Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis

Samson Gebremedhin; Tilahun Bekele

doi:10.1371/journal.pone.0245241

. 2021 Jan 11;16(1):e0245241. doi: 10.1371/journal.pone.0245241

Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis

Samson Gebremedhin ^1,^*, Tilahun Bekele ²

Editor: Susan Horton³

PMCID: PMC7799762 PMID: 33428662

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Background

Every year 41 million deaths, equivalent to 71% of the total deaths, occur globally due to non-communicable diseases (NCDs) [1]. Between 1990 and 2017, the number of avertable NCD-related deaths increased by nearly 50% [2]. Specifically, cardiovascular diseases, cancers, chronic respiratory diseases and diabetes contribute to more than 80% of the total NCD-related deaths [1]. With the existing trends, the Sustainable Development Goals (SDGs) target to reduce pre-mature mortality from NCDs by one-third is unlikely to be realized [3].

NCDs once considered as diseases of affluence are now disproportionately affecting low- and middle-income countries (LMIC) [4]. The risk of death from NCDs is now higher in LMIC than high-income countries and more than 80% of the global NCD-related deaths now occur in LMIC [2, 4]. According to the recent estimate of the World Health Organization (WHO), in Africa 41% of the total deaths are from NCDs and probability of premature death from the four major NCDs is 21% [6]. Furthermore, NCDs explain more than half of the total mortality in 11 of the 54 African nations [5]. In sub-Saharan Africa, between 1990 and 2017, disability adjusted life years attributable to NCDs has increased by 67% [6].

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 [1, 7]. A large body of evidence confirms that the rise in unhealthy diets is among the major drivers of the global NCD pandemic [7–10]. In 2017, 11 million adulthood deaths were attributable to dietary factors [7]. Inadequate intake of fruits, vegetables and dietary fibre and high intake of salt, sugar, alcohol and fats lead to NCDs [7–10]. Even within the domain of fatty acids, individual types have distinct physiological properties: high intakes of saturated (SFA) and trans (TFAs) fatty acids are strongly linked with dyslipidaemia while optimal intakes of polyunsaturated (PUFAs) and monounsaturated (MUFA) fatty acids have beneficial effects [8, 11, 12].

In 2003 WHO and Food and Agriculture Organization of the United Nations (FAO) proposed population intake goals for preventing NCDs [8]. According to the recommendation, optimal macronutrient intake ranges based on contribution to daily energy intake are 55–75% for carbohydrate, 15–30% for fat and 7–20% for protein. Calories from SFAs and TFAs have to be below 10 and 1% of the total intake, respectively and that of PUFAs should be within 6 to 10%. Further, energy derived from free sugars should not exceed 10%. Dietary cholesterol intake should be limited (< 300 mg/day) and adequate (> 400 g/day) intake of fruits and vegetables is recommended. Specific goals have been set for sodium chloride, total dietary fibre and ω-3 and ω-6 fatty acids as well [8].

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 [13]. Furthermore, the ongoing rapid nutrition transition in LMIC has increased the double burden of malnutrition [14]. Accordingly, double-duty actions including optimizing intake of nutrients are required for combating multiple forms of malnutrition [15].

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.

Methods

Study design

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 [16, 17]. Data for the recent four years (2017–2020) had not been assembled and made available for public use and were not included in the analysis. Likewise, the data for Burundi, Comoros, Democratic Republic of Congo, Equatorial Guinea, Eritrea, Libyan, Seychelles, Somalia and Southern Sudan are not publicly available; accordingly, these countries are not represented in the study.

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 [18]. The per capita supply of fruits and vegetables was also computed.

Data source

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 [19]. Food Balance Sheet is an important tool for appraising food situations at various levels (global, regional or national) and informing national food and agriculture policies.

Estimation of nutrient composition

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 [18]. The USDA database was preferred than other food composition databases (e.g. Infoods database of the FAO) because it provide comprehensive information on several food groups for all nutrients represented in the study. However, at times when a food item is missing from the database, the FAO food composition table for international use was used [19]. When a commodity aggregates two different foods (e.g. mutton and goat meat, oranges and mandarins, lemons and limes), the nutrient composition was estimated as the average of the two assuming equal weights. Similarly, when a group aggregates multiple food types (peas, beans, fresh water fish….) the specific food types within that domain were identified from the FAO FBS handbook [19], and the mean nutrient composition was determined assuming equal weights. Nutrient compositions for “others” (other cereals, other pulses, other oil crops, other meats etc…) were estimated by taking the arithmetic mean of the related commodities listed in the FBS.

The FBS provides all data on meat in terms of carcass weight that includes non-edible bones [19]. Carcass weight was converted into edible weight using the conversion factors recommended by USDA [20]. The conversion factor ranged from 70% for bovine meat to 100% for fish. For eggs conversion factor of 98.5 was used [20].

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 [21]. Calories from various types of fatty acids (SFA, MUFA, PUFA) were also computed.

Estimation of energy contribution of free sugars

WHO defines “free sugars” as “all monosaccharides and disaccharides added to foods by the manufacturer, cook or consumer, plus the sugars that are naturally present in honey, syrups and fruit juices” [8, 22]. In the current study, calories from free sugars were estimated by totalling the energy contents of honey and industrially produced sugars and sweeteners, with calories from monosaccharides and disaccharides contained in fruit juices. To the best of our knowledge, no information is available regarding what proportion of fruits are consumed as juices in Africa. Accordingly, based on the National Health and Nutrition Examination Survey (NHANES) of the US, we assumed that 35% of the total fruits were consumed as juice in all country-year [23].

Supply of fruits and vegetables

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 [8] and other similar studies [24, 25], starchy roots and tubers including cassava, yams, potato and sweet potato were not considered as vegetables.

Data management and analysis

The food supply statistics for each country-year was downloaded from the old (1990–2012) [16] and new (2013–2017) [17] FAOSTAT databases as csv files and exported to SPSS v24 for analysis. On top of regional (continental) and country-level figures, sub-regional estimates were computed by grouping countries based on geographic location and national income levels. The UN sub-region classification was used to classify countries into Northern, Eastern, Southern, Western and Central sub-regions [26]. Furthermore, the World Bank’s Gross National Income (GNI) classification for the year 2020 was employed for stratifying countries as low-, lower-middle-, upper middle- or high-income economies [27]. The sub-regional and economic classification of the countries is provided as a S1 File. Whenever estimates are provided by aggregating multiple countries, population-weighted analysis was used. Percentage change over the study period (1990–2017) was determined by dividing difference between the base- and end-year to the base value in 1990. PUFA to SFA ratio was computed by dividing the energy contribution of PUFA with that of SFA.

Results

Trends in energy supply

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 (Table 1).

Table 1. Trends in the supply of macronutrients in Africa, 1990–2017.

Supply of macronutrients	Year							% change
Supply of macronutrients	1990	1995	2000	2005	2010	2015	2017	% change
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

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Calorie contribution of energy-yielding macronutrients

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%) (Table 2).

Table 2. Contribution (%) of carbohydrate, protein and fat to total energy supply in Africa, 1990–2017.

Contribution to total calorie supply (%)	Year
Contribution to total calorie supply (%)	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

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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%) (S2 File).

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% (S2 File).

Energy contribution of specific types of fatty acids

Table 3 shows the trends in per capita supply of SFA, MUFA and PUFA, expressed as contribution to total energy. In Africa, the calorie contribution of the fatty acids is found to be balanced. In 2017, 5.1% of the total calories came from UFA whereas, MUFA and PUFA contributed for 5.7 and 5.3%, respectively. Over the period (1990–2017) meaningful changes have not been seen at regional or sub-regional levels in terms of the energy derived from the three groups of fatty acids (Table 3).

Table 3. Calorie contribution (%) of different groups of fatty acids to the total energy supply in Africa, 1990–2017.

Contribution to total calorie (%)	Year
Contribution to total calorie (%)	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

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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%) (S2 File).

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.

Energy contribution of free sugars

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 (S3 File) (Table 4).

Table 4. Contribution of free sugars (%) to the total calorie supply in Africa, 1990–2017.

Contribution of free sugars to the total calorie (%)	Year
Contribution of free sugars to the total calorie (%)	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

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Cholesterol supply

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 S4 File (Table 5).

Table 5. Dietary cholesterol supply (mg/capita/day) in Africa, 1990–2017.

Dietary cholesterol supply (mg/capita/day)	Year							% change
Dietary cholesterol supply (mg/capita/day)	1990	1995	2000	2005	2010	2015	2017	% change
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

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Fruits and vegetables supply

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 1 and 2. In terms of economic levels, the supply increased by 37.0 and 13.2% in lower-middle- and low-income countries, respectively, but had fallen by 20.8% in upper-middle- or high-income countries. Regarding geographical sub-regions, the supply substantially increased by 57.7% in Northern Africa. Considerable improvements have been seen in Western (33.3%) and Central (28.0%) sub-regions as well. Conversely, the improvements were modest (5.9%) in Eastern sub-region and even a considerable decline (22.6%) was seen in Southern sub-region. In general, the supply of fruits and vegetables remained below the minimum target of 400 g/capita/day in all regions and economic levels except in Northern Africa and lower-middle-income countries. Country-level estimates are given as a S5 File.

Discussion

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 [8]. This analysis suggested, between 1990 and 2017 the energy contribution of fat in Africa did not meaningfully change and in 2017 fat provided less than 35% of energy at all levels (regional, sub-regional or country). Yet, the figure exceeded 20% in 21 countries. Though the finding is potentially worrying, it is less alarming than what is being observed in the other regions of the world. According to an estimate, in 2013 Africa had the lowest (54.5 g/capita/day) fat supply in the world, and between 1961 and 2013 the supply was only increased by 15 g/capita/day. Conversely, fat supply markedly increased by 52, 48, 30 g/capita/day respectively, in South America, Asia and the Caribbean regions [28].

From the perspective of preventing both under- and over-nutrition, protein should contribute 10–15% of the daily energy intake [8]. However, the current analysis indicated that in 2017 energy derived from protein remained below 10% in all sub-regions except in Northern Africa. The finding indicates that the existing food supply in the region favours protein undernutrition more than overnutrition. A global estimate indicated, between 1961 and 2013 the protein supply per person in Africa was only increased by 31%, which was much lower than advances made in Asia (64%) and the Caribbean (46%) regions [29]. The supply in Africa (69.10 g/capita/day) was also the lowest among all other regions of the world [29]. An estimate based on FAO database also reported that globally the national meat supplies ranges from less than 10 kg/person/year in many low-income African countries, to more than 100 kg/person/year in high-income countries like USA and Australia. Similar patterns have also been seen for other protein rich animal source foods including milk, egg and fish [28]. The availability of protein rich foods is limited in many African countries due to multiple reasons including export of livestock to earn the much-needed foreign currency, low productivity in the dairy and poultry sectors and high price of animal source foods due to supply constraints [30].

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 [8]. Apart from NCDs, ω-3 PUFAs are also required for brain development during early stages of life [31]. In Africa, with the exception of the Southern and Northern sub-regions, the PUFA supply appears to be suboptimal (<6% of energy). According to our data, in Southern and Northern sub-regions, the PUFA supply was higher due to better access to fish and seed oils. Very few studies have so far evaluated the adequacy of PUFA supply at national or international levels [29, 32–34]. According to an estimate, globally only 1.3% of the total energy comes from PUFA, while MUFA and UFA, contribute 6.4% and 6.7%, respectively indicating suboptimal supply of PUFA is a global concern [29]. The global analysis of PUFA intake found low intakes in many sub-Saharan Africa countries suggesting infrequent use of health cooking oils in the region [35].

Generally, it is assumed that the desirable ratio of PUFA to SFA in the diet is 1:1 [36, 37]. In the current study, the ratio was optimal at regional level but it was much lower in the Western sub-region (0.76:1) suggesting the dominance of SFAs. This can be due to relatively higher production of coconut oil in Western African countries including Côte D’ivoire, Nigeria and Gahanna. From NCDs perspective this is worrying because SFAs have strong tendency to increase LDL (“bad cholesterol”) [8]. Analysis of national FBS data of Ethiopia [34] and Trinidad and Tobago [33] also indicated that the PUFA to SFA ratios were lower than one indicating the dominance of SFA in the respective national food supplies.

High dietary sugar intake causes positive energy balance and ultimately leads to NCDs through encouraging unhealthy weight gain [8]. It is recommended that the contribution of free sugars should not exceed 10% of the total energy [8] and further reduction to 5% provides additional benefits [22]. While free sugar intake in Africa remains within the acceptable range, the figure exceeded 10% in many countries, especially those in Southern and Northern sub-regions. This may partly explain the higher prevalence of NCDs in these sub-regions [5]. According to WHO, percentage of deaths from NCDs in Southern and Northern sub-regions is 70%, as compared to 41% in the entire region [5]. A study also indicated, between 1961 and 2007, calories derived from sugars had substantially increased in Northern Africa by 63% [38].

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 [23]. This decision might have made us to over or under-estimate the intake of free sugars because the consumption pattern of whole fruits is likely to vary among different regions and countries. It can also change over time. Furthermore, as the consumption of sugars considerably varies across different stages of lifespan [39], the per capita supply statistics may not adequately capture the inter-individual variation in intake.

Even though the effect of dietary cholesterol on serum cholesterol has long been debated [40–42], the existing WHO’s nutrient intake recommends for restricting intake below 300 mg/day [8]. According to our finding, between 1990 and 2017, the total dietary cholesterol supply in Africa has increased by 14% but it remained below the aforementioned threshold in all sub-regions and economic levels. Very few studies have so far explored the supply of dietary cholesterol at national-level [33, 35, 43]. In Trinidad and Tobago, between 1961 and 2007, the supply increased by 80% to 225 mg/day [33]; whereas the corresponding level in Finland was 440 mg/day [43]. According to a global estimate, the mean dietary cholesterol intake in 2010 was 228 mg/day and lowest intakes were in South Asian and East African nations [35].

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 [8]. The current analysis suggested that, in Africa the fruits and vegetables supply is gradually improving but remains below the minimum target of 400 g/person/day in all sub-regions, except in the North. The better supply of fruits and vegetables observed in the North Africa is likely the reflection of the better economic status of the countries in the sub-region. A previous study estimated that in 2005 the fruit and vegetable availability was around 546 g/person/day globally, and the lowest supply was in sub-Saharan Africa (206 g/person/day) and South Asia (326 g/person/day). Conversely, the Middle East and North Africa had one of the highest supplies (735 g/person/day) [25]. It has also been projected that, by 2050 up to 1.9 billion people in sub-Saharan Africa could live in countries with inadequate availability of fruits and vegetables [25]. A multicounty study that enrolled 10 sub-Saharan Africa countries concluded that in all countries the per capita consumption of fruits and vegetables was below 50 g/day [31]. In addition to NCDs, low intake of fruits and vegetables had been identified as a major driver of micronutrient deficiencies in Africa [44].

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 [19]. According to a review, multiple studies from Africa have already reported large subnational variation in the supply and consumption of foods and nutrients [45]. Furthermore, FBS does not take household-level food wastage and subsistent production of less pertinent food commodities, into consideration and this might have caused underestimation of intake.

In the current study, trends in dietary supply over the reference period were constructed by merging the old (1990–2012) [16] and new (2013–2017) [17] FAOSTAT databases. The two datasets have some methodological differences especially in relation to estimation of unbalanced amounts, source of population data and modelling of food stock and loss data [46]. However, in all of the trend analysis presented in this study, no abrupt interruption was observed between 2012 and 2013, suggesting that the use of the two datasets had no major effects on the timeseries analysis.

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 [47]. Similarly, the intake of fibre is more affected by food processing rather than total food supply. While we compare the outcomes of interest across macroeconomic status of the countries, we used the GNI per capita categories for the year 2020. However, it is important to note that over the study period many countries have moved up in the income brackets and this might have affected the precision the time series comparisons made across GNI per capita categories. Irrespective of these methodological shortcomings, the study has provided comprehensive information on the African food supply from NCDs perspective and permitted for between-country and over time comparisons.

Conclusion

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.

Supporting information

S1 File. Sub-regional and economic classification of the 45 countries included in the study.

(XLSX)

Click here for additional data file.^{(9.9KB, xlsx)}

S2 File. Country-specific estimates: Contribution (%) of carbohydrate, protein and fat to total energy supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(54.5KB, xlsx)}

S3 File. Country-specific estimates: Contribution of free sugars (%) to the total calorie supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(16.4KB, xlsx)}

S4 File. Country-specific estimates: Dietary cholesterol supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(17.9KB, xlsx)}

S5 File. Country-specific estimates: Fruit and vegetable supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(18.9KB, xlsx)}

Acknowledgments

The author acknowledges the Food and Agriculture Organization of the UN for making the food balance sheets data publicly available without restrictions.

Data Availability

Data are freely available from the FAOSTAT database. Please use the following links to access the data: http://www.fao.org/faostat/en/#data/FBSH http://www.fao.org/faostat/en/#data/FBS.

Funding Statement

The authors received no specific funding for this work.

References

1.WHO. Noncommunicable disease: fact sheet. 2017. [Cited 2020, October 05] http://www.who.int/mediacentre/factsheets/fs355/en/.
2.Martinez R, Lloyd-Sherlock P, Soliz P, Ebrahim S, Vega E, Ordunez P, et al. Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: a population-based study. Lancet Glob Health. 2020; 8: e511–23. 10.1016/S2214-109X(20)30035-8 [DOI] [PubMed] [Google Scholar]
3.NCD Countdown 2030 collaborators. NCD Countdown 2030: Pathways to achieving Sustainable Development Goal target 3.4. Lancet. 2020; 396: 918–34. 10.1016/S0140-6736(20)31761-X [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Dain K. Challenges Facing Global Health Networks: The NCD Alliance Experience, comment on "Four Challenges that Global Health Networks Face". Int J Health Policy. Manag. 2018; 7(3): 282–285. 10.15171/ijhpm.2017.93 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.WHO. Noncommunicable diseases: Progress monitor 2020. Geneva: World Health Organization; 2020.
6.Gouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990–2017: Results from the Global Burden of Disease Study 2017. Lancet Glob Health. 2019; 7: e1375–87. 10.1016/S2214-109X(19)30374-2 [DOI] [PubMed] [Google Scholar]
7.GBD Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017. Lancet. 2019; 393: 1958–72. 10.1016/S0140-6736(19)30041-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.WHO. Diet, nutrition and the prevention of chronic diseases. Report of a Joint WHO/FAO Expert Consultation. Geneva: World Health Organization; 2003.
9.Branca F, Lartey A, Oenema S, Aguayo V, Stordalen GA, Richardson R, et al. Transforming the food system to fight non-communicable diseases. BMJ. 2019. 364: l296 10.1136/bmj.l296 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Micha R, Shulkin ML, Peñalvo JL, Khatibzadeh S, Singh GM, Rao M, et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS One. 2017; 12: e0175149 10.1371/journal.pone.0175149 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Food and Agriculture Organization of the United Nations (FAO). Fats and fatty acids in human nutrition: Report of an expert consultation. Rome: FAO; 2010. [PubMed]
12.Smit LA, Mozaffarian D, Willett W. Review of fat and fatty acid requirements and criteria for developing dietary guidelines. Ann Nutr Metab. 2009; 55:44–55. 10.1159/000228995 [DOI] [PubMed] [Google Scholar]
13.Bentham J, Singh GM, Danae G, Green R, Lin JK, Stevens GA et al. Multidimensional characterization of global food supply from 1961 to 2013. Nat Food. 2020; 1: 70–75. 10.1038/s43016-019-0012-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Hawkes C, Ruel MT, Salm L, Sinclair B, Branca F. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet. 2020; 395(10218): 142–155. 10.1016/S0140-6736(19)32506-1 [DOI] [PubMed] [Google Scholar]
15.Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020; 395(10217): 75–88. 10.1016/S0140-6736(19)32472-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.FAO. FAOSTAT: Food Balances (old methodology and population). 2019. [Cited 2020, October 05] http://www.fao.org/faostat/en/#data/FBSH.
17.FAO. FAOSTAT: New Food Balances. 2019. [Cited 2020, October 05] http://www.fao.org/faostat/en/#data/FBS.
18.US Department of Agriculture (USDA). FoodData Central. [Cited 2020, October 05] https://fdc.nal.usda.gov/.
19.FAO. Food balance sheets: A hand book. Rome: FAO; 2001.
20.Putnam JJ, Allshouse JE. Food consumption, prices, and expenditures, 1970–97: Statistical Bulletin No. 965. Washington, DC: Food and Rural Economics Division, Economic Research Service, U.S. Department of Agriculture; 1999.
21.FAO. Food energy—methods of analysis and conversion factors: FAO food and nutrition paper 77. Rome: FAO; 2003.
22.WHO. Information note about intake of sugars recommended in the WHO guideline for adults and children. Geneva: WHO; 2015.
23.Drewnowski A, Rehm CD. Socioeconomic gradient in consumption of whole fruit and 100% fruit juice among US children and adults. Nutr J. 2015; 14:3 10.1186/1475-2891-14-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Herforth A, Arimond M, Álvarez-Sánchez C, Coates J, Christianson K, Muehlhoff E. A global review of food-based dietary guidelines. Adv Nutr. 2019; 10(4): 590–605. 10.1093/advances/nmy130 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Mason-D’Croz D, Bogard JR, Cenacchi N, Dunston S, Herrero M, Wiebe K. Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: an integrated modelling study. Lancet Planet Health. 2019; 3(7): e318–e329. 10.1016/S2542-5196(19)30095-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.United Nations Statistics Division. Standard country or area codes for statistical use. 2020. [Cited 2020, October 05] https://unstats.un.org/unsd/methodology/m49/.
27.World Bank. Country and lending groups: Country classification. 2020. [Cited 2020, October 05] https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.
28.Roser M, Ritchie H. Our world in data: Food Supply. 2013. [Cited 2020, October 05] https://ourworldindata.org/food-supply.
29.Schmidhuber J, Sur JP, Fay K, Huntley B, Salama J, Lee A, et al. The Global Nutrient Database: availability of macronutrients and micronutrients in 195 countries from 1980 to 2013. Lancet Planet Health. 2018; 2: e353–68. 10.1016/S2542-5196(18)30170-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Headey DD, Alderman HH. The relative caloric prices of healthy and unhealthy foods differ systematically across income levels and continents. J Nutr. 2019; 149 (11): 2020–2033. 10.1093/jn/nxz158 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Innis SM. Dietary omega 3 fatty acids and the developing brain. Brain Res. 2008; 1237: 35–43. 10.1016/j.brainres.2008.08.078 [DOI] [PubMed] [Google Scholar]
32.Sanders T. Polyunsaturated fatty acids in the food chain in Europe. Am J Clin Nutr. 2002; 71(Suppl): S176–S178. 10.1093/ajcn/71.1.176s [DOI] [PubMed] [Google Scholar]
33.Sheehy T, Sharma S. Trends in energy and nutrient supply in Trinidad and Tobago from 1961 to 2007 using FAO food balance sheets. Public Health Nutr. 2013;16(9):1693–702. 10.1017/S136898001200537X [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Sheehy T, Carey E, Sharma S, Biadgilign S. Trends in energy and nutrient supply in Ethiopia: a perspective from FAO food balance sheets. Nutr J. 2019; 18:46 10.1186/s12937-019-0471-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Micha R, Khatibzadeh S, Shi P, Fahimi S, Lim S, Andrews KG, et al. Global, regional, and national consumption levels of dietary fats and oils in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys. BMJ. 2014; 348: g2272 10.1136/bmj.g2272 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Hayes KC. Dietary fat and heart health: in search of the ideal fat. Asia Pacific J Clin Nutr. 2002; 11 (Suppl): S394–S400. 10.1046/j.1440-6047.11.s.7.13.x [DOI] [PubMed] [Google Scholar]
37.Grundy SM. What is the desirable ratio of saturated, polyunsaturated, and monounsaturated fatty acids in the diet? Am J Clin Nutr. 1997; 66(suppl): S988–S990. [DOI] [PubMed] [Google Scholar]
38.Golzarand M, Mirmiran P, Jessri M, Toolabi K, Mojarrad M, Azizi F. Dietary trends in the Middle East and North Africa: An ecological study (1961 to 2007). Public Health Nutr. 2012; 15(10): 1835–1844. 10.1017/S1368980011003673 [DOI] [PubMed] [Google Scholar]
39.Newens KJ, Walton J. A review of sugar consumption from nationally representative dietary surveys across the world. J Hum Nutr Diet. 2016. April;29(2):225–40. 10.1111/jhn.12338 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Hopkins PN. Effects of dietary cholesterol on serum cholesterol: a meta-analysis and review. Am J Clin Nutr. 1992; 55:1060–1070. 10.1093/ajcn/55.6.1060 [DOI] [PubMed] [Google Scholar]
41.Soliman GA. Dietary cholesterol and the lack of evidence in cardiovascular disease. Nutrients. 2018; 10(6): 780 10.3390/nu10060780 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Berger S, Raman G, Vishwanathan R, Jacques PF, Johnson EJ. Dietary cholesterol and cardiovascular disease: a systematic review and meta-analysis. Am J Clin Nutr. 2015;102(2):276–94. 10.3945/ajcn.114.100305 [DOI] [PubMed] [Google Scholar]
43.Heinonen M, Lampi AM, Hyvonen L, Homer D. The fatty acid and cholesterol content of the average Finnish diet. J Food Compost Anal.1992; 5(3):198–208. 10.1016/0889-1575(92)90038-l [DOI] [Google Scholar]
44.Ruel MT, Minot N, Smith L. Patterns and comparison of fruits and vegetables consumption in sub-Saharan Africa: a multicounty comparison. Geneva: WHO; 2005.
45.Steyn NP, McHiza ZJ. Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci. 2014; 1311: 88–101. 10.1111/nyas.12433 [DOI] [PubMed] [Google Scholar]
46.FAO. Key differences between new and old Food Balance Sheet (FBS) methodology. [Cited 2020, October 05] http://fenixservices.fao.org/faostat/static/documents/FBS/Key%20differences%20between%20new%20and%20old%20Food%20Balance%20Sheet.pdf.
47.U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans. 7th Edition, Washington, DC: U.S. Government Printing Office; 2010. [DOI] [PMC free article] [PubMed]

PLoS One. doi: 10.1371/journal.pone.0245241.r001

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9 Dec 2020

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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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 (http://www.fao.org/faostat/en/#data/FBS) . What are the implications of using data from the old and new methods in your results? Any associated limitations need to be included in the discussion section.

Minor comments:

1) Using FAO data (1961-2013), a recent study by Bentham et al, 2020 (https://www.nature.com/articles/s43016-019-0012-2) described the global food supply and identified important trends, including trends towards more diversification and reduced sugars of diets in high income countries while low income groups remain relatively unchanged or trends towards poor diet combinations. While a regional specific analysis is still important, the authors may want to discuss or mention how the regional trends differ/compare with the global and what the present trends mean for Africa or different income-level countries within Africa as they make further economic developments.

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, https://doi.org/10.1016/S0140-6736(19)32472-9) and this requires double-duty actions such as the policy changes recommended above. (reference: Hawkes et al. 2020, Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms, https://doi.org/10.1016/S0140-6736(19)32506-1)

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 https://doi.org/10.1007/s40858-020-00378-1). Also, the very high relative cost of fruit juice and sugar-sweetened beverages in African countries, in contrast to North America (cf. Headey & Alderman, 2019, https://doi.org/10.1093/jn/nxz158) is another reason to believe that the authors’ assumption would over-estimate consumption of free sugars in many of these countries.

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 https://doi.org/10.1093/jn/nxz158)

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, https://doi.org/10.1111/nyas.12433) provides several examples of these contrasts at the subnational level.

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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PLoS One. 2021 Jan 11;16(1):e0245241. doi: 10.1371/journal.pone.0245241.r002

Author response to Decision Letter 0

19 Dec 2020

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 (http://www.fao.org/faostat/en/#data/FBS). What are the implications of using data from the old and new methods in your results? Any associated limitations need to be included in the discussion section.

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 (https://www.nature.com/articles/s43016-019-0012-2) described the global food supply and identified important trends, including trends towards more diversification and reduced sugars of diets in high income countries while low income groups remain relatively unchanged or trends towards poor diet combinations. While a regional specific analysis is still important, the authors may want to discuss or mention how the regional trends differ/compare with the global and what the present trends mean for Africa or different income-level countries within Africa as they make further economic developments.

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, https://doi.org/10.1016/S0140-6736(19)32472-9) and this requires double-duty actions such as the policy changes recommended above. (reference: Hawkes et al. 2020, Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms, https://doi.org/10.1016/S0140-6736(19)32506-1)

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 https://doi.org/10.1007/s40858-020-00378-1). Also, the very high relative cost of fruit juice and sugar-sweetened beverages in African countries, in contrast to North America (cf. Headey & Alderman, 2019, https://doi.org/10.1093/jn/nxz158) is another reason to believe that the authors’ assumption would over-estimate consumption of free sugars in many of these countries.

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 https://doi.org/10.1093/jn/nxz158)

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, https://doi.org/10.1111/nyas.12433) provides several examples of these contrasts at the subnational level.

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.

Attachment

Submitted filename: Point by point response PLOS.docx

Click here for additional data file.^{(18KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0245241.r003

Decision Letter 1

Susan Horton

22 Dec 2020

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

Dear Dr. Gebremedhin,

I have noted some minor typos and request you to fix these prior to accepting the manuscript.

Please submit your revised manuscript by Feb 05 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS ONE

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"

[Note: HTML markup is below. Please do not edit.]

PLoS One. 2021 Jan 11;16(1):e0245241. doi: 10.1371/journal.pone.0245241.r004

Author response to Decision Letter 1

22 Dec 2020

We have now corrected all the typos that the editor reported. Thank you.

PLoS One. doi: 10.1371/journal.pone.0245241.r005

Decision Letter 2

Susan Horton

26 Dec 2020

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,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0245241.r006

Acceptance letter

Susan Horton

2 Jan 2021

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

Dear Dr. Gebremedhin:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Sub-regional and economic classification of the 45 countries included in the study.

(XLSX)

Click here for additional data file.^{(9.9KB, xlsx)}

S2 File. Country-specific estimates: Contribution (%) of carbohydrate, protein and fat to total energy supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(54.5KB, xlsx)}

S3 File. Country-specific estimates: Contribution of free sugars (%) to the total calorie supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(16.4KB, xlsx)}

S4 File. Country-specific estimates: Dietary cholesterol supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(17.9KB, xlsx)}

S5 File. Country-specific estimates: Fruit and vegetable supply in Africa, 1990–2017.

(XLSX)

Click here for additional data file.^{(18.9KB, xlsx)}

Attachment

Submitted filename: PONE-D-20-31495_reviewer comments.pdf

Click here for additional data file.^{(644.4KB, pdf)}

Attachment

Submitted filename: Point by point response PLOS.docx

Click here for additional data file.^{(18KB, docx)}

Data Availability Statement

Data are freely available from the FAOSTAT database. Please use the following links to access the data: http://www.fao.org/faostat/en/#data/FBSH http://www.fao.org/faostat/en/#data/FBS.

[pone.0245241.ref001] 1.WHO. Noncommunicable disease: fact sheet. 2017. [Cited 2020, October 05] http://www.who.int/mediacentre/factsheets/fs355/en/.

[pone.0245241.ref002] 2.Martinez R, Lloyd-Sherlock P, Soliz P, Ebrahim S, Vega E, Ordunez P, et al. Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: a population-based study. Lancet Glob Health. 2020; 8: e511–23. 10.1016/S2214-109X(20)30035-8 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref003] 3.NCD Countdown 2030 collaborators. NCD Countdown 2030: Pathways to achieving Sustainable Development Goal target 3.4. Lancet. 2020; 396: 918–34. 10.1016/S0140-6736(20)31761-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref004] 4.Dain K. Challenges Facing Global Health Networks: The NCD Alliance Experience, comment on "Four Challenges that Global Health Networks Face". Int J Health Policy. Manag. 2018; 7(3): 282–285. 10.15171/ijhpm.2017.93 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref005] 5.WHO. Noncommunicable diseases: Progress monitor 2020. Geneva: World Health Organization; 2020.

[pone.0245241.ref006] 6.Gouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990–2017: Results from the Global Burden of Disease Study 2017. Lancet Glob Health. 2019; 7: e1375–87. 10.1016/S2214-109X(19)30374-2 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref007] 7.GBD Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017. Lancet. 2019; 393: 1958–72. 10.1016/S0140-6736(19)30041-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref008] 8.WHO. Diet, nutrition and the prevention of chronic diseases. Report of a Joint WHO/FAO Expert Consultation. Geneva: World Health Organization; 2003.

[pone.0245241.ref009] 9.Branca F, Lartey A, Oenema S, Aguayo V, Stordalen GA, Richardson R, et al. Transforming the food system to fight non-communicable diseases. BMJ. 2019. 364: l296 10.1136/bmj.l296 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref010] 10.Micha R, Shulkin ML, Peñalvo JL, Khatibzadeh S, Singh GM, Rao M, et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS One. 2017; 12: e0175149 10.1371/journal.pone.0175149 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref011] 11.Food and Agriculture Organization of the United Nations (FAO). Fats and fatty acids in human nutrition: Report of an expert consultation. Rome: FAO; 2010. [PubMed]

[pone.0245241.ref012] 12.Smit LA, Mozaffarian D, Willett W. Review of fat and fatty acid requirements and criteria for developing dietary guidelines. Ann Nutr Metab. 2009; 55:44–55. 10.1159/000228995 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref013] 13.Bentham J, Singh GM, Danae G, Green R, Lin JK, Stevens GA et al. Multidimensional characterization of global food supply from 1961 to 2013. Nat Food. 2020; 1: 70–75. 10.1038/s43016-019-0012-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref014] 14.Hawkes C, Ruel MT, Salm L, Sinclair B, Branca F. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet. 2020; 395(10218): 142–155. 10.1016/S0140-6736(19)32506-1 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref015] 15.Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020; 395(10217): 75–88. 10.1016/S0140-6736(19)32472-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref016] 16.FAO. FAOSTAT: Food Balances (old methodology and population). 2019. [Cited 2020, October 05] http://www.fao.org/faostat/en/#data/FBSH.

[pone.0245241.ref017] 17.FAO. FAOSTAT: New Food Balances. 2019. [Cited 2020, October 05] http://www.fao.org/faostat/en/#data/FBS.

[pone.0245241.ref018] 18.US Department of Agriculture (USDA). FoodData Central. [Cited 2020, October 05] https://fdc.nal.usda.gov/.

[pone.0245241.ref019] 19.FAO. Food balance sheets: A hand book. Rome: FAO; 2001.

[pone.0245241.ref020] 20.Putnam JJ, Allshouse JE. Food consumption, prices, and expenditures, 1970–97: Statistical Bulletin No. 965. Washington, DC: Food and Rural Economics Division, Economic Research Service, U.S. Department of Agriculture; 1999.

[pone.0245241.ref021] 21.FAO. Food energy—methods of analysis and conversion factors: FAO food and nutrition paper 77. Rome: FAO; 2003.

[pone.0245241.ref022] 22.WHO. Information note about intake of sugars recommended in the WHO guideline for adults and children. Geneva: WHO; 2015.

[pone.0245241.ref023] 23.Drewnowski A, Rehm CD. Socioeconomic gradient in consumption of whole fruit and 100% fruit juice among US children and adults. Nutr J. 2015; 14:3 10.1186/1475-2891-14-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref024] 24.Herforth A, Arimond M, Álvarez-Sánchez C, Coates J, Christianson K, Muehlhoff E. A global review of food-based dietary guidelines. Adv Nutr. 2019; 10(4): 590–605. 10.1093/advances/nmy130 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref025] 25.Mason-D’Croz D, Bogard JR, Cenacchi N, Dunston S, Herrero M, Wiebe K. Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: an integrated modelling study. Lancet Planet Health. 2019; 3(7): e318–e329. 10.1016/S2542-5196(19)30095-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref026] 26.United Nations Statistics Division. Standard country or area codes for statistical use. 2020. [Cited 2020, October 05] https://unstats.un.org/unsd/methodology/m49/.

[pone.0245241.ref027] 27.World Bank. Country and lending groups: Country classification. 2020. [Cited 2020, October 05] https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

[pone.0245241.ref028] 28.Roser M, Ritchie H. Our world in data: Food Supply. 2013. [Cited 2020, October 05] https://ourworldindata.org/food-supply.

[pone.0245241.ref029] 29.Schmidhuber J, Sur JP, Fay K, Huntley B, Salama J, Lee A, et al. The Global Nutrient Database: availability of macronutrients and micronutrients in 195 countries from 1980 to 2013. Lancet Planet Health. 2018; 2: e353–68. 10.1016/S2542-5196(18)30170-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref030] 30.Headey DD, Alderman HH. The relative caloric prices of healthy and unhealthy foods differ systematically across income levels and continents. J Nutr. 2019; 149 (11): 2020–2033. 10.1093/jn/nxz158 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref031] 31.Innis SM. Dietary omega 3 fatty acids and the developing brain. Brain Res. 2008; 1237: 35–43. 10.1016/j.brainres.2008.08.078 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref032] 32.Sanders T. Polyunsaturated fatty acids in the food chain in Europe. Am J Clin Nutr. 2002; 71(Suppl): S176–S178. 10.1093/ajcn/71.1.176s [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref033] 33.Sheehy T, Sharma S. Trends in energy and nutrient supply in Trinidad and Tobago from 1961 to 2007 using FAO food balance sheets. Public Health Nutr. 2013;16(9):1693–702. 10.1017/S136898001200537X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref034] 34.Sheehy T, Carey E, Sharma S, Biadgilign S. Trends in energy and nutrient supply in Ethiopia: a perspective from FAO food balance sheets. Nutr J. 2019; 18:46 10.1186/s12937-019-0471-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref035] 35.Micha R, Khatibzadeh S, Shi P, Fahimi S, Lim S, Andrews KG, et al. Global, regional, and national consumption levels of dietary fats and oils in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys. BMJ. 2014; 348: g2272 10.1136/bmj.g2272 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref036] 36.Hayes KC. Dietary fat and heart health: in search of the ideal fat. Asia Pacific J Clin Nutr. 2002; 11 (Suppl): S394–S400. 10.1046/j.1440-6047.11.s.7.13.x [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref037] 37.Grundy SM. What is the desirable ratio of saturated, polyunsaturated, and monounsaturated fatty acids in the diet? Am J Clin Nutr. 1997; 66(suppl): S988–S990. [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref038] 38.Golzarand M, Mirmiran P, Jessri M, Toolabi K, Mojarrad M, Azizi F. Dietary trends in the Middle East and North Africa: An ecological study (1961 to 2007). Public Health Nutr. 2012; 15(10): 1835–1844. 10.1017/S1368980011003673 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref039] 39.Newens KJ, Walton J. A review of sugar consumption from nationally representative dietary surveys across the world. J Hum Nutr Diet. 2016. April;29(2):225–40. 10.1111/jhn.12338 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref040] 40.Hopkins PN. Effects of dietary cholesterol on serum cholesterol: a meta-analysis and review. Am J Clin Nutr. 1992; 55:1060–1070. 10.1093/ajcn/55.6.1060 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref041] 41.Soliman GA. Dietary cholesterol and the lack of evidence in cardiovascular disease. Nutrients. 2018; 10(6): 780 10.3390/nu10060780 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245241.ref042] 42.Berger S, Raman G, Vishwanathan R, Jacques PF, Johnson EJ. Dietary cholesterol and cardiovascular disease: a systematic review and meta-analysis. Am J Clin Nutr. 2015;102(2):276–94. 10.3945/ajcn.114.100305 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref043] 43.Heinonen M, Lampi AM, Hyvonen L, Homer D. The fatty acid and cholesterol content of the average Finnish diet. J Food Compost Anal.1992; 5(3):198–208. 10.1016/0889-1575(92)90038-l [DOI] [Google Scholar]

[pone.0245241.ref044] 44.Ruel MT, Minot N, Smith L. Patterns and comparison of fruits and vegetables consumption in sub-Saharan Africa: a multicounty comparison. Geneva: WHO; 2005.

[pone.0245241.ref045] 45.Steyn NP, McHiza ZJ. Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci. 2014; 1311: 88–101. 10.1111/nyas.12433 [DOI] [PubMed] [Google Scholar]

[pone.0245241.ref046] 46.FAO. Key differences between new and old Food Balance Sheet (FBS) methodology. [Cited 2020, October 05] http://fenixservices.fao.org/faostat/static/documents/FBS/Key%20differences%20between%20new%20and%20old%20Food%20Balance%20Sheet.pdf.

[pone.0245241.ref047] 47.U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans. 7th Edition, Washington, DC: U.S. Government Printing Office; 2010. [DOI] [PMC free article] [PubMed]

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Evaluating the African food supply against the nutrient intake goals set for preventing diet-related non-communicable diseases: 1990 to 2017 trend analysis

Samson Gebremedhin

Tilahun Bekele

Roles

Abstract

Background

Methods

Results

Conclusion

Background

Methods

Study design

Data source

Estimation of nutrient composition

Estimation of energy contribution of free sugars

Supply of fruits and vegetables

Data management and analysis

Results

Trends in energy supply

Table 1. Trends in the supply of macronutrients in Africa, 1990–2017.

Calorie contribution of energy-yielding macronutrients

Table 2. Contribution (%) of carbohydrate, protein and fat to total energy supply in Africa, 1990–2017.

Energy contribution of specific types of fatty acids

Table 3. Calorie contribution (%) of different groups of fatty acids to the total energy supply in Africa, 1990–2017.

Energy contribution of free sugars

Table 4. Contribution of free sugars (%) to the total calorie supply in Africa, 1990–2017.

Cholesterol supply

Table 5. Dietary cholesterol supply (mg/capita/day) in Africa, 1990–2017.

Fruits and vegetables supply

Fig 1. Trends in the supply of fruits and vegetables in sub-regions of Africa, 1990–2017.

Fig 2. Trends in the supply of fruits and vegetables across national income levels in Africa, 1990–2017.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Susan Horton

Roles

Author response to Decision Letter 0

Decision Letter 1

Susan Horton

Roles

Author response to Decision Letter 1

Decision Letter 2

Susan Horton

Roles

Acceptance letter

Susan Horton

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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