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Assessment of food environments in obesity reduction: a tool for public health action

Authors:
  • Steno Diabetes Center Copenhagen
  • University of the Witwatersrand School of Public health

Abstract and Figures

The nutrition transition in sub-Saharan African countries has contributed to increased incidence of overweight and obesity, which constitutes a major public health risk. This is especially the case where dietary patterns are influenced by the ready availability of fast foods, resulting in a high intake of fat, sugar and salt. This low-quality diet increases the risk of non-communicable diseases (NCDs). By measuring the food environment geographically, healthy food access gaps can be identified and nutrition-sensitive preventive interventions can be developed. Addresses of food retailers were geocoded to quantify the total number of grocery stores (healthy options) and fast-food outlets (less-healthy options) within wards across Gauteng, the most densely populated province in South Africa. The Modified Retail Food Environment Index (mRFEI) was then computed, representing the percentage of ‘healthy’ food retailers in the area. The mRFEI was widely heterogeneous across Gauteng, ranging from a minimum of 5% to a maximum of 100%, with an average of 33%. The index was highest in the most affluent wards and lowest in the poorest wards, with the latter including a high number of informal settlements. This diverse result was consistent with the high levels of socio-economic inequality that have been observed in Gauteng. For countries such as South Africa currently undergoing rapid nutritional transition, it is imperative to be creative in finding cost-effective ways to identify the structural drivers of NCDs. Through supporting healthy food environments, the public health goals of reducing and preventing obesity and improving nutrition can be reached in settings with a high and increasing burden of obesity.
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Authors:
115
Noluthando Ndlovui Candy Dayi
Benn Sartoriusii Jens Aagaard-Hanseniii
Karen Hofmaniv
Assessment of food environments
in obesity reduction: a tool for
public health action
Assessment of food environments
in obesity reduction: a tool for
public health action
By measuring the food
environment geographically,
healthy food access gaps
can be identified and
nutrition-sensitive preventive
interventions can be
developed
1313
i Health Systems Trust
ii School of Nursing and Public Health, Universit y of KwaZulu-Natal
iii Steno Diabetes Center Copenhagen, Gentofte, Denmark; and MRC Developmental Pathways
for Health Research Unit, Faculty of Health Science, University of the Witwatersrand, Johannesburg
iv PRICELESS SA, School of Public Health, University of the Witwatersrand, Johannesburg
The nutrition transition in sub-Saharan African countries has contributed to
increased incidence of overweight and obesity, which constitutes a major public
health risk. This is especially the case where dietary patterns are influenced by
the ready availability of fast foods, resulting in a high intake of fat, sugar and salt.
This low-quality diet increases the risk of non-communicable diseases (NCDs). By
measuring the food environment geographically, healthy food access gaps can be
identified and nutrition-sensitive preventive interventions can be developed.
Addresses of food retailers were geocoded to quantify the total number of grocery
stores (healthy options) and fast-food outlets (less-healthy options) within wards across
Gauteng, the most densely populated province in South Africa. The Modified Retail
Food Environment Index (mRFEI) was then computed, representing the percentage of
‘healthy’ food retailers in the area.
The mRFEI was widely heterogeneous across Gauteng, ranging from a minimum of
5% to a maximum of 100%, with an average of 33%. The index was highest in the
most affluent wards and lowest in the poorest wards, with the latter including a high
number of informal settlements. This diverse result was consistent with the high levels
of socio-economic inequality that have been observed in Gauteng.
For countries such as South Africa currently undergoing rapid nutritional transition,
it is imperative to be creative in finding cost-effective ways to identify the structural
drivers of NCDs. Through supporting healthy food environments, the public health
goals of reducing and preventing obesity and improving nutrition can be reached in
settings with a high and increasing burden of obesity.
Review
116 2018 SAHR
Introduction
In 2015, overweight and obesity contributed to four million deaths
globally, with cardiovascular disease accounting for 70% of those
deaths, followed by diabetes (15%).1 While obesity is prevalent
in both high-income countries (HICs) and low- and middle-income
countries (LMICs), it affects the poor disproportionately and
contributes to growing health inequities at all levels.2
Non-communicable diseases (NCDs) are driven by a complex
interplay of multiple risk factors. However, a low-quality diet,
which can lead to obesity, combined with reduced physical
activity, increases the risk of NCDs such as hypertension, diabetes,
cardiovascular disease and cancer.3,4 In HICs, NCDs have also
been inversely associated with socio-economic status, with some
studies finding increased consumption of fast food among low-
income and black populations.4–6 Morland and Filomena also
found disparities in the availability of healthy food between racially
segregated urban neighbourhoods in the USA, where there were
hardly any supermarkets in predominantly black areas.7 In another
study conducted in Australia, Burns and Inglis found that those living
in advantaged areas had better access to supermarkets, while those
living in disadvantaged areas lived in closer proximity to fast-food
outlets.8
In 2016, South Africa had the highest prevalence of obesity among
sub-Saharan African countries,9 with 68% of women and 31%
of men considered overweight or obese.10 Sub-Saharan African
countries have undergone a nutrition transition towards a diet high
in sugar and saturated fats but low in fibre,11 which has contributed
to the emergence of overweight and obesity as a critical public
health problem.6,12
There is currently a global discourse on the introduction of planning
laws to regulate the spread of fast-food stores13,14 and food
environments that are not supportive of healthy eating.15 ‘Food
environment’ can be defined as the physical, economic and social
factors that impact the availability, accessibility and adequacy of
food within a region, or as the everyday stimuli that encourage
a consumer’s food choices in a particular way.16 Various factors
influence the choices people make in acquiring and consuming
food; these include household income, proximity to food store
location, food price, pervasive and persuasive food marketing, and
convenience.3,17,18
Numerous studies have also found associations between the number
of neighbourhood fast-food outlets and obesity rates, as fast-food
consumption is linked to increased body mass index (BMI) and
weight gain.4,9,19–22 Promotion and low price of fast food, and
easy access to it, are probably major drivers of obesity and related
NCDs.1,23 However, there are no structured prevention interventions
to improve food environments in South Africa, and prevention is still
aimed largely at an individual level.3
Several studies conducted in other countries have found significant
associations between the number and proximity of fast-food outlets
and the high frequency of purchasing such foods.14,24–26 In South
Africa, the fast-food industry is experiencing exponential growth,
with a predicted annual growth rate of 9% for the 2014–2019
period.27 In measuring the food environment, food access gaps
can be identified, allowing for the development of nutrition-sensitive
preventive interventions that prioritise high-risk areas.9
Overview of study
The purpose of this study was to calculate the Modified Retail
Food Environment Index (mRFEI)28 at the ward level in Gauteng
(GP) and to assess whether food environments varied according to
socio-economic status, thereby generating evidence to inform policy
on the drivers of the obesity epidemic. Obesity is a risk factor for
most NCDs,29 yet measures to reverse the increasing prevalence
of overweight and obesity are still largely absent.30 Utilisation of a
tool such as the mRFEI is an example of an easy method that looks
beyond the health system in the prevention of obesity and NCDs.
Setting
Gauteng was selected as a relevant location to assess the food
environment as it has well-developed infrastructure, making it easier
to find geo-located food outlets as there are proper street addresses,
which would be more difficult in areas that are predominantly
rural. Furthermore, there is a high level of socio-economic inequity
in GP, making it an appropriate location to assess whether food
environments differ by socio-economic status. The study was
conducted at ward level. Based on 2011 demarcations, there were
508 wards, with population density ranging from 4 to 66 664
persons per km2 across the various wards.31
The mRFEI
The mRFEI is an environmental indicator of food access or the
proportion of ‘healthy stores’ within a defined neighbourhood
relative to all accessible stores. The definition of ‘healthy’ and ‘less-
healthy’ food retailers is based on the Centers for Disease Control
and Prevention (CDC) definition, which states that healthy food
retailers include grocery stores and supermarkets, while less healthy
food retailers are fast-food restaurants.32
The mRFEI was chosen to quantify the retail food environment
because it includes both unhealthy and healthy food outlets in a single
measure to give a comprehensive picture of the food environment.33
In the South African context, supermarkets and grocery stores were
used as a proxy for healthy food based on typical food available
in this type of retail format, while fruit and vegetable markets were
excluded due to lack of data. The assumption is that grocery stores
stock healthy foods such as fruit and vegetables, meat and whole
grains. The four major grocery store chains accounting for 97%
of sales in the South African formal food sector were selected for
calculation of the mRFEI; these were Shoprite Checkers, Pick ‘n
Pay, Spar, and Woolworths.11 Different size stores were included,
namely convenience stores, supermarkets and hypermarkets.34
Only fast-food outlets were chosen as a proxy for unhealthy foods in
the assessment. Full-service restaurants (e.g. Spur) were not included
as the quality of food differs between fast-food outlets and full-service
restaurants, with full-service restaurants often providing healthier
food options for health-conscious clients.35 Food outlet locations
were collected from the retailers’ websites and Google Maps and
geocoded using ArcMap version 10.5.36 Once the geographical
co-ordinates of the outlets were recorded, further analysis was
done in ArcMap. The mRFEI was then computed using a formula
developed by the CDC. The index measures the number of ‘healthy’
(grocery store) and ‘unhealthy’ (fast-food outlet) food retailers within
wards across GP, as defined by typical food offerings in the specific
store types. The mRFEI shows the percentage of retailers considered
‘healthy’ out of the total number of food retailers.
Assessment of food environments
SAHR 2018117
• •
• •
• •
• •
• •
• •
• •
• •
• •
••
• •
Decile
Grocery Stores
Municipality
endgeL
10 (= most deprived)
1 (= least deprived)
2
3
4
5
6
7
8
9
Wards
SAIMD
Tshwane
West Rand Johannesburg
Ekurhuleni
Sedibeng
0 510 20 30 40
Kilometers
Area-level deprivation and socio-economic
indicators
In addition to assessing the food environment, socio-economic
factors in the wards were also assessed to investigate if there were
any correlations between the food environment and socio-economic
factors. The South African Index of Multiple Deprivation (SAIMD)
was used to assess the socio-economic factors, together with census
and community survey data from Statistics South Africa (Stats
SA).37,38 The SAIMD is a relative measure of multiple deprivation
expressed at small-area (ward) level and takes into account the
four dimensions of deprivation, namely employment deprivation,
education deprivation, material deprivation, and living environment
deprivation. The four dimensions are combined and weighted
equally to construct the overall deprivation score.
All wards in the country are divided into 10 deciles according to
their poverty rates, with decile 1 being least deprived and decile 10
being the most deprived. Gauteng has very few wards in deciles 8,
9 and 10 (for a detailed breakdown of the indices and indicators
see Noble et al.39). The SAIMD deciles were calculated for the
entire country; consequently, the inequality among wards in GP was
masked as GP has low levels of deprivation compared with other
provinces in South Africa. This prompted an exploration of selected
socio-economic factors such as household income and employment
rates in individual wards, using data directly from Stats SA.
Key findings
In November 2016, there were 1 559 unhealthy food outlets and
709 healthy food outlets in GP (Table 1).
Table 1: Total number of food outlets in Gauteng, South Africa,
2016
Unhealthy outlets Total (N) Healthy outlets Total (N)
KFC 202 Checkers 245
Steers 194 Pick ′n Pay 201
Debonairs Pizza 182 Spar 151
Wimpy 164 Woolworths Food 112
ChesaNyama 159
McDonald’s 140
Nan do’s 115
Roman’s Pizz a 88
Chicken Licken 79
Fishaways 64
The Fish a nd Chip Co. 42
Burger K ing 29
Panarottis 23
Pizza Hut 21
Bar cel o’s 20
Anat 18
Andiccio24 16
Chickin Tyme 3
Total 1 559 Total 709
Distribution of healthy food outlets is highly inequitable in GP. Wards
with the highest number of stores with healthier food options were
located predominately in suburban areas (Figure 1).
Figure 1: Distribution of healthy food outlets and ward-level SAIMD deciles across Gauteng, 2016
118 2018 SAHR
• •
• •
• •
• •
Tshwane
West Rand Johannesburg
Ekurhuleni
Sedibeng
0 510 20 30 40
Kilometers
Decile
Fast food outlets
Municipality
endgeL
10 (= most deprived)
1 (= least deprived)
2
3
4
5
6
7
8
9
Wards
SAIMD
Although the distribution of unhealthy food outlets showed a similar
pattern, there was also a high concentration of fast-food outlets
in wards located in the inner city of Johannesburg and in black
communities (Figure 2). The highest number of unhealthy food
outlets in one ward was 29, while the highest number of healthy
food outlets in a ward was only 17 (Tables 2 and 3). Wards with
the highest number of unhealthy food outlets were located mainly in
Johannesburg (Table 2).
Table 2: Wards with the highest number of unhealthy food
outlets, Gauteng, 2016
Ward
no.
Main suburbs Municipality No. of fast-food
outlets
146 Lynwood Tshwan e 29
2106 Bryanston Johannesburg 28
374 Melros e North Johannesburg 23
4103 Sandton Johannesburg 21
593 Paulshof Johannesburg 20
654 Ridgeway Johannesburg 20
760 Braamfontein Johannesburg 20
8112 Noordwyk Johannesburg 20
9115 Craigavon Johannesburg 20
10 97 Wilgeheuwel Johannesburg 20
Figure 2: Distribution of unhealthy food outlets across Gauteng, 2016
Table 3: Wards with highest number of healthy food outlets,
Gauteng, 2016
Ward
no.
Main suburbs Municipality No. of
grocery
stores
191 Mooikloof Hills Tsh wa ne 17
278 Zwartkop, B ronber rik Tsh wa ne 14
3103 Sandton Johannesburg 13
422 Boksburg Noord Ekurhuleni 13
585 Waparand Tshw an e 12
646 Lynwood Tshwan e 10
7106 Dou glasda le, Brya nston,
Rivonia
Johannesburg 10
875 Welgedacht Ekurhuleni 10
992 Activia Park, Bar vallen Ekurhuleni 9
10 20 Bedfordview, Morninghill Ekurhuleni 9
The highest incidence of wards with no grocery stores was observed
in low-population-density wards, which was to be expected. The
maps (Figure 3) show how the mRFEI varied across the wards in
GP. The majority of wards had low mRFEI percentages for healthy
food outlets, either zero or in the range from 20% to 39.9%. Very
few wards had percentages above 59.9% (Table 4). The low mRFEI
percentages could be indicative of highly obesogenic environments.
Assessment of food environments
SAHR 2018119
City of Ekurhuleni Metropolitan Municipality
mRFEI
No retail food outlets
0 (no grocery store)
0.1 - 19.9
20 - 39.9
40 - 59.9
60 - 85
100 (no fast food outlet)
0 5,5 11 16,5 222,75
Kilometers
0 6.5 13 19.5 263.25
City of Johannesburg Metropolitan Municipality
FEI
No retail food outlets
0 (no grocery store)
0.1 – 19.9
20 – 39.9
40 – 59.9
60 – 85
100 (no fast food outlet)
mR
0 10 20 30 405
Kilometers
Sedibeng District Municipality
mRFEI
No retail food outlets
0 (no grocery store)
0.1 – 19.9
20 – 39.9
40 – 59.9
60 – 85
100 (no fast food outlet)
City of Tshwane Metropolitan Municipality
100 (no fast food outlet)
60 – 85
40 – 59.9
20 – 39.9
0.1 –19.9
0 (no grocery store)
No retail food outlets
IEFRm
Kilometers
20 30 400 105
West Rand District Municipality
mRFEI
No retail food outlets
0 (no grocery store)
0.1 – 19.9
20 – 39.9
40 – 59.9
60 – 85
100 (no fast food outlet)
Kilometers
3425.5178.54.250
Figure 3: mRFEI at ward level in the different municipalities in Gauteng, 2016
Table 4: Number of wards per mRFEI category, Gauteng, 2016
mRFEI categor y
(percenta ge of healthy food retai lers)
Number of wards
0% (No retail food outl ets) 208
0% (No groc ery) 83
0.1 –19 .9% 28
20– 39.9% 73
40–59.9% 70
60 85% 17
100% (No fast food outlets) 29
On average, the municipality with the highest percentage of healthy
food outlets was Ekurhuleni, with an mRFEI of 45%. It was also the
municipality with the highest number of wards with percentages of
100, indicating that several wards only had healthier food outlets
available. The worst-performing municipalities were Johannesburg
and the West Rand, which on average had percentages of 28 and
27 respectively, indicating that only 27/28 out of 100 stores in those
municipalities were likely to provide healthier food options (Table 5).
Overall, in GP there are healthy food options in 33 out of 100
stores. The West Rand and Sedibeng, the most rural municipalities
in GP, had the highest number of wards with no retail food outlets.
120 2018 SAHR
Table 5: Descriptive statistics of mRFEI by municipality, Gauteng, 2016
Municipality Mean Standard
error
Median Min. Max. Number
of ward s
No retail
food outlets
Wards with
no groc ery
sto re (%)
Wards with
no fast-food
out le t (%)
Johannesburg 28 2.76 23 0100 13 0 32 21 5
Tsh wa ne 33 3.35 33 010 0 105 37 16 4
Ekurhuleni 45 3.85 46 0100 101 32 12 12
Sedibeng 29 7. 2 2 17 010 0 72 47 15 6
West Rand 27 4.88 13 0100 10 0 60 16 3
GP 33 1.76 30 0100 508 208 16 6
The third-lowest category of areas with a low percentage of healthy
food outlets (mRFEI 20–39.9) were high-population-density wards
in Johannesburg and Tshwane with low-income black residents.
Table 6 shows that most of the top 10 wards with only fast-food
outlets were in black areas, although only two of those wards fell
in the fourth SAIMD decile, indicating higher levels of deprivation
compared with the other wards. However, a few wards also fell in
the least-deprived decile; they had high average annual household
income compared with other wards, and were mostly occupied by
whites. Two wards with a high percentage of informal dwellings were
among the wards with only unhealthy food outlets. In one of those
wards, most households (55%) were living in informal dwellings.
Another ward in a black community had a very high population
density (over 10 000/km2) yet there wasn’t a single grocery store
or supermarket in the immediate area (Table 6).
Wards with the highest deprivation index in GP (between decile 7
and 10) had no formal food retail outlets at all. The majority of these
wards were in the West Rand, and the population was composed
mainly of low-income blacks living predominantly (over 90%) in
informal dwellings. A similar trend was observed in wards in the
other GP municipalities. The majority of wards with fast-food outlets
only fell in the third SAIMD decile, i.e. among the least-deprived
wards, and the number of wards decreased as the SAIMD decile
increased from low deprivation to high deprivation.
Table 6: Top 10 wards with fast-food outlets only (i.e. mREFI = 0), Gauteng, 2016
Ward
no.
Municipality Main suburb SAIMD Decile
(10=most
deprived)
Majority
ethnicity
Average
annual
household
income
Population
density
Informal
settlements
(%)
Employment
rate (%)
74 Johannesburg Melrose North 1White (47%) 115 1 0 0 1 929 0.3 74. 2
12 West Rand Welverdiend 2White (54%) 57 300 35 1.2 45.2
55 Johannesburg Lindbergh Park 1Black (50%) 57 300 4 132 0.8 60.3
96 Johannesburg Lion Par k informa l
settlement
4Black (74%) 29 400 454 54.6 60. 2
8West Rand Bhongweni 2Black (54%) 57 300 663 6.6 37. 4
122 Johannesburg Zakariyya Park 4Black (91%) 14 6 00 810 33.6 4 3.1
53 Johannesburg Slovoville 2Black (100%) 57 300 700 2.3 45.4
48 Johannesburg Dobsonville 2Black (99%) 29 400 10 159 11.4 45
94 Ekurhuleni Generaal
Albertspark
1White (48%) 230 700 955 0.9 69.8
15 Tsh wa ne Mamelodi 1Black (99%) 29 400 2 805 3.3 46 .1
Correlation between the mRFEI and socio-economic
factors
According to the SAIMD, Gauteng is the second-least-deprived
province in the country, and Tshwane and Johannesburg are among
the 10 least-deprived municipalities.39 The majority of the 10 worst-
performing wards in GP were in predominantly black areas, with the
exception of three wards where whites were slightly in the majority
(Table 6). These wards only had fast-food outlets, without a single
healthy food outlet.
Several township areas had high population densities yet there were
no food retail outlets in those wards. One such example was Zola in
Johannesburg, with a population density of approximately 14 000
people per square kilometre (km2). Zola is a low-income area where
100% of the population are black and 6.5% of households live in
informal settlements. Only 35% of the people living in this area are
employed.40
Wards with the lowest percentage of healthy food outlets also had
relatively low population densities (20–2 500 people/km2), with
the exception of one ward in Tshwane that had a high population
density of over 16 000 people/km2. This ward also predominantly
included black residents who were low middle-income earners.
However, the area had very few informal dwellings (0.2%), and
approximately half of the population was employed.
Assessment of food environments
SAHR 2018121
Discussion
Although individuals make decisions about food choices in a complex
set of physical and social environments,35 the social patterning
of NCDs is influenced by differential exposure to obesogenic
environments leading to the consumption of excess calories.41
The mRFEI revealed that GP is a highly obesogenic environment,
especially the wards in Johannesburg. The geographical distribution
of grocery stores in GP is similar to the pattern in Cape Town (the
Western Cape being the least-deprived province) in that grocery
stores are concentrated in higher socio-economic areas.11 This trend
is also similar to what has been observed in HICs such as the USA
and Australia, where the type of food outlet changes according to
neighbourhood economic status.7,8
According to Rudolph et al.,42 fast-food outlets, small shops and
restaurants play an important role in day-to-day provisioning among
the urban poor in GP, with 55% of households sourcing food from
these outlets at least once a week or more often, especially in the
inner city. In the lower- to middle-income and predominantly black
communities, fast-food outlets are typically more available than in
high-income and white communities in urban areas.35 In addition
to this, communities living in those areas had low average annual
household income. This pattern has also been observed in the UK,
where fast-food outlets cluster in areas of deprivation.2
Preference for unhealthy food is further encouraged and intensified
by the low price, as purchasing power is known to be a key
determinant in whether an individual is willing and able to pay
more for healthy food,16 and healthy food typically costs around
60% more than less-healthy food.35 Furthermore, due to the high
number of informal settlements in GP it is possible that families are
purchasing fast food as they do not have adequate utilities in the
home to cook food; this further emphasises the need for outlets that
provide healthier food options for purchase in such areas.
In many LMICs there are few regulatory frameworks preventing the
promotion of processed fast foods and sugar-sweetened beverages
(SSBs).43 However, it has become evident that policy interventions
against obesity should be directed at both the individual and
the food environment to support healthy choices, as effective
government policies and actions are necessary to increase healthy
food options.44
In targeting the food environment, healthy choices are significantly
easier to make at the individual level (rather than trying to compel
the individual alone to make healthy choices via health-promotion
and educational programmes). Such policies also tend to be more
sustainable as they affect the entire population, thus they can
concretely reverse the environmental drivers of obesity.3 Several
countries such as Ecuador, Australia, India, Brazil, Mexico45 and
Chile have implemented policies to prevent obesity (warning labels
on high-fat, sugar and salt foods). Other countries have gone even
further by increasing import and excise tariffs on SSBs and other
high-sugar products.44
Policy interventions that limit the number of fast-food outlets in
communities, and that lower the cost of healthy foods and increase
the cost of unhealthy foods, can assist in reversing the environmental
drivers of obesity.46 However, without formal structures and policies
similar to the restrictions placed on tobacco, food companies
will continue to shape and influence the polices that should be
controlling them, and the negative trajectory of fast-food expansion
will continue to result in collateral health damage.13
The mRFEI is a powerful tool for public health professionals and
provincial administrators to identify areas where access to healthy
food is limited. However, this study and the mRFEI tool also
have limitations. The study only considered the residential food
environment, and assumed that people live in the same areas in
which they work. Furthermore, assumptions were made about the
types of food sold in grocery stores. The study was also limited to
retail food outlets that could be geo-located via Google Maps. There
may have been a number of retail food outlets not included in the
analysis, in addition to food sold in the informal food sector, which
is quite significant in South Africa.
Further research should explore the links between the mRFEI and
epidemiological data such as NCD morbidity and mortality and
assess how the mRFEI differs in provinces that are not as highly
urbanised as GP. In addition, it would be worthwhile to investigate
the informal food environment in GP, especially in the areas that
were most deprived and that had no formal food retail outlets.
Conclusion
The NCD pandemic is widespread globally and is emerging as a
major public health issue in South Africa. Obesity has been identified
as a key driver, yet prevention strategies have targeted individual
behaviour-change. Policy makers need to address the structural
drivers of obesogenic environments. In addition, the available data
are often aggregated at high levels (low granularity, e.g. provincial)
thus hiding health disparities at local level. The mRFEI provides a
tool for policymakers to visualise the food environment at ward level,
allowing them to implement interventions to reduce obesogenic
environments.
The NCD burden can be prevented by addressing diet and creating
health-promoting living environments. Government should commit
to addressing unhealthy food environments by adopting a wide-
ranging, health-in-all policies approach. Municipalities can play
a fundamental role in this by introducing by-laws that limit the
number of fast-food outlets in communities. They can also zone
land using urban-planning tools and use intentional urban design
to promote citizen health. This process will necessitate multi-sectoral
collaboration with different departments and industries to ensure that
health is not negatively impacted by the activities of other sectors
such as trade and industry. Urgent action is needed to mitigate
the adverse effects of the rapidly changing food environment in
South Africa. Policy makers need to understand the structural and
environmental factors contributing to the health and wellbeing of
communities.
Acknowledgments
One of the authors (KH) was funded by the International Development
Research Centre, Grant number 108424–001.
122 2018 SAHR
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... In South Africa, a middle-income country, one in five adolescents is affected by overweight or obesity (Nwosu et al., 2022). Urban food environments promote unhealthy weight gain, especially in areas of socio-economic marginalization (Ndlovu et al., 2018). For example, the density of fast-food outlets increases with socio-economic deprivation, and supermarket density is highest in the least deprived areas (Ndlovu et al., 2018). ...
... Urban food environments promote unhealthy weight gain, especially in areas of socio-economic marginalization (Ndlovu et al., 2018). For example, the density of fast-food outlets increases with socio-economic deprivation, and supermarket density is highest in the least deprived areas (Ndlovu et al., 2018). This is despite the government's endorsement of the World Health Organization's guideline to protect children from the harms of unhealthy food marketing (WHO, 2023) and several initiatives to regulate the marketing and manufacture of unhealthy foods. ...
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Children’s exposure to outlets selling, and outdoor advertisements marketing, unhealthy foods is an important risk factor for obesity. Yet few policies address the food retail and/or outdoor advertising environment, and research about children’s perceptions is limited, especially in low- and middle-income countries. We used a participatory, multimodal visual/verbal approach to explore urban-dwelling South African primary school students’ perceptions of unhealthy food outlets and outdoor advertisements they encountered on their journeys to school. Forty-one grade 7 students aged 11–14 years participated in drawing and/or photography activities and elicitation discussions. A mixed-methods, triangulated analysis involving the content analysis and extraction of data from research artefacts (33 journey to school drawings and 10 food advertisement photo collages) and thematic analysis of discussion transcripts was conducted. Drawings depicted 175 food outlets, two-thirds (64%) of which sold only unhealthy foods and 125 advertisements, most of which marketed unhealthy food. Unbranded, deep-fried foods prepared and sold by informal traders and independent shops were prominent. Informal and independent traders also sold unhealthy branded foods. Advertisements were primarily for unhealthy foods, especially branded, sugar-sweetened beverages. Participants thought extensive advertising bans, regulation of the sale of unhealthy food to children and other measures were needed to promote children’s health in urban contexts. The results point to the need for food system-wide approaches that address multiple commercial determinants of health, including ‘big food’ advertising, unhealthy food sales by informal and independent traders and programs to address socio-economic influences such as poverty, unemployment and parents’ poor work conditions.
... South Africa has several policies in line with the comprehensive NOURISHING (not an acronym, but a mnemonic for food policy action) policy framework of the World Cancer Research Fund and American Institute for Cancer Research, promoting healthy diets and lifestyle behaviour that may reduce the risk of developing breast cancer (4) . However, as a result of the nutrition transition, the South African food environment is becoming more obesogenic where whole foods are frequently being replaced by ultra-processed foods (5,6) . According to the NOVA food processing classification system (not an acronym but a name and hereafter referred to as the NOVA system), ultra-processed foods are defined as 'formulations of ingredients, mostly of exclusive industry use, that result from a series of industrial processes' (7) . ...
... However, cultural influences and preferences of different countries may also influence the amount of ultra-processed food consumed. The high percentage ultra-processed food contributions to total EI in this study reflect the changes in dietary patterns due to the growing obesogenic food environment in South Africa (5,45) . In the South African context, cost and affordability can also be a major driver of food choices, irrespective of their nutritional value and quality (46) . ...
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This study aimed to investigate the association between consumption of ultra-processed foods, whole foods and breast cancer risk in black women from Soweto, South Africa. A population-based case (n 396)-control (n 396) study matched on age and residence, using data from the South African Breast Cancer study. Dietary intake was assessed using a validated quantified FFQ. Food items were categorised using the NOVA system ((1) unprocessed/minimally processed foods, (2) culinary ingredients, (3) processed foods and (4) ultra-processed foods). Conditional logistic regression models were used to estimate OR and 95 % CI of dietary contributions from each NOVA food group (as a percentage of total energy intake (EI)) and adjusting for potential confounders. Considering contributions to total EI per day, ultra-processed food consumption contributed to 44·8 % in cases and 47·9 % in controls, while unprocessed/minimally processed foods contributed to 38·8 % in cases and 35·2 % in controls. Unprocessed/minimally processed food consumption showed an inverse association with breast cancer risk overall (OR = 0·52, 95 % CI 0·35, 0·78), as well as in pre- and postmenopausal women separately (OR = 0·52, 95 % CI 0·27, 0·95 and OR = 0·55, 95 % CI 0·35, 0·89, respectively) and in women with progesterone positive breast cancer (OR = 0·23, 95 % CI 0·06, 0·86). There was no heterogeneity in association with breast cancer when analyses were stratified according to BMI. No significant associations were observed for the consumption of other NOVA food groups. Intake of unprocessed/minimally processed foods may reduce the risk of developing breast cancer in black women from Soweto, South Africa.
... Studies have shown that there is a correlation between high rates of fast-food outlets in poor neighbourhoods and the increasing rates of obesity (Block et al., 2004). The food environment in the Gauteng area of South Africa shows a low concentration of healthy food outlets in poorer areas compared to wealthier areas (Ndlovu et al., 2018). Unhealthy foods/beverages, which have been defined by the World Health Organisation (WHO) as products high in energy, added fat, added sugar or sodium (Sadeghirad et al., 2016), often include ultra-processed foods (UPFs). ...
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South Africa has high levels of obesity and overweight, which contribute to non-communicable diseases and are associated with poor diets high in ultra-processed foods. Bundling occurs when two or more products are packaged and sold together, often at a discount and is a marketing strategy for unhealthy foods. Given the paucity of data on bundling of unhealthy foods, this exploratory study sought to document how unhealthy foods are bundled together to be more attractive to consumers in Johannesburg, South Africa. Fifty food outlets were included and compared across two regions. In-store photos were taken of bundles and individual items, and data were captured on the costs of the bundle and of individual components. Descriptive analysis and the contents of the bundles based on the photographs and promotional material were conducted. Almost half of all outlets had more than five bundles and nearly a quarter had bundles targeting children. Most children’s meals were burgers, and all had fries as part of the bundle. The average savings per bundle when compared to the combined cost of individual items was R21,39 overall, or 18%. The study demonstrated that unhealthy food bundling is a common practice in food outlets in Johannesburg, often with cost advantages and promotional appeal. Policy options for promoting a favourable food environment include regulating portion sizes of bundles and offering healthy options as part of a bundle. Marketing food to children by bundling unhealthy food with toys is of particular concern and is prohibited in the recently gazetted Regulations Relating to the Labelling and Advertising of Foodstuffs.
... A 2016 study found that in Gauteng, South Africa's most densely populated province, fast-food outlets (n=1559) vastly outnumbered their healthier counterparts, formal grocery stores (n=709). 34 Furthermore, the distribution of food availability followed a social gradient, where grocery stores were predominantly available in higher socio-economic areas, while fast-food outlets were concentrated in areas with lower-to middleincome and predominantly black South African communities. A similar trend was reported for Cape Town. ...
... In contrast, by the end of 2016, these rates had risen to 68% of women and 31% of men [3]. One of the key contributors is an obesogenic environment characterized by the ubiquitous presence of unhealthy cheap foods, extensive marketing [4] and a fast-food industry that is growing exponentially [5]. In parallel, consumption of ultra-processed foods and sugarsweetened beverages has grown [6], together with sedentary behavior [2,7,8]. ...
Article
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Background Overweight and obesity are major risk factors for noncommunicable diseases. This presents a major burden to health systems and to society in South Africa. Collectively, these conditions are overwhelming public healthcare. This is happening when the country has embarked on a journey to universal health coverage, hence the need to estimate the cost of overweight and obesity. Objective Our objective was to estimate the healthcare cost associated with treatment of weight-related conditions from the perspective of the South African public sector payer. Methods Using a bottom-up gross costing approach, this study draws data from multiple sources to estimate the direct healthcare cost of overweight and obesity in South Africa. Population Attributable Fractions (PAF) were calculated and multiplied by each disease’s total treatment cost to apportion costs to overweight and obesity. Annual costs were estimated for 2020. Results The total cost of overweight and obesity is estimated to be ZAR33,194 million in 2020. This represents 15.38% of government health expenditure and is equivalent to 0.67% of GDP. Annual per person cost of overweight and obesity is ZAR2,769. The overweight and obesity cost is disaggregated as follows: cancers (ZAR352 million), cardiovascular diseases (ZAR8,874 million), diabetes (ZAR19,861 million), musculoskeletal disorders (ZAR3,353 million), respiratory diseases (ZAR360 million) and digestive diseases (ZAR395 million). Sensitivity analyses show that the total overweight and obesity cost is between ZAR30,369 million and ZAR36,207 million. Conclusion This analysis has demonstrated that overweight and obesity impose a huge financial burden on the public health care system in South Africa. It suggests an urgent need for preventive, population-level interventions to reduce overweight and obesity rates. The reduction will lower the incidence, prevalence, and healthcare spending on noncommunicable diseases.
... Due to rapid economic growth and urbanization, the ongoing nutrition transition in South Africa contributes to more obesogenic food environments and displacement of whole foods with ultra-processed foods (41)(42)(43)(44) . Intervention is therefore required to improve accessibility, availability and affordability of healthier (nutrient dense) foods such fruit and vegetables to enhance healthier lifestyles of the South African population. ...
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Breast cancer prevention is of great importance to reduce high incidence in South Africa. This study aimed to investigate adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and the association with breast cancer risk in black urban women from Soweto, South Africa. A total of 396 breast cancer cases and 396 population-based controls from the South African Breast Cancer study (SABC) matched on age and demographic settings was included. Validated questionnaires were used to collect dietary and epidemiological data. To assess adherence to these recommendations, an 8-point adherence score was developed, using tertiles among controls for scoring each recommendation (0, 0.5 and 1) with zero indicating the lowest adherence to the recommendations. Odds ratios and 95% confidence intervals were estimated using multivariate logistic regression models to analyse associations between the WCRF/AICR score and breast cancer risk. Greater adherence (>4.5 vs <3.25) to the 2018 WCRF/AICR Cancer Prevention Recommendations was associated with a significant inverse association with breast cancer risk overall (OR=0.54, 95%CI:0.35-0.91) and specifically in postmenopausal women (OR=0.55, 95%CI:0.34-0.95), in cases with oestrogen positive (ER+) and progesterone positive (PR+) breast cancer subtypes (OR=0.54, 95%CI:0.39-0.89 and OR=0.68, 95%CI:0.43-0.89, respectively), and in obese women (OR=0.52, 95%CI:0.35-0.81). No significant association with breast cancer risk was observed in premenopausal women. Greater adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations may reduce breast cancer risk in this black urban population of Soweto. Adherence thereof should be encouraged and form part of cost-effective breast cancer prevention guidelines.
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Large scale metrics of food access are frequently used to identify spatial inequities and for targeting local government intervention. The Food Access Research Atlas by the USDA and the Modified Retail Food Environment Index by the CDC are two such measures frequently used at the census-tract level. This paper uses spatial analyses to identify ways in which they can be refined for better actionability. Outcomes from this paper are threefold: (i) Upon examination of the average food environment, it finds that we might be undercounting rural census tracts with low food access relative to urban tracts; (ii) Disaggregating the often-conflated goals of physical access for food equity and nutritional access for public health might help targeting interventions; (iii) Data on food environments, and underlying assumptions on food consumption behavior should be investigated with greater caution.
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With the growing burden of non-communicable diseases (NCDs), countries across the globe are finding ways to reduce the consumption of ultra-processed food and drinks including sugar-sweetened beverages (SSBs). South Africa implemented a health promotion levy (HPL) in April 2018 as one strategy to reduce sugar intake. Such efforts are frequently countered or mitigated by industry action in various ways, including through marketing and advertising strategies. To better understand trends in the extent of advertising, this paper analyses advertising expenditures and exposure of children to SSB advertisements in South Africa. Using Nielsen’s monthly data on advertising expenditure before and after the introduction of the HPL, for the period January 2013 to April 2019, the results show that manufacturers spent ZAR 3683 million to advertise their products. Advertising expenditure on carbonated drinks accounted for over 60% (ZAR 2220 million) of the total expenditure on SSBs. The results also show that companies spend less in advertising powdered SSBs (an average of ZAR 0.05 million per month). Based on expenditure patterns, television (TV) was the preferred medium of advertisements, with companies prioritizing what is often considered children’s and family viewing time. Urgent mandatory regulations are needed to prevent child-directed marketing.
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Informal settlements in South Africa are facing diverse challenges such as land inaccessibility for food production, poverty, unemployment, malnutrition, and climate change attributing to food insecurity. This paper considered income sources, employment status, household food budget, agricultural production, and anthropometrics as indicators in reviewing the status of this study area. Evaluating geographic dimensions of food accessibility and acceptability locally whilst subsequently determining measures that will promote viable land utilisation options as an intervention within this peri-urban township food environment, required a systematic approach. A general household survey measuring factors contributing to food access was used also evaluating production and consumption patterns of adaptable indigenous crops (n = 200 households). Anthropometric data measured body mass index (BMI) kg/m2, waist circumference (WC) and waist to height ratio (WHtR) to determine levels of malnutrition and health risk factors. Supporting data included a survey from local street vendors and spaza shop owners (n = 25) to determine food items that were frequently accessible and consumed, then compared with the national urban food basket. Land ecotope data was collected to determine if the soil type/s, soil texture, and planting depth are appropriate for effective crop yields in the study area. Secondary data were sourced from the Geographic Information System (GIS) utilised by municipal services and national statistical data. The survey indicated that more than 67.0% of informal dwellers were unemployed and survived on a highly restricted household food budget (
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Objective: This study aimed to apply the newly developed Chile Adjusted Model (CAM) nutrient profiling model (NPM) to the food supply in South Africa (SA) and compare its performance against existing NPMs as an indication of suitability for use to underpin food policies targeted at discouraging consumption of products high in nutrients associated with poor health. Design: Cross-sectional analysis of the SA packaged food supply comparing the CAM to three other NPMs: SA health and nutrition claims (SA HNC), Chilean warning octagon (CWO) 2019, and Pan-American Health Organization (PAHO) NPM. Setting: The SA packaged food supply based on products stocked by supermarkets in Cape Town, SA. Participants: Packaged foods and beverages (N=6474) available in 2018 were analyzed. Results: 49% of products contained excessive amounts of nutrients of concern (considered non-compliant) according to the criteria of all four models. Only 10.9% of products were not excessive in any nutrients of concern (considered compliant) according to all NPMs evaluated. The CAM had an overall non-compliance level of 73.2%, and was comparable to the CWO 2019 for foods (71.2% and 71.1% respectively). The CAM was the strictest NPM for beverages (80.4%) due to the criteria of non-sugar sweeteners and free sugars. The SA HNC was the most lenient with non-compliance at 52.9%. This was largely due to the inclusion of nutrients to encourage, which is a criterion for this NPM. Conclusion: For the purpose of discouraging products high in nutrients associated with poor health in SA, the CAM is a suitable NPM.
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BACKGROUND: Although the rising pandemic of obesity has received major attention in many countries, the effects of this attention on trends and the disease burden of obesity remain uncertain. METHODS: We analyzed data from 68.5 million persons to assess the trends in the prevalence of overweight and obesity among children and adults between 1980 and 2015. Using the Global Burden of Disease study data and methods, we also quantified the burden of disease related to high body-mass index (BMI), according to age, sex, cause, and BMI in 195 countries between 1990 and 2015. RESULTS: In 2015, a total of 107.7 million children and 603.7 million adults were obese. Since 1980, the prevalence of obesity has doubled in more than 70 countries and has continuously increased in most other countries. Although the prevalence of obesity among children has been lower than that among adults, the rate of increase in childhood obesity in many countries has been greater than the rate of increase in adult obesity. High BMI accounted for 4.0 million deaths globally, nearly 40% of which occurred in persons who were not obese. More than two thirds of deaths related to high BMI were due to cardiovascular disease. The disease burden related to high BMI has increased since 1990; however, the rate of this increase has been attenuated owing to decreases in underlying rates of death from cardiovascular disease. CONCLUSIONS: The rapid increase in the prevalence and disease burden of elevated BMI highlights the need for continued focus on surveillance of BMI and identification, implementation, and evaluation of evidence-based interventions to address this problem. (Funded by the Bill and Melinda Gates Foundation.)
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Background Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. Methods This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. Results For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. Conclusions This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.
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This research examines the larger-scale associations between obesity and food environments in metropolitan areas in the United States (US). The US Census County Business Patterns dataset for 2011 was used to construct various indices of food environments for selected metropolitan areas. The numbers of employees engaged in supermarkets, convenience stores, full service restaurants, fast food restaurants, and snack/coffee shops were standardised using the location quotients, and factor analysis was used to produce two uncorrelated factors measuring food environments. Data on obesity were obtained from the 2011 Behavioral Risk Factor Surveillance System. Individual level obesity measures were linked to the metropolitan area level food environment factors. Models were fitted using generalised estimating equations to control for metropolitan area level intra-correlation and individual level sociodemographic characteristics. It was found that adults residing in cities with a large share of supermarket and full-service restaurant workers were less likely to be obese, while adults residing in cities with a large share of convenience store and fast food restaurant workers were more likely to be obese. Supermarkets and full-service restaurant workers are concentrated in the Northeast and West of the US, where obesity prevalence is relatively lower, while convenience stores and fast-food restaurant workers are concentrated in the South and Midwest, where obesity prevalence is relatively higher. The food environment landscapes measured at the metropolitan area level explain the continental-scale patterns of obesity prevalence. The types of food that are readily available and widely served may translate into obesity disparities across metropolitan areas.
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The food environment in markets constrains and signals consumers what to purchase. It encompasses availability , affordability, convenience, and desirability of various foods. The effect of income on dietary consumption is always modified by the food environment. Many agricultural interventions aim to improve incomes, increase food availability and reduce food prices. Their effects on nutrition could be better understood if food environment measures helped to explain how additional income is likely to be spent, and how food availability and prices change as a result of large-scale interventions. Additionally, measurement of the food environment could elucidate food access gaps and inform the design of nutrition-sensitive interventions. This paper reviews existing measures of the food environment, and then draws from these tools to suggest ways the food environment could be measured in future studies and monitoring.
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Local food environments have been linked with dietary intake and obesity in adults. However, overall evidence remains mixed with calls for increased theoretical and conceptual clarity related to how availability of neighbourhood food outlets, and within-outlet food options, influence food purchasing and consumption. The purpose of this work is to develop a programme theory of food availability, supported by empirical evidence from a range of local food environment interventions. A systematic search of the literature will be followed by duplicate screening and quality assessment (using the Effective Public Health Practice Project tool). Realist synthesis will then be conducted according to the Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) publication standards, including transparent appraisal, synthesis and drawing conclusions via consensus. The final synthesis will propose an evidence-based programme theory of food availability, including evidence mapping to demonstrate contextual factors, pathways of influence and potential mechanisms. With the paucity of empirically supported programme theories used in current local food environment interventions to improve food availability, this synthesis may be used to understand how and why interventions work, and thus inform the development of theory-driven, evidence-based interventions to improve healthy food choice and future empirical work. PROSPERO CRD42014009808. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
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This paper provides a comprehensive overview of the recent obesity prevention–related food policies initiated in countries worldwide. We searched and reviewed relevant research papers and government documents, focusing on those related to dietary guidelines, food labeling, regulation of food marketing, and policies affecting food prices. We also commented on the effects and challenges of some of the related policy options. There are large variations regarding what, when, and how policies have been implemented across countries. Clearly, developed countries are leading the effort, and developing countries are starting to develop some related policies. The encouraging message is that many countries have been adopting policies that might help prevent obesity and that the support for more related initiatives is strong and continues to grow. Communicating information about these practices will help researchers, public health professionals, and policy makers around the world to take action to fight the growing epidemic of obesity and other nutrition-related diseases.
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Effective government policies are essential to increase the healthiness of food environments. The International Network for Food and Obesity/non-communicable diseases (NCDs) Research, Monitoring and Action Support (INFORMAS) has developed a monitoring tool (the Healthy Food Environment Policy Index (Food-EPI)) and process to rate government policies to create healthy food environments against international best practice. The aims of this study were to pilot test the Food-EPI, and revise the tool and process for international implementation. New Zealand. Thirty-nine informed, independent public health experts and non-governmental organisation (NGO) representatives. Evidence on the extent of government implementation of different policies on food environments and infrastructure support was collected in New Zealand and validated with government officials. Two whole-day workshops were convened of public health experts and NGO representatives who rated performance of their government for seven policy and seven infrastructure support domains against international best practice. In addition, the raters evaluated the level of difficulty of rating, and appropriateness and completeness of the evidence presented for each indicator. Inter-rater reliability was 0.85 (95% CI 0.81 to 0.88; Gwet's AC2) using quadratic weights, and increased to 0.89 (95% CI 0.85 to 0.92) after deletion of the problematic indicators. Based on raters' assessments and comments, major changes to the Food-EPI tool include strengthening the leadership domain, removing the workforce development domain, a stronger focus on equity, and adding community-based programmes and government funding for research on obesity and diet-related NCD prevention, as good practice indicators. The resulting tool and process will be promoted and offered to countries of varying size and income globally. International benchmarking of the extent of government policy implementation on food environments has the potential to catalyse greater government action to reduce obesity and NCDs, and increase civil society's capacity to advocate for healthy food environments. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Article
Aims: Obesity is one of the most significant global health and social problems, with rates rising dramatically over the past few decades. While the basic drivers of obesity are obvious (more energy consumed than expended), the causes are multifactorial and complex. A decade ago, it was suggested that exploring the ways in which the built environment influenced physical activity and dietary behaviours might provide fertile ground for investigation. This article overviews current evidence and, in particular, emergent themes that are of significance for the United Kingdom. Methods: This article is based on literature extracted from keyword searching of electronic databases. A timeframe of 2006–2016 was used. Results: In the past decade, the research base has grown significantly; while frustratingly some results are still inconclusive or contradictory, it might be argued enough evidence exists to act upon. Themes such as the importance of the journey to school for young people and the multiple environments in which people spend their time are examples of where real progress has been made in the evidence base. Conclusion: Progress towards real change in policy and practice may seem slow; however, the opportunities afforded for health and planning professionals to work together provide a step towards the whole systems approaches to tackle obesity that are desperately needed.
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Context.— Cardiovascular disease (CVD) risk factors are higher among ethnic minority women than among white women in the United States. However, because ethnic minority women are disproportionately poor, socioeconomic status (SES) may substantially explain these risk factor differences.Objective.— To determine whether differences in CVD risk factors by ethnicity could be attributed to differences in SES.Design.— Third National Health and Nutrition Examination Survey conducted between 1988 and 1994.Setting.— Eighty-nine mobile examination centers.Participants.— A total of 1762 black, 1481 Mexican American, and 2023 white women, aged 25 to 64 years, who completed both the home questionnaire and medical examination.Main Outcome Measures.— Ethnicity and years of education (SES) in relation to systolic blood pressure, cigarette smoking, body mass index (BMI, a measure of weight in kilograms divided by the square of height in meters), physical inactivity, non–high-density lipoprotein cholesterol (non–HDL-C [the difference between total cholesterol and HDL-C]), and non–insulin-dependent diabetes mellitus.Results.— As expected, most CVD risk factors were higher among ethnic minority women than among white women. After adjusting for years of education, highly significant differences in blood pressure, BMI, physical inactivity, and diabetes remained for both black and Mexican American women compared with white women (P<.001). In addition, women of lower SES from each of the 3 ethnic groups had significantly higher prevalences of smoking and physical inactivity and higher levels of BMI and non–HDL-C than women of higher SES (P<.001).Conclusions.— These findings provide the greatest evidence to date of higher CVD risk factors among black and Mexican American women than among white women of comparable SES. The striking differences by both ethnicity and SES underscore the critical need to improve screening, early detection, and treatment of CVD-related conditions for black and Mexican American women, as well as for women of lower SES in all ethnic groups.
Article
Environmental factors may contribute to the increasing prevalence of obesity, especially in black and low-income populations. In this paper, the geographic distribution of fast food restaurants is examined relative to neighborhood sociodemographics.Methods Using geographic information system software, all fast-food restaurants within the city limits of New Orleans, Louisiana, in 2001 were mapped. Buffers around census tracts were generated to simulate 1-mile and 0.5-mile “shopping areas” around and including each tract, and fast food restaurant density (number of restaurants per square mile) was calculated for each area. Using multiple regression, the geographic association between fast food restaurant density and black and low-income neighborhoods was assessed, while controlling for environmental confounders that might also influence the placement of restaurants (commercial activity, presence of major highways, and median home values).ResultsIn 156 census tracts, a total of 155 fast food restaurants were identified. In the regression analysis that included the environmental confounders, fast-food restaurant density in shopping areas with 1-mile buffers was independently correlated with median household income and percent of black residents in the census tract. Similar results were found for shopping areas with 0.5-mile buffers. Predominantly black neighborhoods have 2.4 fast-food restaurants per square mile compared to 1.5 restaurants in predominantly white neighborhoods.Conclusions The link between fast food restaurants and black and low-income neighborhoods may contribute to the understanding of environmental causes of the obesity epidemic in these populations.