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Identification of Foods to Monitor the Sodium Content of Processed Foods using Nationally Representative Consumption Data for Developing a Sodium Reduction Program in the Philippines
Journal of Nutrition & Food Sciences

Journal of Nutrition & Food Sciences
Open Access

ISSN: 2155-9600

Research Article - (2021)

Identification of Foods to Monitor the Sodium Content of Processed Foods using Nationally Representative Consumption Data for Developing a Sodium Reduction Program in the Philippines

Maria Sofia Amarra1,2*, Mario V. Capanzana3 and Francisco de los Reyes4
 
*Correspondence: Maria Sofia Amarra, Department of Food Science and Nutrition, University of the Philippines, Philippines, Email:

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Abstract

Background: The study identified processed foods that can be targeted for reformulation and whose sodium content can be monitored over time in order to reduce sodium intake in the Philippines. The objectives were to estimate per capita sodium intake from minimally processed and processed foods by income quintile and urban/rural location; and identify foods that contribute to the variance in sodium intake of the Philippine population.

Methods: One day household food weighing data covering 4880 households from the 2008 National Nutrition Survey was used. Mean per capita sodium consumption and percentiles of intake from processed and minimally processed food categories were calculated using STATA. Regression analysis was used to identify foods that contributed to the variance in sodium intake.

Results: Foods which significantly accounted for 99.4% of the variance in sodium intake were 13 types of processed foods and 2 types of minimally processed foods. Processed Soup, Sauces, and Flavor Enhancers contributed the greatest proportion to per capita sodium intake. Processed foods with significant contributions to the variance in intake were instant noodles, traditional fermented condiments and sauces, dried and processed meat/fish/poultry products, salted eggs, alcoholic beverages, white bread and pan de sal (a traditional Filipino bread), wheat and egg noodles, crispy cereal chips and extruded snacks, butter and margarine, cheese, and chocolate based beverages.

Conclusion: Identifying processed foods that significantly contribute to sodium intake, followed by reformulating and monitoring the sodium content of these foods over time, should be considered as one strategy to reduce sodium intake in the Philippines.

Keywords

Sodium; Salt intake; Dietary sources; Philippines; Salt reduction

Introduction

Hypertension is a risk factor for cardiovascular disease driven by excess dietary salt intake. The WHO Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 set voluntary targets for achievement in 2025 by its Member States, including “a 30% relative reduction in mean population intake of salt/sodium (Na)” towards the recommended level of 2000 mg Na/ day (5 g salt/day) [1]. This can be achieved by developing “guidelines, recommendations or policy measures that engage different relevant sectors, such as food producers and processors, and other relevant commercial operators, as well as consumers, to reduce the level of salt/sodium added to food (prepared or processed)” [1]. In response to the global target for reduction in salt intake, several countries have implemented population sodium reduction strategies. These strategies include identification of major sources of sodium in the diet and reformulation of a set number of products available on the market [2]. In the United States, a sodium monitoring program led by USDA tracks “sentinel foods” i.e., foods that contribute to sodium intake in the population and are used as indicators to track changes in the sodium content of processed foods [3]. Since most sodium in the diet comes from processed foods, reducing the amount of sodium in sentinel foods will translate into reduced sodium intake at the population level. The present study aimed to identify processed foods that can be targeted for reformulation to reduce sodium intake among Filipinos, using one day household food weighing data from the 2008 National Nutrition Survey. The objectives were to:

1. Estimate per capita sodium intake from minimally processed and processed food groups by income quintile and urban/rural location;

2. Identify foods that significantly contribute to the variance in per capita sodium intake among Filipinos which can potentially serve as indicator foods to monitor the sodium content of processed foods.

Methods

Aim, design and setting

The study identified processed foods that can be targeted for reformulation and whose sodium content can be monitored over time to achieve reduced sodium intake in the Philippines. This cross sectional study examined per capita food consumption obtained from one day household food weighing data of 4880 households participating in the 2008 National Nutrition Survey.

Sampling method

The 2008 National Nutrition Survey used a stratified multi stage sampling design. In the first stage, primary sampling units were selected from 17 regions and 79 provinces throughout the country. In the second stage, enumeration areas were identified from primary sampling units. Finally specific households from each enumeration area were selected, comprising a total of 4880 households (~5 members per household) nationwide.

Characteristics of the sample

The sample comprised 43% urban and 57% rural households with more households belonging to lower income groups, reflecting the country’s socioeconomic classification as a low middle income country. Table 1 shows the distribution of households by location of residence and wealth quintile.

Table 1: Distribution of sample households, Philippines 2008.

Wealth quintile Urban No. % Rural No. % Both No. %
Q0 (unidentified) 5 0 7 0 12 0
Q1 (lowest) 167 3 945 19 1112 23
Q2 351 7 685 14 1036 21
Q3 466 10 511 10 977 20
Q4 540 11 403 8 943 19
Q5 (highest) 592 12 208 4 800 16
All 2121 43 2759 57 4880 100
Note: Median number of household members = 5

Data collection

One day household weighing of food items from breakfast through supper, including snacks was conducted. Digital dietetic scales were calibrated using a one kg standard weight. On the day of weighing, all items were weighed before cooking or serving including: raw as purchased foods to be cooked for each meal and snacks, food served and eaten raw, cooked and processed foodstuff served directly on the dining table. Leftover foods were weighed and, together with the weights of plate wastes and foods given out, were deducted from the sum of weighed food to obtain the actual amount of food consumed by the household [4]. A food inventory was also conducted. Nonperishable food items that might be used anytime of the day such as coffee, sugar, salt, cooking oil, and other condiments were weighed at the beginning and end of the food weighing day. Foods eaten by household members who ate outside their homes were recalled and recorded to complete the household’s food record. Sample weighing of similar food items eaten out was performed for validation purposes [4].

Data analysis

Prior to statistical analysis, the following steps were taken:

1. Creation of a food composition database for sodium

2. Grouping of all foods consumed into 2 categories: Minimally Processed Foods and Processed Foods/Food Products.

Development of a food composition database for sodium

The Philippine food composition table does not provide nutrient values for sodium. Hence, the sodium content of all foods consumed was estimated from values derived from different food composition tables, using the process described by INFOODS. The INFOODS guidelines for food matching guided the selection of appropriate foods from which to borrow sodium values, in the most appropriate source of compositional data [5]. Values for sodium consumption were then computed by multiplying each food’s sodium content by the amount consumed by the entire household.

Grouping of foods into minimally processed and processed food categories

Almost all foods consumed in the Filipino diet are processed or cooked to a certain extent prior to ingestion. FAO recommended that the level of food processing should be taken into account when examining food consumption data, so as to inform the development and implementation of food based guidelines and approaches to the prevention of chronic diseases [6]. The NOVA food classification system developed by researchers in Brazil, classifies foods according to the nature, degree, and purpose of processing [6,7]. The present study used a modified version of the NOVA classification wherein foods were classified into two main groups and each group was further classified into subgroups

1. Minimally Processed Foods (subgroups comprised cooked/ prepared whole foods, e.g., boiled rice and tubers, whole fish/meat/chicken dishes, milk (fresh liquid and whole milk powder), raw or cooked whole vegetables and fruits)

2. Processed Foods (subgroups comprised processed and preserved/salted food products, foods made from processed ingredients).

All foods consumed by survey households were listed. Similar foods were grouped into specific subgroups (a total of 18 subgroups or categories were created for 1306 individual food items). Each food category was classified as belonging to either the minimally processed or processed groups (Table 2). This classification was done to allow the development of recommendations for sodium reduction that correspond to dietary patterns of the entire population.

Table 2: Minimally processed and processed foods consumed by the population, Philippines 2008.

Main food groups & subgroups Foods in each subgroup
A. Minimally processed foods
1. Fish, meat, poultry Fresh meat, poultry, organ meat
Fresh fish & seafood
Prepared dishes ready-to-eat
Unsalted fresh eggs
2. Rice, cereals, starches Cooked rice
Corn & other cereals
Starchy roots & tubers
3. Vegetables & fruits Fresh fruits & vegetables
Seaweed dried & fresh
Sundried & cooked fruits
4. Beans, nuts, seeds Cooked beans, nuts, seeds dishes
5. Milk Liquid milk (fresh, evaporated, recombined);
Milk powder (whole, full cream, filled);
Skimmed milk
Fermented milk
B. Processed foods/food products
6. Processed fish, meat & poultry products Canned & processed meat, fish, seafood
Dried & smoked fish & seafood
Salted eggs
7. Baked products White bread & pan de sal
Sweet breads
Biscuits, crackers, cookies
Cakes, pies, pastries
8. Instant noodles Instant noodles
9. Processed soup, sauces, flavor enhancers Soup powder
Fermented fish & seafood sauce
Salt
MSG and MSG-containing cubes
10. Other noodles & pasta Wheat & egg noodles
Rice & mungbean noodles
11. Rice, cereal, starch products Sweetened rice cakes & snacks
Sweet popcorn
Crispy cereal chips & extruded snacks
Breakfast cereal
Cassava cake & snacks
Infant cereal
Starch wrappers
12. Non-alcoholic beverages Coffee/ tea
Chocolate beverage
Sweetened juice & other sweet drinks
Soft drink
13. Fats, oils, & products Cooking oil & lard
Creamers & cream
Butter & margarine
Peanut butter, mayonnaise & spreads
14. Sugars & sweets Sugar (refined, second class, crude)
Candies & jams
15. Milk formula & milk products Milk formula for adults, infants & children
Ice cream & dairy products
Cheese & fermented dairy products
Condensed milk
16. Alcoholic beverages Beer & indigenous alcoholic beverages
17. Vegetable & fruit products Canned fruit & fruit juice
Canned vegetables
Preserved fruits
18. Beans, nuts & seed products Soy foods & beverages
Salted nuts & seeds

Statistical analysis

Per capita consumption of sodium from different food subcategories was obtained by summing the total amount of sodium (in milligrams) ingested by the entire household divided by the number of consumption units. Percentiles of sodium intake (P25, P50, P75, and P99) from different food subcategories and interquartile range (IQR) were obtained using STATA. The percentage contribution of different food categories to mean per capita intake was calculated using the ratio of means wherein mean sodium intake from a specific category was divided by mean per capita sodium intake.

Multiple regression analysis was used to identify specific foods that contributed to the variance in sodium intake for the entire population. Sodium intake values from specific foods in the different categories shown in Table 2 were transformed logarithmically. Thus the form of the regression model fitted is

Equation

Where V1, V2, …, Vp the milligram consumption in different foods is across food groups, and ε is the error term that represents the variation not due to food consumption, including measurement errors. The significant variables were obtained by backward elimination. Variables in the equation were retained at 5% level of significance. To account for heteroskedasticity, the linearized robust standard errors were produced. Outliers and influential observations were excluded from the analysis.

Results

Per capita sodium intake from different food categories

Table 3 shows the mean per capita sodium intake and percentile distribution of sodium intake from minimally processed and processed food groups. Mean per capita intake exceeded the WHO recommendation of 2000 mg sodium, with rural households ingesting more sodium than urban households. Median sodium intake was highest for Processed Soup, Sauces and Flavor Enhancers, with half of the population consuming >1416 mg Na from this food category alone. Median intake was highest among the highest income households.

Table 3: Mean per capita intake by urban/rural location and percentile distribution of sodium (mg/day) ingested from minimally processed and processed food groups by income quintile and urban/rural location.

- Per capita Na intake (mg/day)
Urban Rural Both
Mean ± SE 2767 ± 57 2862 ± 68 2813 ± 44
Minimally processed foods P25 P50 P75 P99 IQR P25 P50 P75 P99 IQR P25 P50 P75 P99 IQR
A. Fish, meat, poultry
Q1 (lowest) 3 24 62 547 59 0 18 56 693 56 0 18 56 675 56
Q2 24 49 105 597 81 10 43 93 730 83 17 45 95 713 79
Q3 34 64 124 776 89 25 58 118 585 94 30 61 121 756 91
Q4 42 80 141 637 99 44 95 173 953 129 43 87 150 784 107
Q5 (highest) 63 107 175 869 112 67 110 189 1173 123 64 108 176 880 112
All wealth quintiles 38 77 139 756 101 10 48 105 846 95 24 62 125 776 101
B. Rice, cereals, starches
Q1 50 76 101 240 51 53 87 114 244 61 53 86 112 244 60
Q2 60 76 104 192 44 62 85 115 244 53 62 81 110 207 48
Q3 60 78 101 194 41 62 82 110 190 47 61 80 106 193 45
Q4 58 74 95 187 37 61 81 113 319 52 58 76 100 219 42
Q5 51 68 88 170 38 51 78 100 279 49 51 69 92 205 41
All 56 73 96 193 40 59 84 112 239 53 57 78 104 213 47
C. Vegetables & fruits
Q1 2 4 15 90 13 2 8 17 128 16 2 7 17 105 15
Q2 1 5 13 131 11 2 8 19 82 17 2 7 16 119 15
Q3 3 7 15 72 12 3 7 16 125 13 3 7 15 91 12
Q4 3 9 17 85 13 4 10 21 112 17 3 9 18 94 15
Q5 4 10 20 104 15 4 10 21 120 16 4 10 20 104 16
All 3 8 16 90 13 3 8 18 125 16 3 8 17 104 14
D. Milk
Q1 0 0 0 48 0 0 0 0 55 0 0 0 0 55 0
Q2 0 0 1 69 1 0 0 0 81 0 0 0 0 78 0
Q3 0 0 8 90 8 0 0 2 76 2 0 0 6 79 6
Q4 0 0 8 138 8 0 0 14 77 14 0 0 9 106 9
Q5 0 0 12 134 12 0 0 9 101 9 0 0 12 131 12
All 0 0 8 118 8 0 0 0 73 0 0 0 5 90 5
E. Beans, nuts, seeds
Q1 0 0 0 9 0 0 0 0 7 0 0 0 0 7 0
Q2 0 0 0 8 0 0 0 0 8 0 0 0 0 8 0
Q3 0 0 0 7 0 0 0 0 10 0 0 0 0 9 0
Q4 0 0 0 7 0 0 0 0 16 0 0 0 0 10 0
Q5 0 0 0 6 0 0 0 0 4 0 0 0 0 6 0
All 0 0 0 7 0 0 0 0 9 0 0 0 0 8 0
- Urban Rural Both
Processed foods P25 P50 P75 P99 IQR P25 P50 P75 P99 IQR P25 P50 P75 P99 IQR
A. Processed soups, sauces & flavor enhancers
Q1 499 1118 2122 8113 1623 740 1446 2483 9307 1743 691 1413 2459 8776 1767
Q2 354 1082 1988 7526 1634 718 1474 2689 7452 1970 555 1354 2484 7452 1929
Q3 333 1137 2174 7035 1841 833 1498 2547 8174 1715 510 1331 2372 8000 1862
Q4 569 1375 2574 10021 2004 926 1679 2775 7908 1849 666 1454 2707 9693 2041
Q5 705 1537 2943 8020 2238 916 1812 3114 10778 2198 713 1630 2959 8720 2246
All 508 1309 2496 8301 1988 770 1524 2635 8600 1866 629 1416 2556 8315 1926
B. Processed fish, meat & poultry products
Q1 0 84 209 1370 209 0 57 281 1762 281 0 61 268 1641 268
Q2 0 95 320 1501 320 0 128 341 1596 341 0 113 332 1596 332
Q3 0 131 362 1562 362 0 140 353 1775 353 0 132 360 1721 360
Q4 0 139 424 2474 424 0 128 429 2408 429 0 135 429 2474 429
Q5 0 152 456 1957 456 0 143 349 1553 349 0 150 431 1888 431
All 0 131 392 1900 392 0 110 330 1762 330 0 120 358 1829 358
C. Baked products
Q1 0 0 55 289 55 0 0 10 353 10 0 0 14 353 14
Q2 0 25 145 695 145 0 0 49 396 49 0 0 86 531 86
Q3 0 67 187 655 187 0 8 91 748 91 0 36 144 679 144
Q4 0 86 240 792 240 0 32 142 550 142 0 63 207 771 207
Q5 30 142 317 920 287 0 87 275 1088 275 23 131 305 934 282
All 0 77 228 771 228 0 0 72 582 72 0 25 150 720 150
D. Instant noodles
Q1 0 0 0 1076 0 0 0 0 1050 0 0 0 0 1064 0
Q2 0 0 0 1067 0 0 0 11 1044 11 0 0 0 1067 0
Q3 0 0 0 1163 0 0 0 0 960 0 0 0 0 1032 0
Q4 0 0 0 922 0 0 0 0 800 0 0 0 0 907 0
Q5 0 0 0 990 0 0 0 0 1110 0 0 0 0 1000 0
All 0 0 0 1050 0 0 0 0 1009 0 0 0 0 1032 0
E. Other noodles & pasta
Q1 0 0 0 180 0 0 0 0 310 0 0 0 0 259 0
Q2 0 0 0 296 0 0 0 0 360 0 0 0 0 360 0
Q3 0 0 1 445 1 0 0 0 388 0 0 0 1 445 1
Q4 0 0 1 349 1 0 0 0 502 0 0 0 1 423 1
Q5 0 0 2 604 2 0 0 1 745 1 0 0 1 604 1
All 0 0 1 482 1 0 0 0 423 0 0 0 0 450 0
F. Rice, cereal, starch products
Q1 0 0 0 140 0 0 0 0 143 0 0 0 0 142 0
Q2 0 0 0 221 0 0 0 0 213 0 0 0 0 221 0
Q3 0 0 1 221 1 0 0 0 156 0 0 0 0 178 0
Q4 0 0 4 289 4 0 0 1 238 1 0 0 3 238 3
Q5 0 0 20 228 20 0 0 17 461 17 0 0 19 276 19
All 0 0 6 235 6 0 0 0 209 0 0 0 1 221 1
G. Non-alcoholic beverages
Q1 0 0 4 44 4 0 0 3 67 3 0 0 3 67 3
Q2 0 0 11 81 11 0 0 7 89 7 0 0 9 82 9
Q3 0 2 16 104 16 0 1 13 71 13 0 1 14 84 14
Q4 0 4 20 119 20 0 3 23 106 23 0 3 21 106 21
Q5 0 10 29 152 28 0 8 22 232 22 0 9 27 159 27
All 0 3 19 115 19 0 0 10 91 10 0 1 15 105 15
H. Milk formula & milk products
Q1 0 0 0 5 0 0 0 0 9 0 0 0 0 9 0
Q2 0 0 0 99 0 0 0 0 40 0 0 0 0 55 0
Q3 0 0 0 142 0 0 0 0 67 0 0 0 0 120 0
Q4 0 0 0 246 0 0 0 0 262 0 0 0 0 246 0
Q5 0 0 31 362 31 0 0 12 297 12 0 0 26 362 26
All 0 0 0 285 0 0 0 0 91 0 0 0 0 215 0
I. Fats, oils, & products
Q1 0 0 1 20 1 0 0 0 24 0 0 0 1 23 1
Q2 0 0 2 41 2 0 0 1 34 1 0 0 2 41 2
Q3 0 0 3 155 3 0 0 2 95 2 0 0 3 98 3
Q4 0 1 5 89 5 0 0 3 196 3 0 0 4 117 4
Q5 0 2 6 176 6 0 0 5 208 4 0 1 5 176 5
All 0 0 3 131 3 0 0 2 82 2 0 0 3 106 3
J. Beans, nuts, seed products
Q1 0 0 0 5 0 0 0 0 1 0 0 0 0 3 0
Q2 0 0 0 11 0 0 0 0 9 0 0 0 0 11 0
Q3 0 0 0 24 0 0 0 0 3 0 0 0 0 21 0
Q4 0 0 0 28 0 0 0 0 13 0 0 0 0 24 0
Q5 0 0 0 28 0 0 0 0 45 0 0 0 0 28 0
All 0 0 0 24 0 0 0 0 8 0 0 0 0 14 0
K. Sugars & sweets
Q1 0 0 2 40 2 0 0 2 21 2 0 0 2 21 2
Q2 0 0 1 12 1 0 1 3 26 3 0 0 2 19 2
Q3 0 0 1 19 1 0 0 2 23 2 0 0 2 20 2
Q4 0 0 1 39 1 0 0 2 33 2 0 0 2 33 2
Q5 0 0 2 43 2 0 0 2 57 2 0 0 2 43 2
All 0 0 1 29 1 0 0 2 23 2 0 0 2 26 2
L. Vegetable & fruit products
Q1 0 0 0 134 0 0 0 0 0 0 0 0 0 0 0
Q2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Q3 0 0 0 11 0 0 0 0 5 0 0 0 0 6 0
Q4 0 0 0 16 0 0 0 0 11 0 0 0 0 16 0
Q5 0 0 0 50 0 0 0 0 5 0 0 0 0 49 0
All 0 0 0 30 0 0 0 0 4 0 0 0 0 15 0
M. Alcoholic beverages
Q1 0 0 0 0 0 0 0 0 3 0 0 0 0 1 0
Q2 0 0 0 4 0 0 0 0 25 0 0 0 0 15 0
Q3 0 0 0 2 0 0 0 0 2 0 0 0 0 2 0
Q4 0 0 0 1 0 0 0 0 20 0 0 0 0 4 0
Q5 0 0 0 5 0 0 0 0 109 0 0 0 0 5 0
All 0 0 0 3 0 0 0 0 6 0 0 0 0 4 0

Percentage contribution of processed and minimally processed foods to per capita sodium intake

Table 4 shows the percentage contribution of processed and minimally processed foods to per capita sodium intake of urban and rural households across income quintiles.

Table 4: Percentage contribution of processed and minimally processed foods to per capita sodium intake by income quintile in urban and rural households. Philippines 2008.

Percentage contribution to mean per capita Na intake (%)
Urban households Rural households
Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 All
A. Processed foods
Processed soup, sauces & flavor enhancers 57.96 55.86 58.14 73.2 76.56 67.36 71.62 72.7 74.93 79.39 97.41 75.83
Processed fish, meat & poultry products 7.37 8.77 9.96 11.53 12.34 10.75 7.41 9.1 9.7 11.29 9.9 9.08
Baked products 1.55 3.85 4.82 5.85 7.83 5.66 1.01 1.71 2.8 3.75 6.48 2.4
Instant noodles 3.33 3.93 3.87 3.21 3.04 3.45 3.94 4.37 3.82 3.52 3.09 3.89
Other noodles & pasta 0.46 0.68 1.02 0.93 1.07 0.92 0.47 0.51 0.7 0.99 1.37 0.68
Rice, cereal, starch products 0.38 0.5 0.56 0.8 0.83 0.68 0.26 0.52 0.46 0.54 0.96 0.46
Milk formula & milk products 0 0.17 0.28 0.42 1.26 0.58 0.01 0.06 0.14 0.41 0.76 0.17
Non-alcoholic beverages 0.21 0.31 0.44 0.55 0.78 0.54 0.23 0.31 0.35 0.56 0.83 0.37
Fats, oils & products 0.05 0.13 0.18 0.31 0.46 0.28 0.07 0.1 0.17 0.28 0.28 0.15
Sugars & sweets 0.08 0.05 0.05 0.09 0.11 0.08 0.07 0.09 0.07 0.1 0.12 0.08
Beans, nuts, seed products 0.01 0.01 0.02 0.07 0.12 0.06 0.01 0.27 0.12 0.02 0.14 0.1
Veg & fruit products 0.1 0.08 0.03 0.03 0.08 0.06 0.01 0.04 0.04 0.03 0.02 0.03
Alcoholic beverages 0 0.04 0 0 0.01 0.01 0.03 0.04 0 0.02 0.08 0.03
B. Minimally processed foods
Fish, meat, poultry 2.17 3.52 4.3 4.55 5.93 4.61 2.14 3.08 4.09 5.51 6.68 3.63
Rice, cereals, starches 3.12 3.16 3.19 3.03 2.78 3.01 3.29 3.41 3.3 3.39 3.14 3.32
Veg & fruits 0.42 0.45 0.45 0.51 0.57 0.5 0.57 0.55 0.52 0.62 0.7 0.57
Milk 0.18 0.26 0.36 0.4 0.5 0.39 0.12 0.26 0.26 0.35 0.35 0.23
Beans, nuts, seeds 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.03 0.01 0.02

Processed foods

In both urban and rural households, Processed Soup, Sauces and Flavor Enhancers contributed the greatest amount (>50%) to sodium intake, followed by Fish, Meat and Poultry Products. Rural households consumed more Processed Soup, Sauces and Flavor enhancers across all income quintiles than urban households. Baked Products and Instant Noodles were the next highest contributors. Urban households consumed more Baked Products while rural households consumed more Instant Noodles.

Minimally processed foods

Among the highest income quintiles, minimally processed food categories that contributed the most sodium were Fish, Meat and Poultry followed by Rice, Cereals and Starches. In lower income quintiles, (Q1 in urban and rural areas, and Q2 in rural areas), Rice, Cereals and Starches contributed the most sodium followed by Fish, Meat and Poultry. In both urban and rural locations, Vegetables and Fruits contributed minimal amounts (<1%) of sodium. Foods that contributed the least amount to sodium intake were Milk followed by Beans, Nuts and Seeds.

Foods that contribute significantly to the variance in per capita sodium intake

Table 5 shows the results of multiple regression analysis. A total of 15 foods (13 foods belonging to the processed food group, and 2 foods belonging to the minimally processed group) explained the variance in per capita sodium intake. Minimally processed foods that contributed significantly to sodium were cooked white rice and ready to eat prepared foods (fish, meat, poultry, and organ meats). All other foods with significant contributions belonged to the processed food group. Among all foods, cooked white rice contributed the greatest amount of sodium (i.e., consumption of one gram of rice increased per capita sodium intake by 0.79 mg) followed by instant noodles (i.e., consumption of one gram instant noodles increased per capita sodium intake by 0.02 mg). This was followed by traditional condiments (fermented fish/seafood sauce) and table salt, and processed meat, fish, poultry products.

Table 5: Food groups/subgroups and foods within each subgroup that contribute significantly to the variance in per capita sodium intake of Filipinos.

R2/Adjusted R2=99.39% Coefficient (b) Linearized robust S.E. p-value
Processed foods
1. Instant noodles 0.019 0.001 0
2. Processed soup, sauces, flavor enhancers
-Fermented fish/seafood sauce 0.011 0.003 0.001
-Table salt 0.011 0.003 0
3. Processed fish, meat, poultry products
-Dried and smoked fish & seafood 0.01 0.003 0.004
-Canned & processed meat, fish, seafood 0.007 0.003 0.023
-Eggs salted 0.004 0.002 0.031
4. Alcoholic beverages 0.009 0.004 0.034
5. Baked products
-White bread & pandesal 0.008 0.015 0
6. Other noodles & pasta
-Noodles (wheat and egg) 0.006 0.003 0.016
7. Rice, cereal & starch products
-Crispy cereal chips & extruded snacks 0.005 0.001 0
8. Fats, oils & products
-Butter & margarine 0.005 0.002 0.01
9. Milk products
-Cheese & fermented dairy products 0.004 0.001 0.011
10. Non-alcoholic beverages
-Chocolate beverage 0.002 0.001 0.034
B. Minimally processed foods
1. Rice, cereals, starches
-Cooked white rice 0.79 0.267 0.003
2. Fish, meat, poultry
-Prepared dishes (ready-to-eat) 0.01 0.004 0.012

Discussion

The prevalence of hypertension among adult Filipinos aged 20 years and above increased from 16% in 2003 to 21% in 2008 to 28% in 2013, highlighting the need to reduce sodium intake [8,9]. The present study identified processed foods that can be targeted for reformulation to achieve reduced salt intake. Important sources of sodium were 13 foods in the processed food group and 2 foods in the minimally processed group, which together accounted for 99.4% of the variance in sodium intake of the entire population. In the processed foods group, the greatest contributors were the following: instant noodles and foods in the following categories: Processed Soup, Sauces and Flavor Enhancers (traditional fermented fish and seafood sauces, table salt); Processed Fish, Meat and Poultry Products (dried/smoked fish and seafood, canned and processed meat/fish/seafood, salted eggs); Alcoholic Beverages; Baked Products (white bread, pan de sal); Other Noodles and Pasta (wheat and egg noodles); Rice, Cereal and Starch Products (crispy cereal chips and extruded snacks); Fats, Oils and Products (butter, margarine); Milk Products (cheese); Non-alcoholic Beverages (chocolate based drinks).

Instant noodles

Estimated per capita consumption of instant noodles in 2008 was 2.86 kg/year or approximately 8 g/day, contributing 158 mg Na/day [10]. In 2017, instant noodles was the top noodle product consumed in the Philippines (consumed by 70.12% of households) [11]. Households consumed an average of 0.05 kg instant noodles per week or 2.69 kg a year. Rural households consumed greater amounts at 2.78 kg per year. During the same period, 27.6% of households reported substituting instant noodles for rice. The most frequent reason for substitution (reported by 18.43% of respondents) was that it is more affordable than rice [11]. Instant noodles contain ≈1975 mg Na/100 g [12]. Wheat and egg noodles (commonly called pancit canton) contain ≈1006 mg Na/100 g [13].

Processed soup, sauces and flavour enhancers

Within this category, table salt and traditional fermented fish/ seafood sauces were the significant contributors to sodium intake. In 2008, coarse salt was the most commonly consumed condiment in the Philippines, with 64.9% of households consuming an average of 3 grams salt per day, equivalent to 1200 mg Na [14]. Philippine shrimp paste contains 13 g-14 g Na/100 g [15]. The percentage of households consuming these traditional fermented foods in 2008 was: bagoong isda (fermented anchovy) and ginamos (fermented shrimp)-10.1%; patis (fish sauce)-6.1%; bagoong alamang (shrimp paste)-4.7% [16]. In a study among 1789 women, Lee found that salty condiments added during cooking or at the table accounted for 76.3% of sodium intake [17]. The most significant source of sodium was table salt, contributing 53.3% for women who consumed <4600 mg/day of sodium and 66.5% for women who consumed higher amounts of sodium [17].

Pros and cons of indigenous fermented sauces

Traditional fermented salted products, while contributing significantly to sodium intake of Filipinos, are an important part of the food culture in the Philippines. Commonly used indigenous sauces are fermented fish and seafood sauces (patis or fish sauce, bagoong or fish/shrimp paste), soy sauce. These products are generally produced with high levels of salt, up to 25% for fish sauces and 11% to 25% for soy sauce [18,19]. High levels of salt and low pH are important to suppress the growth of pathogenic microorganisms and enable bacterial degradation of proteins, carbohydrates, and nucleic acids. In spite of their high sodium content, these fermented sauces were shown to have functional effects. Japanese style fermented soy sauce (shoyu) showed antiallergic, antimicrobial, antihypertensive, and anticarcinogenic effects [20,21]. Fermented foods contain live microorganisms and therefore comprise a good source of probiotics. Lactic acid bacteria were found in fermented fish (ranging from 3.48 to 5.43 log cfu/g) while aerobic bacteria were found in fish sauce (ranging from 4.92 to 5.53 log cfu/g) [22]. Fermentation derived microorganisms have the potential to influence gut microbiota diversity, structure, and function and increase the amount of nutrients such as vitamins and other bioactive molecules produced from microbial metabolism that are not present in the original food [22]. These bacteria may also secrete anti-microbial agents, degrade anti-nutritive compounds, produce short chain fatty acids from indigestible carbohydrates, and contribute to immune homeostasis [22-24]. A study on the composition of shrimp pastes produced in some parts of the Philippines showed these foods were good sources of omega-3 fatty acids, iron, zinc, and calcium [15]. Due to their extensive use, fortification of condiments and seasonings is seen as a cost effective intervention to address micronutrient deficiencies in Southeast Asia [25,26]. Studies in young children and adult women suggested that fortification of sauces (fish sauce, soy sauce) can effectively address iron and iodine deficiencies [27,28].

Processed fish, meat, poultry products

Processed animal foods that contributed significantly to sodium intake were dried/smoked fish and seafood, canned/processed meat, fish and seafood, and salted eggs. In 2008, consumption of fish and fish products was 110 grams per capita. Canned sardines (containing approximately 521 mg Na/100 g) was consumed by 15.3% of households with mean consumption of 8 grams per capita per day [12,14]. Dried and smoked fish was consumed by 20.5% of households [16]. Dried fish contains ≈7000 mg Na per 100 g [29]. Filipinos aged 60+ years ate the most fish and fish products (15.6% of total food consumption), followed by those aged 20 to 59 years (14.7% of total consumption) [16].

Consumption of meat and meat products in 2008 was 83 grams per capita. The Family Income and Expenditure Survey (FIES) showed that household food expenditures on meats increased by 4 to 5 percentage points from 1965 to 2000. The biggest growth in expenditure was for processed meats, increasing by 2.7% during the same period [30]. In 2003, processed meat products (hotdogs, meatloaf, sausages) represented nearly 30% of per capita meat intake [31].

Limitations of the study

The study examined only 2008 national food consumption data. Data from multiple successive surveys should be examined since the market for processed foods is dynamic, with products constantly being introduced, reformulated, or taken out. In spite of this, the present study is the currently the only one that identifies sodiumcontributing foods for development of population sodium reduction initiatives. The consumption of processed foods among Filipinos has increased over time. For instance, the demand for instant noodles in the Philippines increased from 3400 million servings in 2016 to 4470 million servings in 2020 [32]. For processed meat, the average volume per person is expected to amount to 3.9 kg in 2021 and the market is expected to grow annually by 1.89% from 2021 to 2025 [33]. During this pandemic, sodium intake is expected to increase further. Food relief packs distributed nationwide by the Department of Social Welfare and Development contains rice, corned beef, sardines, and chocolate energy drink or coffee [34]. Corned beef, sardines, and chocolate beverage are among the foods identified in this study which significantly contribute to the variance in sodium intake of Filipinos.

Conclusion

Indicator foods that can be targeted for reformulation to reduce population sodium intake among Filipinos are instant noodles, traditional fermented condiments and sauces, and processed meat, fish, and poultry products. Other processed foods with significant contributions to the variance in sodium intake and whose consumption can be reduced via consumer education or reformulation (e.g., “stealth” reductions) are table salt, alcoholic beverages, white bread and pan de sal (a traditional bread), crispy cereal chips and extruded snacks, butter and margarine, cheese, and chocolate based beverages.

Acknowledgement

Acknowledgement is given to International Life Sciences Institute Southeast Asia Region (ILSI SEAR) Science Cluster on Food and Nutrients for Public Health Guidance for partial funding support.

Sources of Funding

The study was funded by International Life Sciences Institute Southeast Asia Region (ILSI SEAR) which is supported by its industry members.

Conflict of Interest

The Food and Nutrition Research Institute received funds for access to data. MVC and GG received honoraria from ILSI SEAR. The rest of the authors declare no conflict of interest.

References

Author Info

Maria Sofia Amarra1,2*, Mario V. Capanzana3 and Francisco de los Reyes4
 
1School of Nutrition, Philippine Women’s University, Philippines
2Department of Food Science and Nutrition, University of the Philippines, Philippines
3Department of Science and Technology, Food and Nutrition Research Institute, Philippines
4Department of Statistics, University of the Philippines, Philippines
 

Citation: Maria SA, Capanzana MV, Gironella G, Reyes FD (2021) Identification of Foods to Monitor the Sodium Content of Processed Foods using Nationally Representative Consumption Data for Developing a Sodium Reduction Program in the Philippines. J Nutr Food Sci. 11:829.

Received: 09-Dec-2021 Published: 30-Dec-2021, DOI: 10.35248/2155-9600.21.s9.1000833

Copyright: © 2021 Amarra MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Sources of funding : The study was funded by International Life Sciences Institute Southeast Asia Region (ILSI SEAR) which is supported by its industry members.

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