The authors have declared that no competing interests exist.
Population intake goals intended to prevent diet-related non-communicable diseases (NCDs) have been defined for multiple nutrients. Yet, little is known whether the existing food supply in Africa is in conformity with these goals or not. We evaluated the African food balances against the recommendations for macronutrients, free sugars, types of fatty acids, cholesterol and fruits and vegetables over 1990 to 2017, and provided regional, sub-regional and country-level estimates.
The per capita supply of 95 food commodities for 45 African countries (1990–2017) was accessed from the FAOSTAT database and converted into calories, carbohydrate, fat, protein, free sugars, cholesterol, saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids contents using the Food Data Central database. The supply of fruits and vegetables was also computed.
In Africa the energy supply increased by 16.6% from 2,685 in 1990 to 3,132 kcal/person/day in 2017. However, the energy contribution of carbohydrate, fat and protein remained constant and almost within acceptable range around 73, 10 and 9%, respectively. In 2017, calories from fats surpassed the 20% limit in upper-middle- or high-income and Southern Africa countries. Energy from SFA remained within range (<10%) but that of PUFA was below the minimum desirable level of 6% in 28 countries. Over the period, energy from free sugars remained constant around 7% but the figure exceeded the limit of 10% in upper-middle- or high-income countries (14.7%) and in Southern (14.8%) and Northern (10.5%) sub-regions. Between 1990 and 2017 the availability of dietary cholesterol per person surged by 14% but was below the upper limit of 300 mg/day. The supply of fruits and vegetables increased by 27.5% from 279 to 356 g/capita/day; yet, with the exception of Northern Africa, the figure remained below the target of 400 g/capita/day in all sub-regions.
According to this population level data, in Africa most population intake goals are within acceptable range. Yet, the supply of fruits and vegetables and PUFAs are suboptimal and the increasing energy contributions of free sugars and fats are emerging concerns in specific sub-regions.
Every year 41 million deaths, equivalent to 71% of the total deaths, occur globally due to non-communicable diseases (NCDs) [
NCDs once considered as diseases of affluence are now disproportionately affecting low- and middle-income countries (LMIC) [
NCDs have multiple genetic, environmental and behavioural determinants. Yet, the epidemiological shift observed in the last few decades is primarily attributable to changes in few major modifiable risk factors including dietary factors [
In 2003 WHO and Food and Agriculture Organization of the United Nations (FAO) proposed population intake goals for preventing NCDs [
In Africa where there is active rise in NCD-related mortality, limited information is available whether the existing food supply is in conformity with these goals or not. A recent study that described the global food supply over the period of 1961 and 2013 reported that high income countries are moving towards more dietary diversification and reduced supply of sugars while low-income countries remain relatively unchanged or had moved towards poor diet combinations [
The current study evaluated the African food balances against population intake recommendations for macronutrients, free sugars, fatty acids, dietary cholesterol and fruits and vegetables defined for preventing diet-related NCDs, assessed trends over three decades (1990–2017), and provided regional (continental), sub-regional (geographic and gross national income classifications) and country-level estimates.
The analysis was made based on the food balance sheets (FBS) compiled by FAO for 45 of the 54 African countries for the period 1990 to 2017 [
The per capita supply of 95 major food commodities (kg/capita/day) was downloaded for each country-year. Then the energy, carbohydrate, fat, protein, SFA, MUFA, PUFA and cholesterol contents were determined by referring to a standard food composition database [
Food Balance Sheet, also known as Food Disappearance data, is estimation of the food supply of a country in a given period. FAO based on multiple data sources including official reports, determines the production, import, export, changes in stocks and non-food uses for major food commodities and estimates the food available for human consumption in a territory. FAO annually publishes the supply statistics of more than 90 primary (e.g. eggs, milk) and processed commodities (e.g. butter) for about 180 countries as the average of supply over the past three-year period [
The supply of 95 commodities (kg/person/day) was converted into nutrient values (calorie, carbohydrate, protein, fat, SFA, MUFA, PUFA, sugar and cholesterol) using the US Department of Agriculture (USDA) Food Data Central food composition database [
The FBS provides all data on meat in terms of carcass weight that includes non-edible bones [
The amount of all energy-yielding macronutrients (carbohydrate, protein and fat) and alcohol available in the food supply for each country-year was converted into calories using Atwater specific factors [
WHO defines “free sugars” as “
The supply of fruits and vegetables (g/capita/day) was estimated by summing the balances of all specific fruits and vegetables represented in the FBS. In line with the approach used by WHO [
The food supply statistics for each country-year was downloaded from the old (1990–2012) [
Between 1990 and 2017, the per capita supply of energy in Africa increased by 16.6% from 2,685 in 1990 to 3,132 kcal/person/day in 2017. The rates of increase were above the regional average in Central (28.8%) and Western (22.2%) sub-regions. Figures from 2017 indicated that the energy supply (kcal/person/day) was highest in Southern (3,406) and lowest in Eastern (2,625) sub-regions. Over the period, Africa had also seen significant improvements in the supply of all energy-yielding nutrients. Protein supply increased by 19.0%, whereas carbohydrate and fat supplies rose by 16.7 and 14.6%, respectively.
Comparison based on national income levels indicated that in 2017 the energy supply (kcal/person/day) in low-income countries (2,771) was much lower than that of upper-middle- or high-income countries (3,448). However, rates of improvements for all energy yielding nutrients were substantially higher in low- than in high-income countries (
Supply of macronutrients | Year | % change | ||||||
---|---|---|---|---|---|---|---|---|
1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2017 | ||
Calorie supply (kcal/capita/day) | ||||||||
Africa | 2685 | 2791 | 2856 | 2942 | 3020 | 3144 | 3132 | 16.6 |
National income level | ||||||||
Low-income | 2208 | 2233 | 2355 | 2462 | 2562 | 2765 | 2771 | 25.5 |
Lower-middle-income | 2907 | 3063 | 3106 | 3194 | 3271 | 3338 | 3312 | 13.9 |
Upper-middle- or high-income | 3025 | 3026 | 3118 | 3182 | 3237 | 3402 | 3448 | 14.0 |
Sub-region | ||||||||
Northern | 3208 | 3283 | 3343 | 3412 | 3549 | 3713 | 3675 | 14.6 |
Central | 2283 | 2320 | 2436 | 2628 | 2834 | 2940 | 2940 | 28.8 |
Southern | 3004 | 2994 | 3087 | 3155 | 3201 | 3346 | 3406 | 13.4 |
Eastern | 2267 | 2217 | 2286 | 2385 | 2436 | 2633 | 2625 | 15.8 |
Western | 2700 | 3028 | 3102 | 3195 | 3261 | 3309 | 3300 | 22.2 |
Carbohydrate supply (g/capita/day) | ||||||||
Africa | 495.0 | 518.3 | 527.4 | 536.6 | 549.3 | 579.0 | 577.6 | 16.7 |
National income level | ||||||||
Low-income | 411.8 | 414.9 | 439.9 | 455.6 | 469.8 | 509.6 | 511.5 | 24.2 |
Lower-middle-income | 538.6 | 575.0 | 576.7 | 585.8 | 600.0 | 620.9 | 616.7 | 14.5 |
Upper-middle- or high-income | 513.4 | 510.0 | 526.0 | 518.4 | 518.5 | 562.8 | 574.4 | 11.9 |
Sub-region | ||||||||
Northern | 603.0 | 612.4 | 619.7 | 638.5 | 656.3 | 691.8 | 684.2 | 13.5 |
Central | 403.9 | 420.2 | 431.2 | 465.2 | 506.6 | 513.0 | 521.0 | 29.0 |
Southern | 513.4 | 508.9 | 524.7 | 517.7 | 516.9 | 556.3 | 570.3 | 11.1 |
Eastern | 431.0 | 424.8 | 440.7 | 450.0 | 453.5 | 491.1 | 488.2 | 13.3 |
Western | 488.1 | 560.1 | 566.5 | 571.4 | 589.8 | 615.2 | 615.7 | 26.1 |
Protein supply (g/capita/day) | ||||||||
Africa | 66.6 | 67.6 | 71.0 | 74.8 | 78.1 | 80.5 | 79.3 | 19.0 |
National income level | ||||||||
Low-income | 54.9 | 55.6 | 59.0 | 62.7 | 67.0 | 70.8 | 71.4 | 30.0 |
Lower-middle-income | 71.0 | 72.3 | 76.2 | 80.3 | 83.7 | 85.1 | 82.9 | 16.8 |
Upper-middle- or high-income | 83.9 | 82.7 | 85.1 | 88.0 | 88.4 | 90.2 | 89.6 | 6.8 |
Sub-region | ||||||||
Northern | 80.8 | 83.5 | 88.8 | 92.8 | 98.7 | 101.8 | 100.1 | 23.8 |
Central | 57.6 | 55.3 | 62.2 | 66.5 | 72.9 | 83.5 | 80.3 | 39.4 |
Southern | 82.9 | 81.2 | 83.6 | 86.8 | 86.9 | 88.0 | 87.9 | 6.0 |
Eastern | 55.0 | 53.7 | 55.0 | 58.5 | 61.1 | 66.5 | 66.2 | 20.4 |
Western | 65.1 | 68.6 | 73.1 | 77.8 | 81.1 | 79.2 | 77.9 | 19.7 |
Fat supply (g/capita/day) | ||||||||
Africa | 55.3 | 56.8 | 58.9 | 63.1 | 64.7 | 63.7 | 63.4 | 14.6 |
National income level | ||||||||
Low-income | 42.4 | 44.2 | 45.2 | 48.7 | 51.7 | 54.6 | 54.3 | 28.0 |
Lower-middle-income | 60.2 | 61.2 | 64.2 | 68.7 | 69.4 | 66.3 | 65.9 | 9.5 |
Upper-middle- or high-income | 73.6 | 77.4 | 79.9 | 88.9 | 94.7 | 91.6 | 92.1 | 25.2 |
Sub-region | ||||||||
Northern | 65.3 | 68.0 | 70.3 | 68.3 | 73.6 | 74.8 | 74.8 | 14.6 |
Central | 53.0 | 51.9 | 57.0 | 61.3 | 62.8 | 66.0 | 64.5 | 21.6 |
Southern | 72.0 | 75.1 | 77.9 | 86.9 | 92.2 | 89.5 | 90.2 | 25.2 |
Eastern | 41.5 | 39.4 | 39.8 | 45.4 | 48.4 | 50.9 | 51.4 | 23.8 |
Western | 58.0 | 62.1 | 66.0 | 72.9 | 70.4 | 64.5 | 63.6 | 9.6 |
Between 1990 and 2017, the contribution of carbohydrates to the total daily energy supply in Africa remained constant around 73%. In 2017, the carbohydrate’s contribution was relatively lower in upper-middle- or high-income (65.6%) and Southern Africa (66.1%) countries but both were within the acceptable range (55–75%) (
Contribution to total calorie supply (%) | Year | ||||||
---|---|---|---|---|---|---|---|
1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2017 | |
Carbohydrate contribution (%) | |||||||
Africa | 72.9 | 73.6 | 73.2 | 72.2 | 72.0 | 73.0 | 73.2 |
National income level | |||||||
Low income | 74.3 | 74.4 | 74.8 | 74.0 | 73.4 | 73.9 | 74.0 |
Lower-middle income | 72.8 | 74.0 | 73.1 | 72.1 | 72.1 | 73.3 | 73.5 |
Upper-middle- or high-income | 66.7 | 66.3 | 66.2 | 64.1 | 63.0 | 65.2 | 65.6 |
Sub-region | |||||||
Northern | 72.4 | 71.8 | 71.3 | 72.0 | 71.2 | 71.9 | 71.9 |
Central | 70.1 | 71.9 | 70.4 | 70.3 | 70.9 | 69.3 | 70.3 |
Southern | 67.3 | 67.0 | 66.9 | 64.7 | 63.7 | 65.6 | 66.1 |
Eastern | 75.3 | 76.3 | 76.7 | 75.0 | 74.0 | 74.3 | 74.1 |
Western | 72.7 | 74.1 | 73.1 | 71.5 | 72.3 | 74.5 | 74.9 |
Protein contribution (%) | |||||||
Africa | 8.9 | 8.6 | 8.8 | 9.0 | 9.2 | 9.1 | 8.9 |
National income level | |||||||
Low income | 8.9 | 8.7 | 8.8 | 9.0 | 9.2 | 9.0 | 9.1 |
Lower-middle-income | 8.8 | 8.4 | 8.7 | 8.9 | 9.1 | 9.0 | 8.8 |
Upper-middle- or high-income | 9.8 | 9.6 | 9.6 | 9.8 | 9.8 | 9.5 | 9.3 |
Sub-region | |||||||
Northern | 9.5 | 9.7 | 9.9 | 10.2 | 10.4 | 10.2 | 10.1 |
Central | 9.2 | 8.3 | 8.9 | 8.9 | 9.0 | 10.2 | 9.7 |
Southern | 9.7 | 9.5 | 9.5 | 9.7 | 9.6 | 9.4 | 9.2 |
Eastern | 8.6 | 8.4 | 8.3 | 8.5 | 8.7 | 8.8 | 8.8 |
Western | 8.4 | 7.9 | 8.3 | 8.6 | 8.7 | 8.3 | 8.1 |
Total fat contribution (%) | |||||||
Africa | 17.9 | 17.6 | 17.8 | 18.5 | 18.6 | 17.6 | 17.5 |
National income level | |||||||
Low-income | 16.7 | 16.7 | 16.3 | 16.9 | 17.3 | 16.9 | 16.7 |
Lower-middle-income | 18.2 | 17.4 | 18.0 | 18.8 | 18.6 | 17.3 | 17.4 |
Upper-middle- or high-income | 21.3 | 22.4 | 22.5 | 24.5 | 25.7 | 23.7 | 23.5 |
Sub-region | |||||||
Northern | 18.0 | 18.5 | 18.8 | 17.8 | 18.4 | 17.9 | 18.0 |
Central | 20.3 | 19.4 | 20.2 | 20.3 | 19.2 | 19.6 | 19.2 |
Southern | 21.0 | 21.9 | 22.1 | 24.1 | 25.2 | 23.4 | 23.2 |
Eastern | 15.9 | 15.2 | 14.9 | 16.2 | 17.1 | 16.5 | 16.7 |
Western | 18.7 | 17.9 | 18.5 | 19.8 | 18.8 | 16.9 | 16.7 |
Country-specific figures indicated, in 2017 contribution of carbohydrates exceeded the upper limit of 75%, in seven countries including Madagascar (83.8%), Rwanda (78.9%), Ghana (78.9%), Mozambique (77.4%) and Nigeria (76.8%) (
At regional-level, the calorie contribution of protein was around 9% and the figure remained below the minimum target of 10% in almost all sub-regions and income levels, with the exception of the Northern Africa sub-region (10.1%).
At regional-level, the calorie contribution of fat also remained constant around 10%. However, in 2017, the figure exceeded the limit of 20% in upper-middle- or high-income countries and in Southern Africa sub-region. Country-level estimates indicated, 21 countries including Sao Tome and Principe (35.3%), Gambia (28.0%), Mauritius (26.5%) and Tunisia (25.7%) exceeded the limit of 20% (
Contribution to total calorie (%) | Year | ||||||
---|---|---|---|---|---|---|---|
1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2017 | |
Saturated fatty acids contribution (%) | |||||||
Africa | 5.4 | 5.2 | 5.1 | 5.7 | 5.7 | 5.1 | 5.1 |
National income level | |||||||
Low income | 4.9 | 4.9 | 4.6 | 5.0 | 5.2 | 4.8 | 4.8 |
Lower-middle income | 5.6 | 5.3 | 5.4 | 6.0 | 5.9 | 5.2 | 5.3 |
Upper-middle or high income | 5.5 | 5.4 | 5.5 | 6.1 | 6.3 | 5.8 | 5.6 |
Sub-region | |||||||
Northern | 4.9 | 5.0 | 5.2 | 5.2 | 5.2 | 4.6 | 4.6 |
Central | 6.1 | 5.6 | 5.6 | 5.6 | 5.3 | 5.2 | 5.1 |
Southern | 5.4 | 5.3 | 5.4 | 6.0 | 6.2 | 5.7 | 5.5 |
Eastern | 4.8 | 4.5 | 4.3 | 4.9 | 5.4 | 4.8 | 4.9 |
Western | 6.2 | 6.0 | 5.8 | 6.6 | 6.2 | 5.6 | 5.5 |
Monounsaturated fatty acids contribution (%) | |||||||
Africa | 5.6 | 5.5 | 5.6 | 5.8 | 5.9 | 5.7 | 5.7 |
National income level | |||||||
Low-income | 5.4 | 5.4 | 5.3 | 5.4 | 5.5 | 5.6 | 5.5 |
Lower-middle-income | 5.7 | 5.4 | 5.7 | 5.9 | 5.9 | 5.6 | 5.6 |
Upper-middle- or high-income | 6.3 | 6.5 | 6.7 | 7.3 | 7.8 | 7.4 | 7.4 |
Sub-region | |||||||
Northern | 5.5 | 5.7 | 5.9 | 5.3 | 5.6 | 5.7 | 5.8 |
Central | 7.0 | 6.8 | 7.2 | 7.3 | 6.9 | 7.2 | 6.9 |
Southern | 6.2 | 6.3 | 6.5 | 7.1 | 7.6 | 7.3 | 7.2 |
Eastern | 4.9 | 4.5 | 4.5 | 4.9 | 5.2 | 5.1 | 5.1 |
Western | 6.2 | 5.9 | 6.2 | 6.4 | 6.2 | 5.6 | 5.6 |
Polyunsaturated fatty acids contribution (%) | |||||||
Africa | 5.3 | 5.3 | 5.4 | 5.5 | 5.4 | 5.2 | 5.3 |
National income level | |||||||
Low income | 4.8 | 4.9 | 4.8 | 4.9 | 4.9 | 4.9 | 4.9 |
Lower-middle income | 5.4 | 5.2 | 5.4 | 5.4 | 5.3 | 5.1 | 5.1 |
Upper-middle or high income | 7.6 | 8.6 | 8.4 | 9.2 | 9.6 | 8.7 | 8.6 |
Sub-region | |||||||
Northern | 6.3 | 6.4 | 6.2 | 5.7 | 6.0 | 6.1 | 6.2 |
Central | 5.5 | 5.4 | 5.8 | 5.7 | 5.4 | 5.4 | 5.4 |
Southern | 7.4 | 8.4 | 8.3 | 9.1 | 9.5 | 8.6 | 8.6 |
Eastern | 4.7 | 4.6 | 4.6 | 4.9 | 4.9 | 5.1 | 5.1 |
Western | 4.7 | 4.5 | 4.9 | 5.1 | 4.8 | 4.3 | 4.2 |
Polyunsaturated to saturated fatty acid ratio | |||||||
Africa | 0.98 | 1.02 | 1.06 | 0.96 | 0.95 | 1.02 | 1.04 |
National income level | |||||||
Low-income | 0.98 | 1.00 | 1.04 | 0.98 | 0.94 | 1.02 | 1.02 |
Lower-middle-income | 0.96 | 0.98 | 1.00 | 0.90 | 0.90 | 0.98 | 0.96 |
Upper-middle- or high-income | 1.38 | 1.59 | 1.53 | 1.51 | 1.52 | 1.50 | 1.54 |
Sub-region | |||||||
Northern | 1.29 | 1.28 | 1.19 | 1.10 | 1.15 | 1.33 | 1.35 |
Central | 0.90 | 0.96 | 1.04 | 1.02 | 1.02 | 1.04 | 1.06 |
Southern | 1.37 | 1.58 | 1.54 | 1.52 | 1.53 | 1.51 | 1.56 |
Eastern | 0.98 | 1.02 | 1.07 | 1.00 | 0.91 | 1.06 | 1.04 |
Western | 0.76 | 0.75 | 0.84 | 0.77 | 0.77 | 0.77 | 0.76 |
In 2017, the energy from SFAs remained within the acceptable range (<10%) in all countries except in Sao Tome and Principe (26.0%). The supply of PUFAs, on the other hand was sub-optimal (< 6%) in 28 African countries including Madagascar (2.0%), Liberia (3.0%), Sierra Leone (3.0%), Ghana (3.0%) and Rwanda (3.1%) (
The balance between SFA and PUFA in a diet can also be measured using PUFA to SFA ratio. Over the period, the ratio remained around 1:1 in Africa. In 2017, the ratio was relatively higher in upper-middle- or high-income countries (1.54:1) and in Southern (1.56:2) and Northern (1.56:1) sub-regions suggesting the dominance of PUFA. Conversely, the ratio was low (0.76:1) in the Western sub-region indicating the opposite.
Over the period, the energy contribution of free sugars (%) in Africa remained constant around 7%. Throughout the years the figure exceeded the upper limit of 10% in upper-middle- or high-income countries and in Southern and Northern sub-regions. At country-level, thirteen countries including Botswana (18.8%), Namibia (16.4%), Eswatini (15.2%) and South Africa (14.9%) exceed the limit (
Contribution of free sugars to the total calorie (%) | Year | ||||||
---|---|---|---|---|---|---|---|
1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2017 | |
Africa | 6.9 | 6.8 | 7.0 | 7.0 | 7.1 | 7.0 | 7.1 |
National income level | |||||||
Low-income | 5.2 | 5.2 | 5.2 | 5.9 | 5.8 | 6.0 | 6.0 |
Lower-middle-income | 7.2 | 7.1 | 7.5 | 7.4 | 7.5 | 7.0 | 6.9 |
Upper-middle- or high-income | 12.8 | 11.6 | 10.9 | 10.2 | 11.2 | 14.0 | 14.7 |
Sub-region | |||||||
Northern | 10.2 | 9.5 | 10.3 | 10.6 | 10.8 | 10.5 | 10.5 |
Central | 6.1 | 5.3 | 5.3 | 5.9 | 5.8 | 5.8 | 6.4 |
Southern | 12.7 | 11.6 | 10.9 | 10.2 | 11.3 | 14.1 | 14.8 |
Eastern | 5.6 | 5.9 | 6.3 | 6.1 | 6.2 | 6.2 | 6.2 |
Western | 4.4 | 4.9 | 4.7 | 5.1 | 5.1 | 4.7 | 4.6 |
At regional-level, the total dietary cholesterol supply increased by 14% from 92.8 in 1990 to 105.7 mg/capita/day in 2017. The increase was also observed almost in all national income levels and sub-regions. Despite the progressive rise, cholesterol supply remained within the tolerable range set by WHO (<300 mg/day). Country-level estimates are given in a
Dietary cholesterol supply (mg/capita/day) | Year | % change | ||||||
---|---|---|---|---|---|---|---|---|
1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2017 | ||
Africa | 92.8 | 88.5 | 93.1 | 101.4 | 111.7 | 110.5 | 105.7 | 14.0 |
National income level | ||||||||
Low income | 58.8 | 55.0 | 57.4 | 63.0 | 68.3 | 65.5 | 65.0 | 10.7 |
Lower-middle income | 99.1 | 93.9 | 100.7 | 110.1 | 122.0 | 120.3 | 114.4 | 15.4 |
Upper-middle or high income | 196.0 | 194.1 | 200.3 | 223.7 | 258.0 | 274.4 | 260.2 | 32.8 |
Sub-region | ||||||||
Northern | 119.9 | 123.9 | 141.1 | 153.1 | 173.6 | 183.0 | 177.5 | 48.1 |
Central | 91.1 | 74.8 | 83.6 | 87.8 | 101.8 | 127.8 | 116.6 | 28.0 |
Southern | 187.6 | 184.4 | 189.8 | 213.8 | 246.1 | 263.7 | 250.5 | 33.5 |
Eastern | 60.5 | 51.2 | 49.3 | 54.1 | 59.4 | 57.8 | 56.6 | -6.4 |
Western | 82.2 | 78.8 | 82.5 | 92.6 | 99.5 | 87.1 | 83.3 | 1.4 |
Over the period (1990–2017) the availability of fruits and vegetables per person in Africa rose by 27.5% from 279 to 356 g/capita/day. The trends in the supply of fruits and vegetables in different sub-regions and economic levels are shown in Figs
The purpose of the current study was to evaluate the African food supply against population nutrient intake goals defined for preventing diet-related NCDs, and provide regional, sub-regional and country-level estimates.
The study indicated that between 1990 and 2017 the supply of all energy-yielding nutrients has considerably increased in the region, including in all national income levels. In low-income counties the actual supply remains lower but the rates of increase were higher than countries in higher income levels. The higher rates of increase can be explained by low baseline rates and recent economic growth being observed in many low-income African countries.
According to the recommendation of WHO, the fraction of energy derived from fat should not exceed 35% of the total intake and threshold of 20% is more compatible with good health [
From the perspective of preventing both under- and over-nutrition, protein should contribute 10–15% of the daily energy intake [
Diets should contain an optimal amount of PUFAs, 6–10% of the total energy. PUFAs reduce plasma LDL and lessen the risk of cardiovascular diseases [
Generally, it is assumed that the desirable ratio of PUFA to SFA in the diet is 1:1 [
High dietary sugar intake causes positive energy balance and ultimately leads to NCDs through encouraging unhealthy weight gain [
We estimated the energy contribution of free sugars by assuming that 35% of fruits were consumed as juice in all country-years. The assumption was made based on the finding of a national survey conducted in USA [
Even though the effect of dietary cholesterol on serum cholesterol has long been debated [
Adequate intake of fruits and vegetables contributes to the reduction of energy density, promotes the consumption of dietary fibre, and reduces the risk of NCDs including obesity, cardiovascular diseases and possibly gastrointestinal cancers [
This study evaluated the African food supply against multiple population intake goals set for preventing NCDs. However, the following limitations should be taken into consideration while interpreting the findings. The overall analysis is made by considering food supply as a proxy indicator of food consumption; however, this assumption is not strictly true and might have caused overestimation of the intake of the nutrients. While FBS is a useful tool for international comparison and analysis over time, it does not take within country variation including geographic, seasonal and interpersonal differences, into account [
In the current study, trends in dietary supply over the reference period were constructed by merging the old (1990–2012) [
On top of the inherent limitations of FBS, we did not evaluate the African food supply against nutrient intake goals set for ω-3 and ω -6 fatty acids because a comprehensive food composition database is not available for these nutrients. Further, intake goals set for TFAs and fibres had not been evaluated because naturally occurring trans fats are less relevant to NCDs than their artificial counterparts [
Between 1990 and 2017, the per capita supply of calories substantially increased in Africa, including all sub-regions and economic categories. Most population intake goals set for preventing NCDs remained within acceptable range suggesting that many African countries are in the early stages of the nutrition transition. However, the supplies of fruits and vegetables and PUFAs are low and the increasing energy contributions of free sugars and fats are emerging concerns in specific sub-regions and countries of Africa.
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The author acknowledges the Food and Agriculture Organization of the UN for making the food balance sheets data publicly available without restrictions.
PONE-D-20-31495
Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis
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3. In the data analysis section, please clearly describe how the increases in nutrient supply are assessed against the regional recommendations. Currently, the statistical analysis only show percentage change which does not completely answer the primary research objective.
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Reviewer #1: Yes
Reviewer #2: Yes
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Reviewer #1: Yes
Reviewer #2: Yes
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The
Reviewer #1: Yes
Reviewer #2: Yes
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Reviewer #1: Yes
Reviewer #2: Yes
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5. Review Comments to the Author
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Reviewer #1: Thank you for the opportunity to review this interesting paper. The authors have provided trends of energy and nutrient supply data relevant for NCDS in Africa. I think these findings could be useful to inform African countries and sub-regions as they transition to different income levels and potential diet shifts that may imply.
My comments are to help the authors improve upon the reporting and strength of the paper and to assist the editor in making a decision.
Two major comments: 1) Important trends not mentioned and discussed. From Tables 1, we see that the percent change in supply of all nutrients have been higher for low-income countries which decreases with increase in income levels except for fats where higher changes were seen in the two extremes of income classifications (low-income (28%) and upper-middle income (25.2%) categories). Similar country-income relevant trends are seen for the proportion of energy for specific nutrients. These specific trends are important for different income categories as they make various transitions. These need to be described in results and their implications discussed. Could authors speculate what may be driving the changes in food supply? Food trade or own production and what are the implications for sustainable diets?
2) The FAO adopted a new methodology for compiling the Food Balance Sheet data from 2014-2017 (
Minor comments:
1) Using FAO data (1961-2013), a recent study by Bentham et al, 2020 (
2) Table 1: Please check the total calorie supply for whole of Africa and the sub-region, Northern. These are exactly the same numbers.
3) Some minor typos that need checks. It would have been helpful to have line numbers to make reference very specific. Page 7, ‘…the amount of all all’… delete one “all”. Page 18, …’vegetables remained below below… delete one “below”. Abstract; conclusion: should be revised to read as “In Africa....”. If space allow, it will be useful to state that these are country level data and not individual consumption data or other form that notifies the reader.
4) References: Some revisions needed to ensure consistency especially where organizations are cited. #5, 8,11, 16, 19 need to include URL and access date to be consistent with 1, 13, 14, 15, 23, 24.
Thank you.
Reviewer #2: I find the rationale for conducting this study compelling and its results have a wide range of potential uses, including advocacy for governments of African nations to consider what policies are needed to encourage increased production and availability of foods that make healthy diets more available, accessible, affordable and desirable to their populations. I appreciate the occasional mention by the authors of the double burden of malnutrition and encourage them to reference the recent Lancet series on this topic. Both under-nutrition and over-nutrition contribute to the growing burden of diet-related NCDs in this continent (reference: Wells et al. 2020, The double burden of malnutrition: aetiological pathways and consequences for health,
Methodology – Overall the authors appear to have conducted this study with careful consideration of the strengths and limitations of food balance sheet data from FAO. However, the decision by the authors to use an American population survey to estimate the proportion of fruits that are consumed as juice and apply this across all African countries and all 27 years is very difficult to justify, in my opinion. I recommend that the authors provide more data specific to Africa to show that this assumption is reasonable. The literature shows large changes over time in consumption of fruit juices globally, with an increase in countries like South Africa but still relatively low per capita consumption compared to the USA (for example, Fava Neves 2020
Discussion - The authors of this study do well in placing their findings in the context of other comparable studies, both for the Africa region as well as other regions. However, I recommend that they take these findings one step further in the discussion section and consider what are the contributing factors to the food supply issues observed for Africa region. What are the likely explanations for the results observed? The higher supply of fruits and vegetables in North Africa is remarkable and it would be good to describe some of the key reasons for why this sub-region has succeeded in increasing its supply.
In another example, supply constraints for protein-rich animal source foods contribute to the high costs of these foods, making them unaffordable for a large proportion of the population. What are some of the supply chain issues affecting these foods specifically? For example, fresh cow’s milk and eggs are highly perishable and low productivity in the dairy and poultry sectors of low-income countries contributes to the high prices of these foods. (Headey & Alderman 2019
Including in the results or discussion section an analysis of what are the key food groups that are implicated in some of the sub-regional differences would further add to our understanding of what has contributed to these trends. For example, what foods are available in higher amounts in West African countries that contribute to the higher SFA amount compared to PUFA? I believe this is the higher production and availability of palm oil, but it would be helpful to the reader to confirm this, if possible.
I think it would be helpful to mention the fact that these are national estimates and do not adequately describe the large subnational variation in supply and consumption of these foods and nutrients. I recommend including a reference to studies from African populations that show the contrast in fat intake between wealthy and poor households or between adults in urban vs. rural areas. The article by Steyn & Mchiza (2014,
Finally, given the current global context, I recommend that the authors also consider adding a couple of statements on the expected impact of the COVID-19 pandemic on food supplies in the Africa region.
Please see my detailed comments and suggested language edits in the attached file.
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Reviewer #2: No
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Submitted filename:
Point by point response to the comments of the reviewers
Reviewer #1:
Comment 1: Important trends not mentioned and discussed. From Tables 1, we see that the percent change in supply of all nutrients have been higher for low-income countries which decreases with increase in income levels except for fats where higher changes were seen in the two extremes of income classifications (low-income (28%) and upper-middle income (25.2%) categories). Similar country-income relevant trends are seen for the proportion of energy for specific nutrients. These specific trends are important for different income categories as they make various transitions. These need to be described in results and their implications discussed. Could authors speculate what may be driving the changes in food supply? Food trade or own production and what are the implications for sustainable diets?
Response: The issue is now addressed in the Results (Page 10, past paragraph) and Discussion (Page 20, second paragraph) sections.
Comment 2: The FAO adopted a new methodology for compiling the Food Balance Sheet data from 2014-2017 (
Response: The limitation is now discussed in the Discussion section (Page 23, second paragraph)
Comment 3: Using FAO data (1961-2013), a recent study by Bentham et al, 2020 (
Response: thank you for recommending this key reference. Now it is cited in the introduction section (Page 5, third paragraph).
Comment 4: Table 1: Please check the total calorie supply for whole of Africa and the sub-region, Northern. These are exactly the same numbers.
Response: We really sorry for this silly error. We have now corrected the values for the North Africa region (Table 1).
Comment 5: Some minor typos that need checks. It would have been helpful to have line numbers to make reference very specific. Page 7, ‘…the amount of all all’… delete one “all”. Page 18, …’vegetables remained below below… delete one “below”. Abstract; conclusion: should be revised to read as “In Africa....”. If space allow, it will be useful to state that these are country level data and not individual consumption data or other form that notifies the reader.
Response: Thank you very much. All the typos are now corrected.
Comment 6: References: Some revisions needed to ensure consistency especially where organizations are cited. #5, 8,11, 16, 19 need to include URL and access date to be consistent with 1, 13, 14, 15, 23, 24.
Response: The first group of references are books published by organizations while the second group are online resources. That’s why the approach of citation was different for the two groups.
Reviewer #2:
Comment 7: I appreciate the occasional mention by the authors of the double burden of malnutrition and encourage them to reference the recent Lancet series on this topic. Both under-nutrition and over-nutrition contribute to the growing burden of diet-related NCDs in this continent (reference: Wells et al. 2020, The double burden of malnutrition: aetiological pathways and consequences for health,
Response: Thank you very much. The issue of double burden of diseases and the double-duty actions are now described in the introduction section (Page 5, third paragraph) and the two recommended articles are cited.
Comment 8: Methodology – Overall the authors appear to have conducted this study with careful consideration of the strengths and limitations of food balance sheet data from FAO. However, the decision by the authors to use an American population survey to estimate the proportion of fruits that are consumed as juice and apply this across all African countries and all 27 years is very difficult to justify, in my opinion. I recommend that the authors provide more data specific to Africa to show that this assumption is reasonable. The literature shows large changes over time in consumption of fruit juices globally, with an increase in countries like South Africa but still relatively low per capita consumption compared to the USA (for example, Fava Neves 2020
Response: We agree that the use of American population survey to estimate the proportion of fruits that are consumed as juice and apply this across all African countries is difficult to justify. However, we had no other study from Africa or other comparable settings to estimate this key parameter. The only thing we could do is to remove the entire analysis on intake of free sugar from the manuscript or to keep it as it is and discuss the possible limitations of using the external US data. We opted for the latter because, even using the US data, comparison between countries, sub-regions, and income levels can would be somehow possible. However, we have already discussed (Page 21, last paragraph) the possible implication of using the US data for estimating the proportion of fruits that are consumed as juice.
Comment 9: Discussion - The authors of this study do well in placing their findings in the context of other comparable studies, both for the Africa region as well as other regions. However, I recommend that they take these findings one step further in the discussion section and consider what are the contributing factors to the food supply issues observed for Africa region. What are the likely explanations for the results observed? The higher supply of fruits and vegetables in North Africa is remarkable and it would be good to describe some of the key reasons for why this sub-region has succeeded in increasing its supply.
Response: We have now provided further discussion to explain issues including high supply of fruits and vegetables in North Africa, low level of PUFA consumption in West Africa and high level of PUFA supply in North Africa.
Comment 10: In another example, supply constraints for protein-rich animal source foods contribute to the high costs of these foods, making them unaffordable for a large proportion of the population. What are some of the supply chain issues affecting these foods specifically? For example, fresh cow’s milk and eggs are highly perishable and low productivity in the dairy and poultry sectors of low-income countries contributes to the high prices of these foods. (Headey & Alderman 2019
Response: The issue is now discussed in the first paragraph of page 20, and the paper by Headey & Alderman 2019 is now cited.
Comment 11: Including in the results or discussion section an analysis of what are the key food groups that are implicated in some of the sub-regional differences would further add to our understanding of what has contributed to these trends. For example, what foods are available in higher amounts in West African countries that contribute to the higher SFA amount compared to PUFA? I believe this is the higher production and availability of palm oil, but it would be helpful to the reader to confirm this, if possible.
Response: This is likely due to relatively higher production of coconut oil in western African countries including Côte D'ivoire, Nigeria and Gahanna. The same is now mentioned in the discussion section (Page 21, first paragraph).
Comment 12: I think it would be helpful to mention the fact that these are national estimates and do not adequately describe the large subnational variation in supply and consumption of these foods and nutrients. I recommend including a reference to studies from African populations that show the contrast in fat intake between wealthy and poor households or between adults in urban vs. rural areas. The article by Steyn & Mchiza (2014,
Response: This issue is now stated in the Discussion section (second paragraph, page 23)
Comment 13: Finally, given the current global context, I recommend that the authors also consider adding a couple of statements on the expected impact of the COVID-19 pandemic on food supplies in the Africa region.
Response: We fear discussing the impact of the COVID-19 pandemic on food supplies in Africa could take the paper out of context for two reasons: (1) The reference period that the study is focused (1990-2017) does not embrace the COVID-19 pandemic period. (2) limited scientific evidence is available to argue that the pandemic is negatively affecting the food supply in the continent.
Comment 14: Please see my detailed comments and suggested language edits in the attached file.
Response Thank you very much for the inputs and commitment. We have accommodated all the language suggestions.
Submitted filename:
PONE-D-20-31495R1
Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis
PLOS ONE
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Additional Editor Comments (if provided):
Thank you for responding to the reviewer comments very thoroughly. I noted 3 small typos to fix, and following resubmission of the manuscript with these small edits, the manuscript can be accepted for publication. The edits are as follows:
p21, line 2: I believe "lower" is correct, not "low"
p23, first sentence of the second full paragraph on the page: "trends in dietary supply over the reference period were constructed by merging...." not "was constructed"
p24: lines 1 and 2: I believe "fibre" is correct not "fibres"
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We have now corrected all the typos that the editor reported. Thank you.
Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis
PONE-D-20-31495R2
Dear Dr. Gebremedhin,
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PONE-D-20-31495R2
Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis
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