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. 2015 Jul 1;93(7):446–456. doi: 10.2471/BLT.14.150565

Increased food energy supply as a major driver of the obesity epidemic: a global analysis

L'accroissement de la disponibilité énergétique alimentaire comme facteur majeur de l'épidémie d'obésité : une analyse à l'échelle internationale

El aumento del suministro de energía alimentaria como el principal impulsor de la epidemia de obesidad: un análisis internacional

زيادة إمدادات الطاقة الغذائية باعتبارها المسبب الرئيسي لوباء البدانة: تحليل عالمي

食品能量供给增加是肥胖症流行的主要促成因素：一项全球性的分析

Повышение калорийности пищи как основной фактор, способствующий распространению эпидемии ожирения: глобальный анализ

Stefanie Vandevijvere a,✉, Carson C Chow b, Kevin D Hall b, Elaine Umali a, Boyd A Swinburn a
PMCID: PMC4490816  PMID: 26170502

Abstract

Objective

We investigated associations between changes in national food energy supply and in average population body weight.

Methods

We collected data from 24 high-, 27 middle- and 18 low-income countries on the average measured body weight from global databases, national health and nutrition survey reports and peer-reviewed papers. Changes in average body weight were derived from study pairs that were at least four years apart (various years, 1971–2010). Selected study pairs were considered to be representative of an adolescent or adult population, at national or subnational scale. Food energy supply data were retrieved from the Food and Agriculture Organization of the United Nations food balance sheets. We estimated the population energy requirements at survey time points using Institute of Medicine equations. Finally, we estimated the change in energy intake that could theoretically account for the observed change in average body weight using an experimentally-validated model.

Findings

In 56 countries, an increase in food energy supply was associated with an increase in average body weight. In 45 countries, the increase in food energy supply was higher than the model-predicted increase in energy intake. The association between change in food energy supply and change in body weight was statistically significant overall and for high-income countries (P < 0.001).

Conclusion

The findings suggest that increases in food energy supply are sufficient to explain increases in average population body weight, especially in high-income countries. Policy efforts are needed to improve the healthiness of food systems and environments to reduce global obesity.

Introduction

Overweight and obesity have become major global public health problems. Worldwide, the proportion of adults with a body mass index (BMI) of 25 kg/m2 or greater increased from 28.8% to 36.9% in men, and from 29.8% to 38.0% in women between 1980 and 2013.1 Urgent action from governments and the food industry is needed to curb the epidemic.2 Action needs to be directed at the main drivers of the epidemic to meet the global target of halting the rise in obesity by 2025.3

The drivers of the obesity epidemic have been much debated.4–7 An increased food energy supply and the globalization of the food supply, increasing the availability of obesogenic ultra-processed foods, are arguments for a predominant food system driver5 of population weight gain. Increasing motorization and mechanization, time spent in front of small screens and a decrease in transport and occupational physical activity, point to reducing physical activity as a predominant driver6,8 of the obesity epidemic.

A model used to predict body-weight gain, assuming no change in physical activity, follows the simple rule that a sustained increase in energy intake of 100 kJ per day leads to a predicted increase of 1 kg body weight on average, with half of the weight gain being achieved in about one year and 95% in about three years.9 According to this model, the oversupply of food energy is sufficient to drive the increase in energy intake and increases in body weight observed in the United Kingdom of Great Britain and Northern Ireland and the United States of America.9–11 This is despite the fact that, in the United States, food waste has increased by approximately 50% since 1974, reaching about 5800 kJ per person per day in 2003.12 Here we test the hypothesis that an increase in food energy supply is sufficient to explain increasing population body weight, using data from 24 high-income, 27 middle-income and 18 low-income countries.

Methods

Food energy supply

Food balance sheets of the Food and Agriculture Organization of the United Nations (FAO) estimate the food supply of countries, by balancing local production, country-wide stocks and imports with exports, agricultural use for livestock, seed and some components of waste. Waste on the farm, during distribution and processing, as well as technical losses due to transformation of primary commodities into processed products are usually taken into account. However, losses of edible food, e.g. during storage, preparation and cooking, as plate-waste or domestic animal feed, or thrown away, are not considered. The data are expressed as the annual per capita supply of each food item available for human consumption.13 The FAO’s database contains national level data from 1961 to 2010 for 183 countries. For each country, data on food energy supply were extracted to match the time periods of data on adult body weight.

Measured body weight

Three major strategies were used to collect data on measured average adult body weight. First, an electronic search of major databases on obesity prevalence and BMI was performed, including the World Health Organization’s (WHO) global infobase,14 WHO’s global database on BMI,15 the International Association for the Study of Obesity (now World Obesity Federation) database16 and the Organisation for Economic Co-operation and Development’s health data.17 As these databases only included data on obesity rates or mean BMI, the original sources of the data were searched. Second, data on average measured body weight were gathered from reports of national health and nutrition surveys in various countries. The WHO MONICA project18 and WHO STEPwise approach to surveillance (STEPS) country reports19 included anthropometric measures for male and female adult samples. We also calculated body weight for women of child-bearing age using mean BMI and height data from Demographic and Health Surveys.20 Third, an electronic search of Medline was conducted. For each country, a separate search was performed using the following keywords: “obesity”, “weight”, “anthropometric”, “BMI”, “health survey” and “national survey” (using the Boolean operator OR). Finally, specific national health and/or nutrition surveys identified by some of the above sources were electronically searched.

Studies fulfilling the following criteria were extracted: (i) weight was measured after 1961 and again before 2010 (to match the FAO food balance sheet data); (ii) the study samples were representative of a national or subnational adolescent or adult population; (iii) the survey method was comparable with previous or future surveys conducted in the country; (iv) the year in which each survey was conducted could be identified; at least four years elapsed between the two surveys; and (v) FAO food supply data were available for the relevant period.

If there were more than two eligible studies from a country, the surveys which we judged to be the best quality were included. Criteria for estimating study quality included national representativeness, sample size and length of time between surveys.

Demographic data

Demographic data (total population, by age and sex) were retrieved from the United Nations Department of Economic and Social Affairs.21 Average female and male height at survey time points were derived from http://www.averageheight.co/. For 13 countries, data were not available and average height data from a neighbouring country were used for calculating energy requirements.

Data analysis

Three types of analysis were performed. First, we compared the changes in food energy supply with changes in average body weight over time for each country. Second, estimates of population energy requirements at survey time points were performed for each country using Institute of Medicine equations.22 Low active physical activity levels (1.4 ≤ PAL <1.6) were assumed for high- and upper-middle-income countries. Active physical activity levels (1.6 ≤ PAL <1.9) were used for all other countries. Finally, we used a physiologically-based, experimentally-validated predictive energy intake body-weight model, to estimate the change in average population energy intake that would be required to account for the observed change in average body weight.9

Results

In total, 83 countries had at least two surveys with data on measured body weight; 24 countries had more than two surveys at different time points. We excluded countries where the period between surveys was less than four years (eight countries), survey populations were not comparable in terms of area representativeness (eight countries) or FAO food supply data for the country were not available (three countries). Survey pairs from 69 countries were included. Of those, 36 survey pairs included data for women of childbearing age only. One survey pair (Saudi Arabia) included data for men only. Data from 24 high-income, 27 middle-income and 18 low-income countries were included. The average period between the surveys was 12 years (range 4–37 years; Table 1). At the time of the initial survey, food energy supply was greater than the average energy requirements in 52 countries. For 37 of these countries, this excess food energy supply was more than 2000 kJ/day (Table 1).

Table 1. Countries and surveys included in a global analysis of food energy supply and body weight, 1971–2010.

Country
 Income level of country
 Year
Age range, years
 Food energy supply,
kJ/day
First survey Second survey Survey 1 Survey 2 First survey Second survey First survey Change Excess at the first survey
Algeria Upper-MIC 1986 2003 Cross-sectional survey STEPS Survey 16–65 25–64 11 385 1 464 2 958
Australia HIC 1995 2007 National Nutrition Survey National Health Survey ≥ 18 ≥ 18 12 929 594 2 987
Bangladesh LIC 1996 2007 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 849 1 423 506
Barbados HIC 1995 2000 ICSHIB Study Food Consumption and Anthropometric Survey ≥ 25 18–96 11 996 −146 2 414
Belgium HIC 1986 1991 WHO MONICA WHO MONICA 25–34 25–34 14 439 515 4 008
Benin LIC 1996 2001 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 929 54 715
Bolivia (Plurinational State of) Lower-MIC 1994 2008 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 376 544 −285
Burkina Faso LIC 1993 1998 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 10 092 −109 728
Cambodia LIC 2000 2010 National Demographic Health Survey STEPS Survey 15–49 25–64 8 908 1 059 197
Cameroon Lower-MIC 1998 2004 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 870 774 −649
Canada HIC 1971 2008 Nutrition Canada Survey Canadian Community Health Survey 20–69 ≥ 18 12 159 2 339 2 636
Chad LIC 1996 2004 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 7 740 895 −1 665
Chile HIC 2003 2009 National Health Survey National Health Survey ≥ 17 ≥ 15 12 067 100 2 665
China Upper-MIC 1991 2000 China Health and Nutrition Survey Cross-sectional survey 20–45 35–74 10 447 1 548 1 996
Colombia Upper-MIC 1995 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 10 837 188 2 510
Czech Republic HIC 1993 2002 Health Status of the Czech Population Survey Health Status of the Czech Population Survey 15–75 15–75 12 719 833 2 653
Denmark HIC 1983 1991 WHO MONICA WHO MONICA 25–64 25–64 12 740 862 2 795
Dominican Republic Upper-MIC 1991 1996 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 025 301 749
Egypt Lower-MIC 1992 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 13 142 741 3 284
Eritrea LIC 1995 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 6 569 −63 −2 272
Ethiopia LIC 2000 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 7 565 761 −1 343
Fiji Upper-MIC 1980 2004 National Food and Nutrition Survey STEPS Survey (National Nutrition Survey) 18–55 18–55 10 372 2 301 88
Finland HIC 1987 1997 Cross-sectional population survey Cross-sectional population survey 25–64 25–64 12 318 849 2 289
France HIC 1986 2009 WHO MONICA National Epidemiological Survey 35–64 ≥ 18 14 707 67 5 067
Gabon Upper-MIC 2000 2009 National Demographic Health Survey STEPS Survey 15–49 15–64 11 234 251 2 653
Germany HIC 1983 2009 WHO MONICA Microcensus – Health Questions 25–64 ≥ 18 14 267 582 4 305
Ghana Lower-MIC 1993 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 468 1 289 213
Haiti LIC 1994 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 7 163 703 −1 929
Hungary Upper-MIC 1982 1987 WHO MONICA WHO MONICA 25–64 25–64 14 836 753 4 640
Iceland HIC 1983 1993 WHO MONICA WHO MONICA 25–64 25–64 13 334 −343 2 757
India Lower-MIC 1998 2007 National Demographic Health Survey STEPS Survey 15–49 15–64 9 657 113 715
Indonesia Lower-MIC 1983 2001 Cross-sectional survey STEPS Survey 15–49 15–65 9 615 276 1 423
Iran (Islamic Republic of) Upper-MIC 2004 2009 STEPS Survey STEPS Survey 15–65 15–64 13 129 25 3 540
Ireland HIC 1985 2009 Cross-sectional survey National Adult Nutrition Survey 35–64 18–64 14 966 109 5 209
Israel HIC 1985 2000 WHO MONICA National Health and Nutrition Survey 25–64 25–64 13 979 728 4 284
Italy HIC 1983 1993 WHO MONICA WHO MONICA 25–64 25–64 14 493 71 4 749
Jordan Upper-MIC 1997 2002 Cross-sectional survey National Demographic Health Survey ≥ 25 15–49 11 355 720 2 778
Kazakhstan Upper-MIC 1995 1999 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 13 117 −3 778 4 448
Kenya LIC 1993 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 7 954 444 −1 318
Lebanon Upper-MIC 1997 2009 National cross-sectional survey National cross-sectional survey ≥ 20 ≥ 20 12 924 268 2 983
Madagascar LIC 1997 2005 National Demographic Health Survey STEPS Survey 15–49 25–64 8 732 155 −67
Malawi LIC 1983 2009 Cross-sectional survey STEPS Survey ≥ 15 25–64 9 012 686 −690
Malaysia Upper-MIC 1996 2005 National Health & Morbidity Survey STEPS Survey ≥ 20 25–64 12 355 −481 3 745
Mali LIC 1995 2006 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 021 1 155 −322
Malta HIC 1984 2006 WHO MONICA Lifestyle Survey 25–64 18–65 12 711 1 682 3 130
Mauritania Lower-MIC 2000 2006 National Demographic Health Survey STEPS Survey 15–49 15–64 11 351 59 1 636
Mongolia Lower-MIC 2005 2009 STEPS Survey STEPS Survey 15–64 15–64 9 410 774 −891
Morocco Lower-MIC 1992 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 12 117 1 331 2 611
Mozambique LIC 1997 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 263 247 −728
Nepal LIC 1996 2007 National Demographic Health Survey STEPS Survey 15–49 15–64 9 234 674 766
Netherlands HIC 2000 2009 Health Survey Health Survey 15–65 15–65 13 389 255 2 941
New Zealand HIC 1982 2009 WHO MONICA NZ Adult Nutrition Survey 35–64 15–71 12 878 389 3 234
Niger LIC 1992 2006 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 142 1 598 −1 025
Nigeria Lower-MIC 1999 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 11 109 −134 1 741
Norway HIC 1990 2001 Prospective population-based survey Prospective population-based survey ≥ 20 20–79 13 196 992 3 280
Peru Upper-MIC 1991 2009 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 075 1 653 874
Poland HIC 1983 1992 WHO MONICA WHO MONICA 35–64 35–64 14 046 243 4 339
Rwanda LIC 2000 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 7 812 674 −1 385
Saudi Arabia HIC 1996 2004 Cross-sectional survey STEPS Survey ≥ 19 25–64 12 247 519 1 448
Senegal Lower-MIC 1992 2005 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 9 427 506 −155
South Africa Upper-MIC 1998 2003 National Demographic Health Survey National Demographic Health Survey 15–65 15–65 11 929 397 2 243
Sweden HIC 1985 2001 WHO MONICA INTERGENE Project 25–64 25–64 12 456 636 2 703
Switzerland HIC 1985 1994 WHO MONICA WHO MONICA 35–64 25–64 14 242 −310 4 590
Togo LIC 1998 2010 National Demographic Health Survey STEPS Survey 15–49 15–64 9 150 736 −469
Turkey Upper-MIC 1993 2003 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 15 531 −602 7 251
United Kingdom HIC 1993 2009 Health Survey for England Health Survey for England ≥ 16 ≥ 16 13 468 891 3 724
United States HIC 1972 2004 National Health and Nutrition Examination Survey National Health and Nutrition Examination Survey 20–74 20–74 12 770 3 213 2 979
Uzbekistan Lower-MIC 1996 2002 National Demographic Health Survey Health Examination Survey 15–49 15–49 12 242 −2 615 2 803
Zimbabwe LIC 1994 1999 National Demographic Health Survey National Demographic Health Survey 15–49 15–49 8 037 280 −1 343
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LIC: low-income country; Lower-MIC: lower-middle-income country; HIC: high-income country; ICSHIB: the International Comparative Study of Hypertension in Blacks; Upper-MIC: upper-middle-income country; WHO: World Health Organization.

Note: Estimations of population energy requirements were performed for each country using the Institute of Medicine equations for males and females.22 Energy excess was calculated by subtracting energy requirements at the first survey from the energy supply at the same survey.

For 56 countries (81%) both food energy supply and body weight increased between the survey pairs. For 45 of these countries (80%) the increase in food energy supply was more than sufficient to explain the increase in average body weight. This is shown in Fig. 1 with 56/69 countries being in the top right quadrant and 45/56 being to the right of the model-predicted change in energy intake needed to produce the increase in mean body weight for that country. This same pattern was observed for countries of all income levels (Fig. 2, Fig. 3, Fig. 4 and Fig. 5). For 11 countries (Benin, Chile, the Dominican Republic, Gabon, India, Indonesia, Ireland, Italy, Lebanon, Mauritania and New Zealand) in the top right quadrant, the increase in food energy supply was insufficient to account for the observed increase in weight (Fig. 1).

Fig. 1.

Change in food energy supply and change in average body weight for 69 countries, 1971–2010

LIC: low-income countries; Lower-MIC: lower-middle-income countries; HIC: high-income countris; Upper-MIC: upper-middle-income countries.

Note: The dots representing the modelled data are the estimated change in energy intake required to account for the change in average body weight of the population.9

Fig. 1

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Fig. 2.

Change in food energy supply and change in average body weight for 24 high-income countries, 1971–2009

HIC: high-income countries.

Note: The dots representing the modelled data are the estimated change in energy intake required to account for the change in average body weight of the population.9

Fig. 2

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Fig. 3.

Change in food energy supply and change in average body weight for 15 upper-middle-income countries, 1980–2009

Upper-MIC: upper-middle-income countries.

Note: The dots representing the modelled data are the estimated change in energy intake required to account for the change in average body weight of the population.9

Fig. 3

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Fig. 4.

Change in food energy supply and change in average body weight for 12 lower-middle-income countries, 1983–2009

Lower-MIC: lower-middle-income countries.

Note: The dots representing the modelled data are the estimated change in energy intake required to account for the change in average body weight of the population.9

Fig. 4

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Fig. 5.

Change in food energy supply and change in average body weight for 18 low-income countries, 1983–2009

LIC: low-income countries.

Note: The dots representing the modelled data are the estimated change in energy intake required to account for the change in average body weight of the population.9

Fig. 5

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Five countries (Barbados, Burkina Faso, Kazakhstan, Nigeria and Switzerland) experienced reductions in both food energy supply and average body weight. For Kazakhstan the food energy supply decreased by 3778 kJ/day, from 13 117 kJ/day to 9339 kJ/day over a four year period (Table 1), accompanied by a decrease in average body weight of 0.9 kg. For the four other countries, decreases in food energy supply were much more modest (100–300 kJ/day; Table 1).

For five other countries (Eritrea, Iceland, Malaysia, Turkey and Uzbekistan), discordant changes were observed with reductions in food energy supply over the same period as increases in average body weight. The decrease in food energy supply was highest for Uzbekistan (2615 kJ/day) and lowest for Eritrea (63 kJ/day; Table 1). Apart from Eritrea, food energy supply at baseline for those five countries was relatively high (ranging from 12 242 to 15 531 kJ/day) and higher than the values of at least half of the other countries included in this study. In addition, excess food energy supply at baseline was high for those five countries (2757–7251 kJ/day; Table 1).

For three countries (the Islamic Republic of Iran, Rwanda and South Africa) there were discordant changes in the other direction with increases in food energy supply over the same period as reductions in average body weight. However, for two of those countries, the change in average weight was small (a reduction of 5 g for the Islamic Republic of Iran and 100 g for South Africa). In Rwanda, the reduction in weight was 800 g while the food energy supply over the same time period increased by 674 kJ/day (Table 1).

The correlation between the change in food energy supply and change in average body weight was significant (P = 0.011). When stratifying by type of country, associations were significant for high-income countries (P < 0.001), but not for other country groups.

Discussion

For most of the countries included in this study, the change in per capita food energy supply was greater than the change in food energy intake theoretically required to explain the observed change in average body weight. The associations between changes in food energy supply and average population body weight were significant overall and for high-income countries. This suggests that, in high-income countries, a growing and excessive food supply is contributing to higher energy intake, as well as to increasing food waste.12

Other factors, such as a decrease in physical activity, may also lead to an increase in body weight and could occur simultaneously with an increase in food energy supply. It has been shown that among 3.7 million participants in the United States at the county level, increased physical activity has only a very small impact on obesity prevalence.23 It is likely that in some countries, such as China, the impact of reduced physical activity on obesity is more important.24,25 A reduction in physical activity with no compensatory drop in energy intake will cause weight gain until sufficient weight is gained to create energy balance (through both an increased resting metabolic rate and increased energy required to move the larger body).

Researchers have suggested additional contributing factors for obesity, such as pollutants, infections and changes in the gut microbiota. These factors have an effect on metabolism, body composition and/or energy balance efficiencies. However, more evidence is needed to understand the importance of these factors in weight gain.26 Ideally, the cause of obesity in humans would be assessed through randomized controlled trials, where food energy availability is increased randomly and average body weight is then measured. However, such an experiment is not practical, since it is difficult to measure food intake over long time periods and it would require that non-obese subjects be randomly assigned to environments with different food energy supplies.

Our findings suggest that there is an excess of energy available from an increasing national average food energy supply in countries of varying income levels.9 Therefore, policy efforts need to focus on reducing population energy intake through improving the healthiness of food systems and environments.5,11,27 Achieving WHO’s target to halt the rise in obesity by 2025 will require major action by governments and the food industry.3 A combination of several policy actions will be needed to significantly improve diets and reduce overconsumption.2 These policies include restriction of unhealthy food marketing to children, front-of-pack supplementary nutrition labelling,28 food pricing strategies,29 improving the quality of foods in schools30 and other public sector settings. The impact of trade and investment agreements31 and agricultural policies32 on domestic food environments should be assessed.

The main strength of this study is the inclusion of nationally representative body weight and food energy supply data for a range of countries and over many years. Weaknesses include the limitations on the measurement of national per capita food energy supply (e.g. losses of edible food during storage, preparation and cooking, as plate-waste or domestic animal feed, and subsistence farming are not taken into account) and the variable quality of energy supply data. In addition, low- and middle-income countries, in different phases of the nutrition transition,33,34 are likely to have poorer data and have higher levels of subsistence farming, which is not included in the FAO food supply data.13

The association between changes in food supply and changes in body weight may be confounded by changes in physical activity levels, changes in food waste or changes in the demographic profile of countries. Demographic changes, particularly size, ageing, and racial/ethnic diversification of populations, may contribute to increasing obesity levels.35 About half the data sets on weight status used in this study are for women only and thus only represent half of the population. A limitation of the energy-balance model is that it assumes that metabolic physiology and physical activity levels are similar globally. While this is likely to be true for industrialized countries for which accurate data on the relationship between energy expenditure and body weight are available and for which the model has been calibrated, it is not clear how well this assumption applies for developing countries. The model also assumes that population-wide changes in physical activity are negligible over the periods investigated.

In conclusion, in high-income countries, observed increases in body weight over recent decades are associated with increased food energy supply. In addition, increases in food energy supply are sufficient to explain increases in average population weight. Due to the nutrition transition and a potential decrease in physical activity, the same pattern is expected to occur in low- and middle-income countries in the future. Policy efforts should focus on reducing population energy intake through improving the healthiness of food systems and environments.

Funding:

Stefanie Vandevijvere and Boyd Swinburn are funded by the University of Auckland Vice Chancellor’s strategic fund. Carson Chow and Kevin Hall are funded by the intramural research programme of the NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), USA.

Competing interests:

None declared.

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