The Social Determinants of Health: Globalization...

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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]

mailto:[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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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

http://apps.who.int/BMI/INDEX.JSP?INTROPAGE=INTRO_3.HTML

http://www.afro.who.int/press/periodicals/healthmonitor/jan-jun2008.pdf

http://www.who.int/gho/ncd/methods/en/index.html


15

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


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


17

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


18

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


19

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.


20

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.


21

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


22

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

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23

Figure 4 continued

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Figure 4 continued

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