94 (0.92, 0.95)
Country
Botswana 1.00 4904
DRC 0.70 (0.62, 0.79) 3800
Lesotho 3.20 (2.88, 3.55) 3593
Madagascar 0.29 (0.26, 0.32) 7153
Malawi 0.94 (0.85, 1.04) 6405
Mozambique 1.07 (0.98, 1.16) 11318
Namibia 1.32 ( 1.20, 1.44) 8541
Swaziland 4.15 (3.76, 4.58) 4371
Tanzania 1.47 (1.34, 1.61) 8426
Zambia 0.88 (0.80, 0.98) 5775
Zimbabawe 1.48 (1.36, 1.62) 7567
Nagelkerke's R square 0.32
SADC - Pooled
Table 3. Pooled data results. Odds ratios os being overweight or obese among
women aged 15-49 years in SADC region, DHS data 2006-2011.
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Table 4. OLS Macro-Level Results: Dependent variable is Mean BMI in 2009 for 15 SADC countries
Unstandardised
beta Stand error
t-value
Constant 23.07 0.54 42.89
HIV prevalence rate 0.18 0.04 4.51
Standardised Gross National Income 1.82 0.35 5.22
Adjusted R-Squared = 0.75
Figure.2 Conceptual framework of the social determinants of health
Source: WHO CSDH, 2008c.
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Source: World Health Global Health Observatory.
9
15
24
26
26
28
31
40
45
52
52
58
64
68
74
0 10 20 30 40 50 60 70 80
Madagascar
DRC
Malawi
Tanzania
Zambia
Mozambique
Angola
Zimbabwe
Namibia
Mauritius
Botswana
Lesotho
Seychelles
Swaziland
South Africa
Figure 3. Percentage of women 20+ years with Body Mass Index >=25 in SADC Region, 2008
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Figure 4: Probability of being overweight or obese by urban rural residence and household wealth
.000
.100
.200
.300
.400
.500
1 2 3 4 5 6 7 8 9 1011121314151617
Pro
bab
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Household wealth based on number of assets owned
Democratic Republic of Congo 2007
Urban
Rural
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Figure 4 continued
0.00
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0.20
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Figure 4 continued
0.00
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