“All rights reserved. The author and the promoters permit the use of this thesis for consulting purposes and copying of parts for personal use. However, any other use falls under the limitations of copyright regulations, particularly the stringent obligation to explicitly mention the source when citing parts out of this thesis.”
“De auteur en de promotors geven de toelating deze scriptie voor consultatie beschikbaar te stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te vermelden bij het aanhalen van resultaten uit deze thesis.”
Ghent University, 19th of August, 2010
Promoter,
Prof. Dr. ir. Patrick Van Damme
Tutor, ir. Céline Termote Author, ir. Orily Depraetere
Author right
Faculty of Bioscience Engineering
Academic year 2009 – 2010
FOOD SECURITY AND DIETARY PATTERNS OF ADULT WOMEN IN KISANGANI CITY AND YAOSEKO, A RURAL TURUMBU VILLAGE IN DRCONGO
ORILY DEPRAETERE
Promotor: Prof. Dr. ir. Patrick Van Damme
Tutor: ir. Céline Termote
Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master of Nutrition and Rural Development, main subject: Tropical agriculture, major Plant Production
Foreword
After graduating as bio-engineer and because of my interest for developing countries, I decided to study an extra master, “Nutrition and Rural Development, main subject: Tropical agriculture, major Plant Production”. I experienced this master as an enrichment in many ways. For example, getting the possibility to work together and to exchange knowledge with people from all over the world. Doing this master dissertation strengthened this enrichment even more. I guess I never learned so much so fast in my whole life than during this past year.
In this foreword, I should thank everyone that is involved in the realization of this master dissertation. I will try not to forget anyone, although this is quite difficult because so many people were involved.
I would like to start with my promoter, Prof. Dr. ir. Patrick Van Damme. I want to thank him for giving me the opportunity to do this master dissertation and for his always cheerful way of teaching. I am very grateful to my tutor ir. Céline Termote, for bringing me in touch with Congo, for the assistance and stimulation to realize the field work in Congo, for always being available, for the quick feedback, for helping me not to overlook some mysterious problems, and for so many other things.
I also want to thank Prof. Dheda djailo Benoit, coordinator of the WEPs project, Prof. Marcel Bwama Meyi and Paluku muvatsi who made it possible to do this research at the Faculty of Sciences in Kisangani. I am especially grateful to Justine, who was like a sister to me. I am very grateful to the assistance of Justine, Angèle, Mammie, Evelyne and Marie. It was great working together with them and notwithstanding the often difficult conditions, they were always motivated and full of energy to go on with the research. Without them it would not have been possible to do so many surveys. Not to forget is the restaurant of mama Rimka, where they cooked the best Congolese dishes and learned me some Lingala. I want to thank the university assistant Ramazani and his assistant, mister Mundelendombe, who helped us finding the Turumbu women in Kisangani. Special thanks goes to papa Matunda for his hospitality in Yaoseko and Jean-Jacques who cooked for us in Yaoseko. Of course, I am grateful to all women of Kisangani and Yaoseko that participated in our research. Although the miserable circumstances some women lived in, they treated us with kindness and a smile on their face. Respect for all these women.
I would like to express my gratitude to ir. Christine Hoefkens, ir. Lieven Huybregts and ir.Carl Lachat of the Department of Food Safety and Food Quality at our faculty for helping with the data analysis.
I am also very grateful to my best friend and fellow student, Meng Yao.
Finally, let me thank my family and friends for the opportunities, support and appreciation.
i Foreword Table of contents
INTRODUCTION ...... 1 1. PRESENTATION OF CONGO ...... 3 1.1 History of Congo ...... 3 1.1.1 Congo before 1884 ...... 3 1.1.2 Congo Free State (1884-1907) ...... 3 1.1.3 Belgian Congo (1907-1960) ...... 3 1.1.4 The First Republic (1960-1965) ...... 4 1.1.5 The Second Republic (1965-1997) ...... 4 1.1.6 Conflicts (1997-2006) ...... 5 1.1.7 The Third Republic (2007-…) ...... 6 1.2 Geography ...... 7 1.3 Natural resources ...... 10 1.4 Demography ...... 11 1.5 Economy ...... 11 2. LITERATURE REVIEW ...... 13 2.1 Food security ...... 13 2.1.1 Definitions ...... 13 2.1.1.1 Availability ...... 13 2.1.1.2 Access ...... 14 2.1.1.3 Utilization ...... 14 2.1.2 Measurement of food security ...... 14 2.1.3 Causes and vulnerable groups ...... 15 2.1.3.1 Causes ...... 15 2.1.3.2 Vulnerable groups ...... 16 2.1.4 Consequences ...... 16 2.1.5 Levels ...... 17 2.1.6 Some measurement methods ...... 18 2.1.6.1 Methods based on coping strategies ...... 19 2.1.6.2 Advantages and disadvantages of the coping strategy methods ...... 24 2.1.6.3 Methods based on dietary diversity and food frequency ...... 25 2.1.6.4 Advantages and disadvantages of dietary diversity and food frequency methods .... 28 2.1.6.5 Methods based on food intake ...... 29 2.1.6.6 Advantages and disadvantages of food intake methods ...... 31 2.2 Food security and nutrition in DRCongo ...... 32
ii Table of contents 2.2.1 Food security and food consumption ...... 32 2.2.1.1 Some international parameters ...... 32 2.2.1.2 Food insecurity trends and causes in DRCongo ...... 33 2.2.1.3 Food consumption trends ...... 34 2.2.2 Nutritional outcomes ...... 37 2.2.2.1 The Multiple Indicator Cluster Surveys (2001) ...... 37 2.2.2.2 L’Enquête Démographique et de Santé (2007) ...... 38 2.2.2.3 Comparison of nutritional indicators ...... 39 2.2.3 Distribution of malnutrition ...... 40 3. METHODOLOGY ...... 41 3.1. Data collection ...... 41 3.1.1. Research site ...... 41 3.1.2. Sample design ...... 41 3.2.2.1 For both samples ...... 41 3.2.2.2 Kisangani ...... 41 3.2.2.3 Turumbu ...... 42 3.1.3. Equipment ...... 42 3.1.4. Training of interviewers ...... 43 3.2. Questionnaire ...... 45 3.3. Data analysis ...... 47 3.3.1 Wealth index ...... 47 3.3.2 Food security ...... 49 3.3.3 The 24-hour recall ...... 50 3.3.3.1 Elaboration of a food composition table for use in Kisangani and Yaoseko ...... 50 3.3.3.2 Average recipes ...... 51 3.3.3.3 The food intake program ...... 52 3.3.3.4 Evaluating the nutrient intakes ...... 54 3.3.4 Programs used for statistical analysis ...... 57 4. RESULTS AND DISCUSSION ...... 58 4.1. Socio-demographic profile of households ...... 58 4.1.1. Socio-demographic characteristics ...... 58 4.1.2. Composition of the households ...... 60 4.1.3. The wealth index ...... 60 4.1.4. Agricultural activities and income sources ...... 61 4.1.5. Annual household monetary income ...... 62 4.1.6. Pregnancy and breastfeeding ...... 63
iii Table of contents 4.2. Food security indicators ...... 64 4.2.1 Comparison of the food security indicators in the city and village sample ...... 64 4.2.2 The number of food (in)secure ...... 65 4.2.3 Socio-demographic parameters influencing food security ...... 67 4.3. Energy and macronutrient intake based on the 24-hour recall ...... 68 4.3.1 Comparison of the energy and macronutrient intake in the city and village sample ..... 68 4.3.2 Relation between the food security indicators and energy intake ...... 73 4.3.3 Socio-demographic parameters and other factors influencing energy intake ...... 75 4.3.4 Macronutrient intake according to income, age and education level ...... 77 4.3.4.1 Comparison of the macronutrient intake according to income category ...... 77 4.3.4.2 Comparison of the macronutrient intake according to to age category ...... 78 4.3.4.3 Comparison of the macronutrient intake according to education level ...... 79 4.3.5 Comparison of the energy intake with the recommended energy intake ...... 81 4.4. Micronutrient intake based on the 24-hour recall ...... 82 4.4.1 The recommended micronutrient intakes ...... 82 4.4.2 The consumption frequency of most important foods ...... 83 4.4.3 Comparison of the usual and the recommended micronutrient intake ...... 85 4.4.3.1 Fat-soluble vitamins ...... 85 4.4.3.2 Water-soluble vitamins ...... 87 4.4.3.3 Minerals ...... 90 5. CONCLUSION ...... 96
iv Table of contents List of tables
2 2 TABLE 1.1: SURFACE AREA (KM ), INHABITANTS AND POPULATION DENSITY (PEOPLE PER KM ) IN THE ORIENTAL 1 PROVINCE, TSHOPO DISTRICT, ISANGI TERRITORY, TURUMBU COLLECTIVITÉ AND KISANGANI ( UNDP, 1998 2 3 BASED ON THE SERVICES OF AGRICULTURAL STATISTICS, 1994, MINISTÈRE DU PLAN, 2005, ISANGI TERRITORY REPORT 2008) ...... 10 TABLE 2.1: GENERIC LIST OF COPING STRATEGIES (MAXWELL ET AL., 2003) ...... 20 TABLE 2.2: EXAMPLE OF ASSIGNING NUMERIC VALUES TO RELATIVE FREQUENCY (MAXWELL ET. AL, 2003) ...... 21 TABLE 2.3: OCCURRENCE QUESTIONS IN 3 DIFFERENT DOMAINS OF FOOD INSECURITY (FANTA, 2004 AND COATES, 2004) ...... 23 TABLE 2.4: CATEGORIES OF FOOD INSECURITY (COATES ET AL., 2007) WITH FS= FOOD SECURE HOUSEHOLDS AND FI=FOOD INSECURE HOUSEHOLDS ...... 24 TABLE 2.5: AGGREGATE FOOD GROUPS AND WEIGHTS TO CALCULATE THE FOOD CONSUMPTION SCORE (WFP, 2007) ...... 27 TABLE 2.6: FOOD CONSUMPTION SHORTFALLS (WFP, 2005) ...... 28 1 1 TABLE 2.7: THE PERCENT OF MALNUTRITION TYPES OF CHILDREN UNDER FIVE IN DRCONGO IN 1995 , 2001 AND 2 1 2 2007 ( TOLLENS, 2003 AND MINISTÈRE DU PLAN ET MINISTÈRE DE LA SANTÉ, 2008) ...... 39 TABLE 2.8: DISTRIBUTION OF GLOBAL MALNUTRITION OF CHILDREN UNDER FIVE (%) ACCORDING TO PROVINCE AND RESIDENCE AREA IN DRCONGO (TOLLENS, 2003) ...... 40 TABLE 3.1:ASSETS AND SERVICES USUALLY ASKED ABOUT IN DHS SURVEYS (RUTSTEIN AND JOHNSON, 2004) ... 48 TABLE 3.2: AD HOC WEIGHTS (W) FOR THE VARIABLES OF THE WEALTH INDEX ACCORDING TO CATEGORY ...... 49 TABLE 3.3: PERCENTAGE WEIGHTS OF THE INGREDIENTS IN 100 GRAM ‘PONDU’ ...... 51 TABLE 3.4: WEIGHT OF THE RAW INGREDIENTS IN 120 GRAM 'PONDU' ...... 52 TABLE 3.5: PROTEIN CONTENT OF COOKED INGREDIENTS IN 100 GRAM COOKED INGREDIENTS AND 120 GRAM COOKED PONDU...... 53 TABLE 3.6: CONVERSION FACTORS (STERKEN, 1998) ...... 54 TABLE 3.7: BASIC METABOLIC RATE (SCHOFIELD, 1985); W= WEIGHT IN KG ...... 54 TABLE 3.8: PHYSICAL ACTIVITY LEVEL (FAO/WHO/UNU, 2001) ...... 55 TABLE 3.9: RECOMMENDED MINIMUM AND MAXIMUM PERCENT ENERGY CONTRIBUTION TO TOTAL ENERGY INTAKE OF FAT, PROTEIN AND CARBOHYDRATE INTAKE (KOLSTEREN, 2010) ...... 55 TABLE 4.1: SOCIO-DEMOGRAPHIC CHARACTERISTICS OF THE CITY AND VILLAGE SAMPLE,*SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 59 TABLE 4.2: WOMEN GROUPED IN AGE CATEGORIES, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 60 TABLE 4.3: HOUSEHOLD COMPOSITION FOR THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 60 TABLE 4.4: WEALTH INDEX QUINTILES OF THE CITY AND VILLAGE SAMPLE ...... 60 TABLE 4.5: AGRICULTURAL ACTIVITIES IN THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN 2 BOTH SAMPLES (Χ -TEST) ...... 61 1 TABLE 4.6: PURPOSE OF AGRICULTURAL ACTIVITY IN THE CITY AND VILLAGE SAMPLE, SUM IS NOT 100% BECAUSE PEOPLE THAT JUST STARTED DID NOT CONSUME OR SELL ANYTHING ...... 61 TABLE 4.7: CULTIVATED CROPS IN THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 62 TABLE 4.8: MEAN ANNUAL HOUSEHOLD INCOME (US$), PRIMARY AND SECONDARY ACTIVITY IN THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 62 TABLE 4.9: INCOME QUARTILES IN THE CITY AND VILLAGE SAMPLE ...... 63 TABLE 4.10: PREGNANT AND BREASTFEEDING WOMEN IN THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 63 TABLE 4.11: FOOD SECURITY INDICATORS IN THE CITY AND VILLAGE SAMPLE, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 64 TABLE 4.12:PERCENTAGE OF HOUSEHOLDS IN DIFFERENT CSI CATEGORIES, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 65
v List of tables TABLE 4.13: PERCENTAGE OF HOUSEHOLDS IN DIFFERENT HFIA CATEGORIES, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 65 TABLE 4.14: PERCENTAGE OF WOMEN ACCORDING TO THE FOOD CONSUMPTION GROUPS, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 66 TABLE 4.15: WOMEN GROUPED IN DIFFERENT CATEGORIES OF FOOD SECURITY INDICATORS, *SIGNIFICANT DIFFERENCE BETWEEN BOTH SAMPLES (T-TEST, Α=0.05) ...... 66 TABLE 4.16: LINEAR REGRESSION MODELS OF FOOD SECURITY INDICATORS WITH ENERGY INTAKE ...... 73 TABLE 4.17: LINEAR REGRESSION MODELS WITH ENERGY INTAKE ...... 75 TABLE 4.18: ANOVA TEST OF SOCIO-DEMOGRAPHIC PARAMETERS INFLUENCING ENERGY INTAKE ...... 75 TABLE 4.19: MACRONUTRIENT ENERGY CONTRIBUTION ACCORDING TO INCOME CATEGORY (% OF WOMEN) ...... 77 1, 2 TABLE 4.20: MACRONUTRIENT INTAKE ACCORDING TO INCOME CATEGORIES IN THE CITY SAMPLE, DIFFERENT NUMBERS MEANING SIGNIFICANT DIFFERENCE IN THE SAME ROW ...... 78 TABLE 4.21: MACRONUTRIENT INTAKE ACCORDING TO AGE CATEGORY (% OF WOMEN) ...... 79 1,2 TABLE 4.22: MACRONUTRIENT INTAKE ACCORDING TO AGE CATEGORY IN THE CITY AND VILLAGE SAMPLE, DIFFERENT NUMBERS MEANING SIGNIFICANT DIFFERENCE IN THE SAME ROW ...... 79 TABLE 4.23: MACRONUTRIENT INTAKE ACCORDING TO EDUCATION LEVEL (% OF WOMEN) ...... 80 1,2 TABLE 4.24: MACRONUTRIENT INTAKE ACCORDING TO EDUCATION LEVEL IN THE CITY SAMPLE, SIGNIFICANT DIFFERENCE IN THE SAME ROW...... 80 TABLE 4.25: T-TEST, Α=0.05, TO COMPARE THE ACTUAL AND RECOMMENDED ENERGY INTAKE, * SIGNIFICANT DIFFERENCE BETWEEN THE ACTUAL AND RECOMMENDED ENERGY INTAKE ...... 81 TABLE 4.26: RECOMMENDED MICRONUTRIENT INTAKES FOR NON-PREGNANT AND NON-BREASTFEEDING WOMEN (FAO/WHO, 2004) ...... 82 TABLE 4.27: COMPARISON OF CONSUMPTION FREQUENCY OF MOST IMPORTANT FOODS IN THE CITY SAMPLE AND VILLAGE SAMPLE (T-TEST, Α=0.05), WEIGHT (G) AND PRICE (CF) FOR 100 KCAL OF THE MOST IMPORTANT 1 FOODS WITH CF=CONGOLESE FRANK, THE PRICE FOR 100 KCAL OF GREEN LEAFY VEGETABLES SHOULD BE DIVIDED BY TWO FOR THE VILLAGE SAMPLE., * SIGNIFICANT DIFFERENCE IN CONSUMPTION FREQUENCY BETWEEN BOTH SAMPLES ...... 84 TABLE 4.28: THE RNI, PERCENTAGE OF WOMEN WITH INADEQUATE FAT-SOLUBLE VITAMINS INTAKE, THE USUAL INTAKE AND NAR;*SIGNIFICANT DIFFERENCE IN USUAL INTAKE AND NAR BETWEEN THE CITY AND VILLAGE SAMPLE ...... 87 TABLE 4.29: THE RNI, PERCENTAGE OF WOMEN WITH INADEQUATE WATER-SOLUBLE VITAMINS INTAKE, THE USUAL INTAKE AND NAR; *SIGNIFICANT DIFFERENCE IN USUAL INTAKE AND NAR BETWEEN THE CITY AND VILLAGE SAMPLE ...... 90 TABLE 4.30: THE RNI, PERCENTAGE OF WOMEN WITH INADEQUATE MINERAL INTAKE, THE USUAL INTAKE AND NAR ;*SIGNIFICANT DIFFERENCE IN USUAL INTAKE AND NAR BETWEEN THE CITY AND VILLAGE SAMPLE .. 92 TABLE 4.31: ESTIMATED PERCENTAGE BIOAVAILABILITY OF NONHEME IRON FOR IRON-DEFICIENT, NONANEMIC PERSONS WITH DIFFERING INTAKES OF MEAT, FISH AND POULTRY PROTEIN (G) AND ASCORBIC ACID (MG) PER 1000 KCAL (GIBSON AND FERGUSON, 1999)...... 93 TABLE 4.32: MEAN ASCORBIC ACID (MG), MEAT, FISH AND POULTRY PROTEIN (G) INTAKE PER 1000 KCAL ...... 93 TABLE 4.33: THE RNI, PERCENTAGE OF WOMEN WITH INADEQUATE IRON AND ZINC INTAKE, THE USUAL INTAKE AND NAR (*SIGNIFICANT DIFFERENCE IN USUAL INTAKE AND NAR BETWEEN THE CITY AND VILLAGE SAMPLE) ...... 94
vi List of tables List of figures
FIGURE 1.1 DEMOCRATIC REPUBLIC OF CONGO (MINISTÈRE DU PLAN, 2008) ...... 7 FIGURE 1.2: THE DIVISION IN 25 PROVINCES AND KINSHASA BUT TILL TODAY NOT EXECUTED (DE SAINT MOULIN, 2005) ...... 8 FIGURE 1.3: THE ORIENTAL PROVINCE DIVIDED IN DISTRICTS AND TERRITORIES (DE SAINT MOULIN, 2005) ...... 10 FIGURE 2.1: GHI WINNERS AND LOSERS FROM 1990 GHI TO 2009 GHI (GREBMER ET AL., 2009) ...... 32 FIGURE 2.2: SUPPLY OF KCAL/CAPITA/DAY IN DRCONGO FROM 1995 TILL 2005 (FAO, 2005) MADE BY AUTHOR 35 FIGURE 2.3: SUPPLY OF THE PERCENTAGE OF KCAL PER CAPITA PER DAY IN DRCONGO IN 2005 (FAO, 2005), MADE BY AUTHOR ...... 37 FIGURE 3.1: FROM LEFT TO RIGHT: SMALL, MEDIUM AND LARGE PORTION OF BEANS IN OUR PHOTO BOOK (SOURCE: OWN RESEARCH, 2009) ...... 43 FIGURE 3.2: ASSUMED DISTRIBUTION OF ASSETS AND SERVICES (RUTSTEIN AND JOHNSON, 2004) ...... 48 FIGURE 4.1: MACRONUTRIENT ENERGY CONTRIBUTION(%) IN THE CITY SAMPLE (LEFT) AND THE VILLAGE SAMPLE (RIGHT) ...... 69 FIGURE 4.2: ENERGY CONTRIBUTION (%) OF DIFFERENT FOODS IN THE CITY SAMPLE (LEFT) AND THE VILLAGE SAMPLE (RIGHT), WITH F,M,P, E BEING FISH, MEAT, POULTRY, EGGS ...... 70 FIGURE 4.3: CARBOHYDRATE CONTRIBUTION OF DIFFERENT FOODS IN THE CITY SAMPLE (LEFT) AND THE VILLAGE SAMPLE (RIGHT) ...... 70 FIGURE 4.4: FAT CONTRIBUTION OF DIFFERENT FOODS IN THE CITY SAMPLE (LEFT) AND THE VILLAGE SAMPLE (RIGHT) ...... 71 FIGURE 4.5: PROTEIN CONTRIBUTION OF DIFFERENT FOODS IN THE CITY SAMPLE (LEFT) AND THE VILLAGE SAMPLE (RIGHT) ...... 71
vii List of figures List of abbreviations
ABAKO Alliance des Bakongos AFDL Alliance des Forces Démocratiques pour la Libération du Congo-Zaïre BMR Basic Metabolic Rate CNDP Congrès Nationale pour la Défense du Peuple CSI Coping Strategies Index DFID Department For International Development DHS Demographic and Health Survey DRCongo Democratic Republic of Congo EDS-RDC Enquête Démographique et de Santé en République Démocratique du Congo FARDC Forces Armées de la République Démocratique du Congo FDLR Forces Démocratiques pour la Libération du Rwanda FPR Front Patriotique du Rwanda GHI Global Hunger Index HDDS Household Dietary Diversity Score HFIAP Household Food Insecurity Access Prevalence HFIAS Household Food Insecurity Access Scale IFAD International Fund for Agricultural Development MICS Multiple Indicator Cluster Surveys MNC Mouvement National Congolais MONUC United Nations Organization Mission in Congo MPR Mouvement Populaire Révolutionnaire MSM Mulitple Source Method NAR Nutrient Adequacy Ratio NEPAD New Partnership for Africa’s Development PAL Physical Activity Level PARSS Projet d’Appui à la Rehabilitation du Secteur de la Santé PNMLS Programme National Multisectoriel de Lutte contre le SIDA RCD Rassemblement Congolais pour la Démocratie RNI Reference Nutrient Intake UNFPA United Nations Population Fund UNICEF United Nations International Children’s Emergency Fund UPPS Union pour la Démocratie et le Progrès Social USAID United States Agency for International Development WFP World Food Programme
viii List of abbreviations INTRODUCTION
Knowledge in Africa on wild edible plants (WEPs) has been developed during centuries of trial and error, and transmitted over generations (Malaisse and Parent, 1985), but is now more and more declining and even disappearing with increasing contact with modernization and western lifestyles (Keller et al., 2006; Lykke et al., 2002; Maundu, 1997; Ogoye-Ndegwa and Aagaard-Hansen, 2003).
As a consequence, formerly well-balanced diets are disturbed when traditional products are replaced by imported or newly introduced species, whereby deficiencies in nutrients may develop (Herzog et al., 1994; Lykke et al., 2002; Weinberger and Swai, 2006). Documenting and revalorizing indigenous knowledge on WEPs is thus needed to maintain and promote nutritional health as well as to preserve genetic and cultural diversity (Terashima and Ichakawa, 2003).
To (re)valorize the traditional knowledge on WEPs, University of Ghent carried out a WEP-project in the District Tshopo in the Democratic Republic of Congo (DRCongo) in collaboration with the University of Kisangani, namely “Nutritional, socio-economic and cultural importance of Wild Edible Plants in the region of Kisangani”. The objectives of the project were to1) inventory all WEPs known and used in the District; 2) analyze their nutritional value; and 3) study their socio-economic and cultural importance, with the overall aim to construct a priority list of species for participatory domestication and further research (Termote et al., 2010). The research is executed in a participatory way to take into account local people’s opinions in the choice of species for domestication in addition to nutritional and commercial characteristics of the species.
The present study is part of objective 3) of the WEPs project in the Tshopo District and evaluates the diet and food patterns of women in the city (Kisangani) and in a rural village (Yaoseko) as well as the importance of WEPs herein. Before starting to promote WEP consumption and cultivation to improve food security in the region, it is necessary to analyze and evaluate the current dietary patterns. This can be done by comparing the actual nutrient intakes with the recommended amounts by the WHO/FAO for the region. In this way, we may especially look to promote those WEPs rich in nutrients that are lacking or present in too small quantities in the actual diets.
The main objectives of our research are:
1) to evaluate the diets of the Turumbu women in Kisangani (the capital city of the Eastern Province) and Yaoseko (a rural village at 34 km of Kisangani). The Turumbu is one of the major ethnic groups in the district. 2) to analyze the food security situation of the Turumbu women in Kisangani and Yaoseko. 3) to search for relations between socio-demographic and food security parameters on the one hand and energy and macronutrient intake as calculated from a 24-hour recall, on the other hand. 4) to compare the Turumbu women in Kisangani and Yaoseko, for all of the above mentioned parameters
1 INTRODUCTION This brief introduction is followed by a description of the history, geography, national resources, demography and economy of DRCongo in chapter 1.
The literature review in chapter 2 starts with a brief explanation about food security and describes further the different methods to measure food security and food consumption. The chapter ends with an overview of the actual food security and nutrition situation in DRCongo, although reliable statistical data are hard to find for DRCongo. Among others, the FAO food balance sheets for DRCongo are analyzed and an overview of nutritional outcomes of past nutritional research in DRCongo is given.
The research methodology is explained in chapter 3. In the first paragraph, the data collection methods are presented. This is followed by a description of the questionnaire used and the way data were analyzed.
The results are processed and discussed in chapter 4. A brief presentation of some socio-demographic parameters of our research sample is followed by an evaluation of the different food security parameters. Then, the mean daily energy, mean daily macronutrient and usual micronutrient intake are evaluated. Finally, we searched for relations between the socio-demographic and food security parameters on the one hand and energy and macronutrient intake on the other hand.
2 INTRODUCTION 1.PRESENTATION OF CONGO 1.1 History of Congo 1.1.1 Congo before 1884
Before the colonization, in the 13th century, Congo was characterized by big kingdoms. From the 16th till the 19th century, Congo was victim of slave trade, in three centuries time 4 million slaves were shipped out of Congo along the Congo River (Canvas, 2010). Congo was explored at first by Europeans in the 19th century. The most important expedition was the one from Henry Morton Stanley, an English explorer who was sent to Congo by king Leopold II. Stanley sailed on the Lualaba river and discovered that this river formed the upper stream of the Congo river.
1.1.2 Congo Free State (1884-1907)
The proclamation of Congo Free State, with Leopold II as sovereign, took place on the conference of Berlin (1884-1885). Since then, the territory was considered as a free trade area, where commercial collaboration with other countries should have been possible (GNLE, 1979). Because the Belgian government did not want any financial responsibility, Leopold II considered the international free trade area more as a private company. But the big investments of Leopold II in Congo, took him a fortune and to settle his debts, he started asking taxes. As a result, the freedom of trade was gradually abolished and a monopoly of rubber and ivory was built up with an intensive exploitation and a poor situation of the laborers. Under high pressure, Leopold II abandoned “his” Congo on the 28th November of 1907 (Canvas, 2010).
1.1.3 Belgian Congo (1907-1960)
In 1908, Congo Free State became an official Belgian colony, from now on known as Belgian Congo (GNLE, 1979). From this time on, forced labor and molestation were forbidden. From 1908 till 1913, many Belgians moved to Congo: there were some four thousand Belgian colonists in Congo (Canvas, 2010).
After the First World war, there was a big demand for tin, zinc, copper, diamond and gold. This required many manpower and resulted again in forced labor.
The title “évolué” originated around 1950. This title was given to black people which could be compared with the Belgian on as many aspects as possible, the “évolué” got a registration card in the population register (Canvas, 2010). The second world war influenced the nationalism in Congo and other African countries. The
3 PRESENTATION OF CONGO ‘Mouvement National Congolais’ (MNC) was established under supervision of Patrice Lumumba, who was an ‘évolué’. Patrice Lumumba wanted Congo to become independent. The MNC as well as Joseph Kasavubu, the president of the ‘Alliance des Bakongos’ (ABAKO) strived for immediate independence. Both nationalists were presented at the round table conference in Brussels in January 1960 and a date was chosen to declare the independence of Congo. In meantime, the elections had to be organized. King Boudewijn declared the independence of Congo on the 30th of June 1960 (GNLE, 1979).
1.1.4 The First Republic (1960-1965)
The elections were won by Patrice Lumumba and Joseph Kasavubu, who were the prime minister and president of the first federal government, respectively. But Congo was not prepared for the independence. Soon after the independence the administration collapsed and violence and war arose all over the country. The Congolese army, the “force publique”, which was still under supervision of the Belgians began to rob, rape etc. Lumumba tried to calm them, but did not succeed. Meanwhile Moise Tschombe took advantage of the unstable situation and tried to separate Katanga from Congo, which is together with Kasaï, the richest province of the country (Canvas, 2010).
On the 14th of June 1960 the UN tried to bring peace in Congo, but could not solve the problems in Kasaï and Katanga. Lumumba personally went to New York to find a solution for this problem, but the journey did not give any positive results. Finally, he decided to send the Congolese army to Kasaï and Katanga. Thousands of people were killed and Lumumba was held responsible (Canvas, 2010). On the 5th of September 1960, Lumumba got dismissed by president Kasavubu.
On the 14th of September 1960, colonel Joseph-Désiré Mobutu got in charge, but left the presidency to Kasavubu. Mobutu handed over Lumumba to his enemies in Katanga on the 17th of January in 1961, where he was killed the same day (GNLE, 1979). Tschombe lost his enemy, but the VN sent military forces to the battlefield in Katanga and won (Canvas, 2010). During the next four years, different governments took the leading position but none of them could handle the situation.
1.1.5 The Second Republic (1965-1997)
On the 24th of November 1965, Mobutu dismissed Kasavubu with help from the United States and he introduced a new constitution and presidential system with all power belonging to the president. In 1967 Mobutu set up the one-party-state, the ‘Mouvement Populaire Révolutionnaire’ (MPR) for the next 32 years (GNLE, 1979). In 1970, the president was elected for the first time with universal suffrage. At this moment there was peace and money, in short, Congo was a proud and free country (Canvas, 2010). The policy pursued by Mobutu had a nationalistic scope. To enforce this, Mobutu changed in October 1971 the name of the country in Zaire and he nationalized all foreign companies. The small companies led by foreigners were obliged to get a Zairian as chief. Mobutu became a real dictator. The Mubutism became official with the MPR as a new church and Mobutu the prophet in 1974 and 1975 (GNLE, 1979).
4 PRESENTATION OF CONGO The initiatives of Mobutu resulted in a period of relative peace and stability, but from 1976 on, several complications appeared and the Mobutu-regime was not as stable as thought. Repeated allegations towards the policy of Mobutu were human right violations and corruption. However, he received support of several countries to remain in power because of his western-minded policy and the importance of Zaire as raw material supplier (GNLE, 1979).
With the establishment of the ‘Union pour la Démocratie et le Progrès Social’ (UDPS) as second party in the one-party-state, Mobutu got furious and felt obligated to introduce the democracy in 1985. In 1991, the Congolese army revolted and destroyed everything from the Zairian alienation (Canvas, 2010).
1.1.6 Conflicts (1997-2006)
To understand the situation starting from the year 1997, a brief explanation of the political background in Rwanda and Uganda in the nineties is required (Berwouts, 2001). In 1990 the ‘Front Patriotique du Rwanda’ (FPR) from Uganda invaded Rwanda. The FPR conquered many parts of Rwanda and many Tutsi’s joined the FPR. The firing of the airplane of president Habiyarimana on the 6th April 1994 was the start of many murders. The radical Hutu-militia, “the Interahamwe” and the army of the government killed a million of Tutsi’s. The FPR reacted by conquering Kigali. As a result, 2 million Hutu’s fled to refugee camps in the Zairian province Kivu. They formed a heavy pressure on the standard of living of the local population in Kivu. The authorities of Kivu decided on the 7th of October 1996 that the Zairian Tutsi’s had to leave the country, but the Zairian Tutsi’s revolted and got support from the Rwandan army led by Paul Kagame. They shot the camps of the Hutu-rebels and the rebels fled deeper into Congo.
At this moment the ‘Alliance des Forces Démocratiques pour la Libération du Congo-Zaire’ (AFDL) appeared. These were mainly Rwandan rebels but the raid was disguised as a Congolese revolution under supervision of Laurent-Désiré Kabila (Canvas, 2010). It was the AFDL that expelled Mobutu in 1997. The rebels of the AFDL were supported by Rwanda and Uganda. On the 18th of May 1997, the rebels arrived in the capital city, where they were seen as liberators. Laurent-Désiré Kabila exclaimed himself as president of the country two days later and changed the name of the country into ‘The Democratic Republic of Congo’.
Laurent-Désiré Kabila did not seem to be a good president, he lost many credit from the UN because of his neglect towards human rights. The relations with Rwanda and Uganda also got worse. He dismissed Rwandan soldiers, whereupon Rwanda and Uganda re-invaded Congo on the 2nd of August 1998 and now they wanted to expel president Laurent-Désiré Kabila. As in 1996, a new rebel movement appeared: the ‘Rassemblement Congolais pour la Démocratie’ (RCD), which includes ex-Mubitists and unsatisfied AFDL-members. The movement was further supported by Rwanda and Uganda.
During a failed coup on the 16th of January 2001, Laurent-Désiré Kabila was shot and his son, Joseph Kabila, followed him up. In 2003, the Congolese government, the rebel movements and the inland opposition movements agreed to form a transition government for a period of two years presided by
5 PRESENTATION OF CONGO Joseph Kabila and supported by four vice-presidents. The main tasks of this government were to prepare democratic elections in 2005 and to repair the security in the country (Wilbo, 2005). The elections finally took place in 2006 and Joseph Kabila was elected in two rounds as legitimate president of the Democratic Republic of Congo (DRCongo).
1.1.7 The Third Republic (2007-…)
Till the end of 2008, the Congolese army fought together with the ‘Forces Démocratiques pour la Libération du Rwanda’ (FDLR) against ‘the men of Rwanda’ (which could be the RCD or the ‘Congrès Nationale pour la Défense du Peuple’(CNDP)). The Hutu FDLR rebel group from Eastern Congo descended from those groups that carried out the 1994 Rwanda genocide. The FDLR was integrated in the local population: they had shops, went to the market, their children went to school, they got health care. Briefly, a modus vivendi arose between the local population and the FDLR. But this only lasted till 2009 (Vandaele, 2010).
In 2009, an operation called “Kimia II” (Kimia means peace in Swahili and in Lingala) arose. This was a joint Congo-Rwanda military offensive against the Hutu FDLR rebel group. The presidents of Congo and Rwanda, Joseph Kabila and Paul Kagame , suddenly made peace. In exchange for imprisoning Laurent Nkunda (leader of the CNDP) in Rwanda, who set North Kivu on fire from time to time, Kabila promised to fight against the FDLR. So instead of everyone being against the CNDP, now everyone was against the FDLR. The CNDP, with lots of Tutsi’s in leading positions, got integrated in the Congolese army, the ‘Forces Armées de la République Democratique du Congo’ (FARDC). So the army just turned around and started shooting in the opposite direction. The purpose of the military actions was to eliminate as much militaries and refugees as possible or to make them return to Rwanda.
The FARDC is supported by the United Nations Organization Mission in Congo (MONUC) but are criticized because of their violent way of fighting against the rebels. The MONUC is also responsible for bringing the FDLR militaries back to Rwanda. But meanwhile the FDLR is still recruiting new people and this does not always happen in a gentle way. If the FDLR protects the Rwandan refugees or hostages them is not clear. The Rwandan refugees are still afraid of being killed by the Tutsi’s, they aren’t wrong because between the 27th and 30th of April, 129 Rwandan refugees were killed by the FARDS-unit led by CNDP people.
Nowadays, the sexual violence, murders and conflicts are still prevalent in East-Congo caused by foreign armed groups as well as the Congolese army. The challenge for Joseph Kabila is to make a global safety plan for East-Congo to end the violent actions of Congolese and foreign armed groups.
The history of Congo can be summarized as unrest, mistakes, failures, opposition and the continuous struggle for power. But the happiness of Congo is that they are a nation full of energy.
6 PRESENTATION OF CONGO 1.2 Geography
The biggest part of the country belongs to the African equatorial zone. The equatorial zone has an average daily temperature between 30 and 35°C, an annual well-distributed rainfall that amounts from 1500 to 2000 mm a year, and a vegetation characterized by tropical rainforest (GNLE, 1979).
DRCongo is situated in Central Africa between 5°20’ north till 13°28’ south and 12°10’ till 31°15’ east and has a total surface area of about 2 345 409 square kilometers. As shown in figure 1.1, DRCongo has a border of 10 292 km with nine countries, the Central African Republic and Sudan in the north, Uganda, Rwanda, Burundi and Tanzania in the east, Zambia and Angola in the south and the Republic of Congo and the Atlantic Ocean in the west (figure 1.1) (Ministère du plan, 2008).
Our research took place in the 6 municipalities of the city Kisangani and in the rural village Yaoseko, to situate this city and village, we start from country level. The Democratic Republic of Congo is divided in 10 provinces (Bas-Congo, Bandundu, Occidental- Kasaï, Oriental-Kasaï, Maniema, Katanga, South-Kivu, North-Kivu, Oriental Province, Equatorial Province) and 1 city province (Kinshasa) (see figure 1.1).
Figure 1.1 Democratic Republic of Congo (Ministère du Plan, 2008)
This division should have been replaced by 25 provinces and Kinshasa (see figure 1.2) in February 2009 (Kabila, 2006), but till now nothing has changed.
7 PRESENTATION OF CONGO The Oriental Province (bordered by a red line in figure 1.2) is divided in 4 districts, Tshopo District, Bas-Uélé District, Haut-Uélé District and Ituri District. Kisangani and Yaoseko our both situated in the Tshopo District, in the Oriental province.
Figure 1.2: The division in 25 provinces and Kinshasa but till today not executed (De Saint Moulin, 2005)
The Tshopo District (bordered by a green line in figure 1.3) includes 7 territories, Bafwasende, Opala, Yahuma, Isangi, Banalia, Ubundu and Basoko.
The Isangi territory comprises 13 ‘Collectivités,’ operational units at the basis of the hierarchical administration system. The nature territory of the Turumbu is the ‘Collectivité Turumbu’ (colored red in figure 1.3). Yaoseko, a rural village at 34 km west of Kisangani, on the road to Yangambi at 00°35’03’’ north 24°56’14’’east, where a part of our research was done, is situated in this collectivité.
The ‘Collectivité’ Turumbu is bordered in the north by the Banalia Territory and Aruwimi river, in the east by the Lindi river, in the south by the Congo river and in the west by the Basoko Territory. Data on surface area or population numbers of the ‘Collectivité’ are contradictory: 4600 km² with 40,421 inhabitants on the 1st of July 2004 according to the National Institute of Statistics (Ministère du Plan, 2005) or 3674 km² with 61,905 inhabitants in 2007 according to the Isangi Territory Report (2008).
8 PRESENTATION OF CONGO The Turumbu belong together with the Lokele, Wagenia, Walengola, Kumu, Topoke, Bamanga and Bambole to the most important ethnic groups in Kisangani. The different ethnic groups use the forest and other national resources each in their own way. The Lokele and Wagenia live on the rivers and their principal activity is fishing. The Bamanga and Topoke are rather agriculturists.
The Turumbu and their culture have hardly been studied. Their principal activity is agriculture complemented by hunting, fishing, gathering and cattle-raising. The agricultural products can be transported and sold on the markets of Kisangani thanks to their geographical position close to the Congo River and Kisangani. Agriculture is important for their own subsistence but also for the supply of Kisangani (Wengu Chombe, 1997). Today however, the agricultural production and food security strongly decreased in comparison with the colonial period. This is among other due to the bad organization of the agricultural activities. As a result the Turumbu are obliged to eat WEPs to complement their diet. Hunting and fishing still occurs with traditional methods like nets and traps.
Traditionally, the huts of the Turumbu are constructed using clay and leaves. Only 8% of houses are constructed in durable materials (bricks and corrugated iron). The Turumbu are also specialized in handicrafts such as vans, baskets, mortars and canoes (Kienia’h Bikitwa, 1999). The most important energy source for the population is firewood. Due to the high population growth rate, this results in a fast degradation and overexploitation of the forest.
The other part of the survey was done in Kisangani. Kisangani is a the capital city of the Eastern Province and is situated at 0°31’ north and 25°11’ east, with an altitude of 428 m and borders Banalia and Bafwasende in the north, Ubundu in the south and Isangi in the west (colored with a blue color in figure 1.3). It is well located on the banks of the Congo stream and the river Tshopo and is the final place for boat transports from Kinshasa. Kisangani also has a railway that connects Kisangani to Ubundu, an international airport in Bangboka and a military airport in Simisimi. Finally Kisangani is an important gateway to East-Congo. Kisangani is divided into 6 municipalities: Makiso, Tshopo, Mangobo, Kabondo, Kisangani and Lubunga. The central market, the City Hall, NGO’s and international organizations are located in Makiso. That’s why Makiso is the most important municipality.
Originally the population in Kisangani was composed of two ethnicities: Wagenia and Kumu. Today, there’s a very heterogeneous population with more than 250 ethnicities. Besides French, they use Lingala and Kiswahili as the main languages(UNDP, 1998).
The road infrastructure in the city centre of Kisangani is bad because of poor maintenance. For transport, people can use canoes because a lot of rivers surround and cross the city. But the main transport medium in Kisangani is the taxi bicycle or ‘toleka’. Only international co-operations and the richer inhabitants have a motorized transport medium (UNDP, 1998).
9 PRESENTATION OF CONGO
Figure 1.3: The Oriental Province divided in districts and territories (De Saint Moulin, 2005)
The total surface area, the inhabitants and the population density of the Oriental Province, the Tshopo District, the Isangi territory, the Turumbu collectivité and Kisangani are presented in table 1.1.
Surface area (km2) Inhabitants Population density (per km2) Oriental Province 503 2391 5 484 3001 111 Tshopo District 197 6571 1 029 6161 51 Isangi territory 15 7701 312 1701 201 Turumbu collectivité 46002 404212 92 Turumbu collectivité 36743 619053 173 Kisangani 1 9101 406 4001 2131 Table 1.1: Surface area (km2), inhabitants and population density (people per km2) in the Oriental Province, Tshopo District, Isangi territory, Turumbu collectivité and Kisangani (1UNDP, 1998 based on the services of agricultural statistics, 1994, 2 Ministère du Plan, 2005, 3Isangi Territory Report 2008) 1.3 Natural resources
Worldwide DRCongo is one of the richest countries in natural resources with a very big potential in the mine and forestry sector. However, years of war and the corrupt regime of Mobutu had a negative impact on the economy, infrastructure and social situation (BAfD/OCDE, 2005). The once thriving economy with good prospects has fallen back to an economy based on subsistence agriculture and informal activities (BAfD/OCDE, 2005; World Bank, 2007).
DRCongo has 134,7 million ha of fertile soils, but only ten percent is valorized due to a lack of infrastructure and capital (UNICEF, 2001). Agriculture is responsible for almost half of the GNP because of the lower productivity in the mine sector and other industries during conflicts. The food production increases slower than the population growth, meaning that agricultural yields do not satisfy the needs (IMF, 2007; BAfD/OCDE, 2005).
10 PRESENTATION OF CONGO The soil contains numerous minerals. Copper, diamond and gold are the most important for the mine sector.
The total surface of forest in DRCongo is 1.34 million square kilometers, 58.9% of the surface area of the country or 6% of the total world surface of forest (IFAD, 2003; UNDP, 1998). About 70% of the Congolese population directly depends on the forest for food, medicines, wood for construction and fire wood (CIFOR, 2007). The yearly deforestation rate in DRCongo between 1984 and 1998 is estimated to be 0.4% (Laporte and Justice, 2004) which is lower compared to other tropical developing countries and is proportional to the population growth. Political instability and the poor infrastructure decrease the large scale deforestation (CIFOR, 2007).
DRCongo ranks fifth on the world list of animal and plant diversity and 8% of the land surface is protected for biodiversity (CIFOR, 2007).
1.4 Demography
According to the World Bank, the total population of DRCongo in 2008 was 64.3 million people with an annual population growth of 2.7% (World Bank, 2008). The population is characterized by its youth, almost 50% of the population is younger than 15 and less than 5% is older than 60. The life expectancy at birth is 47.6 years, the mortality rate is 125.8 per 1000 live births and the literacy rate of female between 15 and 24 is 61.8% (World Bank, 2008).
As regards the ethnic composition of DRCongo, there are 40 ethnic groups that can be categorized in four major groups, the Bantus, the Nilotics, the Sudans and the Pygmees (Ministère du plan, 2008).
The official language in DRCongo is French. Besides the official language, there are 200 local languages. Lingala (spoken in Kinshasa and north-west), Kikongo (spoken in the west), Tshiluba (spoken in the central south) and Kiswahili (spoken in the east) are four general Bantoe languages which are recognized as national languages (GNLE, 1979).
Concerning the religion in DRCongo at the moment of independence, 50% was animist, 35% Catholic, 10% Protestant and 5% adepts of Simon Kimbangue. The remaining part was Muslim, ‘Branhamiste’, member of a religious sect or atheist (GNLE, 1979).
With regard to the education, half of the children went to primary school and 16% of the girls and 28% of the boys went to secondary school in 1991. The increasing absenteeism causes an increasing illiteracy. Only 62.7% of the population older than 15 could read and write between 1995-2005 (UNDP, 2008).
1.5 Economy
DRCongo is one of the poorest countries in the world. In the public sector, the minimum salary is 15 US$ a month and in the private sector around 50 US$ a month. The GDP in 2008 was 11.67 billion US
11 PRESENTATION OF CONGO dollars, agriculture contributed for 40%, industry for 28% and services for 32% (World Bank, 2008).
At the present stage of economic development of the DRCongo, it is the agricultural sector that offers the best perspectives for a supported growth from which large segments of the population can benefit. Because the agricultural sector is the only sector that can put many people at work and generate incomes and value added. Also the agricultural sector reaches the poorest segments of the population and can hence enhance equity. Compared to other countries in Africa, none of them have as many agricultural potential and only a few means are required to restart the agricultural sector and to contribute significantly to an economic growth: free movement of goods and people, transport infrastructure, quality seeds etc. (Tollens, 2003). DRCongo has an enormous agricultural potential and can become a bread basket of Africa. The commitment of the heads of State in the New Partnership for Africa’s Development (NEPAD) is to spend 10% of all public outlays on agriculture and rural development in the five years to come to support the food security (ECAPAP, 2003; Eicher, 2003) and to arrive at an annual agricultural growth rate of 6% by 2015 (NEPAD, 2003). This is an African commitment, if it is not respected by the African countries AND donors, the agricultural situation will without a doubt not change fundamentally. In 2003 the part of agriculture in the national budget was 1.44%.
The more conflicts there are, the lower the employment and unemployment is the mayor reason for the disastrous social situation. According to the International Fund for Agricultural Development (2003), 80% of the Congolese population lived in extreme poverty in 2001 and the regional differences were huge. The population earned on average 0.09 US$ per day per person in the east of Congo, 0.38 US$ in the south of Congo, while people in the capital city Kinshasa earned 0.88 US$ per day per person. The most important reasons for this are the war, the isolation of many parts of the country, the low agricultural productivity, the lack of trade possibilities and the malfunctioning of social services (IFAD, 2003). Estimated numbers of women and men without any income were respectively 44% and 22% and in 2000, only 4 % of the population had an official profession. This indicates that the informal sector is the dominant sector in the Congolese economy (BAfD/OCDE, 2005).
The lower the employment, the lower the income and the less money is available for medical care, so the worse the health parameters. In 2004, the government only spend 1,1 % of the GNP to health care while 2.4% was used for military purposes (UNDP, 2008).
The less money they have, the less they can consume. In 2002-2004, 74% of the population was undernourished, while this was only 31% in 1990-1992. According to IFAD (2003) and UNDP (2008), 16 million people, especially women, refugees, orphans, uneducated children and child soldiers have problems to find sufficient food on a daily basis.
Since 2001 there’s an increase of external economic help. Even if considerable amounts are given, it will still be insufficient because of the huge surface and challenges with which the country is faced (World Bank, 2007). A future economic growth is only possible when the Congolese government can maintain a political stability, improve the policy, reduce corruption and perform structural reformations (BAfD/OCDE, 2005).
12 PRESENTATION OF CONGO 2.LITERATURE REVIEW
The literature chapter contains two parts. First, the definition of food security and the different methods of measuring the food access component of food security are discussed. The food security and nutrition situation in DRCongo are discussed in the second part.
2.1 Food security
The different components of food security and how each of them can be measured are explained in this chapter with emphasis for the “access” component of food security. Furthermore, the causes, consequences and levels of food security are reviewed.
2.1.1 Definitions
Food security has been defined over time in several ways. The United States Agency for International Development (USAID, 1992) defines food security as the state in which ‘all people at all times have both physical and economic access to sufficient food to meet their dietary needs for a productive and healthy life’. As defined by USAID, food security has three components – availability, access and utilization, which will be discussed here.
Besides food security, nutrition security has two other non-food determinants (Smith et al., 2000). The first is care or the provision in the household and the community of time and attention to satisfy the physical, mental and social needs of the growing child and other household members. The second determinant is health. A bad health or disease has a negative influence on nutrition security by a decreased appetite, inhibited absorption of nutrients together with an increased need for energy and other nutrients, resulting in less energy and nutrients available for growth and basal metabolism (Ramalingaswami et al., 1996). So food security is a necessary but insufficient condition for nutrition security of an individual (Schonfeldt, 2002).
2.1.1.1 Availability
Food security means that sufficient food for an active and healthy life is available to nourish everyone at a certain level (world, regional, country, province, household or individual). But what is sufficient? It concerns both quantity and quality. Quantity reflects a sufficient energy intake in kilocalories (kcal), supposing that a sufficient energy intake automatically leads to sufficient intake of proteins, fat, carbohydrates and micronutrients. Quality of food depends on the nutrient composition and the way of combining and preparing foods (Savage King and Burgess, 1993).
Availability is a necessary but not sufficient condition for food security. On a certain level, for example: on country level, there may be sufficient food (in both quantity and quality) to nourish everyone, for example: all the individuals in the country, but this does not automatically mean that
13 LITERATURE REVIEW everyone has sufficient food.
2.1.1.2 Access
Household food access is defined as the ability to acquire sufficient quality and quantity of food to meet all household members’ nutritional requirements for productive lives (Swindale and Bilinsky, 2006).
The aspect of food access dates from the eighties and is based on the entitlement theory of Amartya Sen (1981). Sen explains the entitlement of an individual as an initial set of resources that are transformed in food or things that can be exchanged into food via production and trade. When this entitlement does not result in enough food, the individual suffers from hunger; according to Sen, the entitlement of the individual fails.
The entitlement of individuals in a market economy is determined by what they possess, produce, trade, inherit or what is given to them. Examples of entitlement are land, labor, capital and other sources (Sutherland et al., 1999).
Although food availability is a necessary condition in this theory, it is not a sufficient condition for enough entitlement.
2.1.1.3 Utilization
Utilization is the individual’s biological capacity to make use of food for a productive life (Swindale and Bilinsky, 2006). A good utilization of food equals a diet with sufficient energy and essential nutrients, potable water and an adequate sanitary provision. It depends on the knowledge of the household about stocking and preparing food, about basic principles concerning food and child care and taking care of ill people (Riely et al., 1999). The health condition plays an important role in the use of essential nutrients.
2.1.2 Measurement of food security
Measures of household food security are needed for many different applications in situations where households are chronically vulnerable due to deepening poverty, environmental and climatic shocks, rapid economic change and conflict. Indicators may be used to predict crises (early warning), to understand shortfalls in access to adequate food (assessment), to allocate resources (targeting) or to track the impact of interventions (monitoring and evaluation) (Maxwell et al., 2008).
Yearly, food availability is influenced by rainfall, soil fertility, agricultural productivity, import and export. Problems of availability may, amongst other, be due to dependence of agricultural production on climate or the depletion of natural resources. Measurements of food availability can be found in the food balance sheets, which are published on a yearly basis by the FAO per country or region.
14 LITERATURE REVIEW Traditional household level measures of food access have relied on proxy indicators such as food consumption (caloric intake), household income, productive assets, food storage and even under five nutritional status, each of which are presumed to be either determinants or consequences of a particular household’s level of food security (Webb et al., 2006). Because these measures have been technically difficult, data-intensive and costly to collect (Coates et al., 2007) a few other methods have been developed. Some traditional as well as newly developed measurement techniques for evaluating the food access component of food security will be discussed in detail in 2.1.6. The 24-hour recall for measuring the food consumption at household level can also be an indicator but it only reflects the current consumption status and does not capture other elements of food security. In addition, the methodology is far too time-consuming to be useful in early warning, assessment, targeting or monitoring, which are very time sensitive (Maxwell et al., 2008).
An effective nutrient use (utilization component) is expressed in the nutritional status. Nutritional status can be an indicator of food security status but it may equally reflect elements of health status, care practices, water quality, and other determinants of nutrition (Young and Jaspars, 2006). Measurement tools for the nutritional status are anthropometric indicators (Coates et al., 2007). Anthropometric indicators show the effects of food insecurity problems on the human body. Three anthropometric measurements are used to follow the growth of children: weight for age as a general appreciation of the nutritional status, height for age as a measure of stunting or chronic malnutrition and weight for length as a measure for wasting or acute malnutrition. This method relies on the hypothesis that well nourished children, with a normal weight at birth and a well nourished mother, will have the same growth curve during the first five years of their life. Another anthropometric indicator is the mid upper arm circumference. This indicator is used as a first screening tool for undernourishment according to a certain cut-off (Kolsteren, 2010). The body mass index is an indicator elaborated for adults which compares weight and length but can not be used for children, older people or pregnant women. An advantage of the anthropometric indicators is that malnutrition and the degree of severity can be measured on an individual level. A disadvantage of the method is that it does not give any information about the causes of malnutrition. The index is only weakly correlated to caloric intake. For example, an adult may have a low BMI but this may be caused by undernutrition, a lack of calories compared to physical activity or a bad health. Also the diet highly influences the anthropometric measurements. For example, vegetarian children have lower values for anthropometric measurements even if they have sufficient calories (European Commission, 2002).
2.1.3 Causes and vulnerable groups 2.1.3.1 Causes
A number of important causes of food or nutrition insecurity in developing countries are described by USAID in the document “Food Aid and Food Security”. Examples are chronic poverty, high population growth, decreasing food production per capita, bad infrastructure, ecological limitations, limited availability of arable land, unstable political situations, crop diseases and pests, insufficient water and sanitary provisions, inadequate food knowledge, civil wars, ethnic conflicts etc.
15 LITERATURE REVIEW There are different ways in which these factors can influence nutrition security of households and individuals. A high population growth for example, may negatively influence the nutrition security by lowering the amount of land per capita and as a consequence the food availability, or by degradation of the environment and reduced agricultural productivity or by influencing sanitary provisions and spreading of diseases which influence the labor productivity and income as well as the nutritional status (Riely et al., 1999).
Because the nutrition security problem in developing countries is very complex, multiple conceptual frameworks and causal models have been developed to identify the causes of food insecurity. These frameworks must permit to analyze the different mechanisms that negatively influence the food security of some population groups and to design data collection systems (Riely et al., 1999). These data collection systems are required to predict crises (early warning), to understand shortfalls in access to adequate food (assessment), to allocate resources (targeting) or to track the impact of interventions (monitoring and evaluation) (Maxwell et al., 2008).
2.1.3.2 Vulnerable groups
According to the FAO (1999) , the most vulnerable groups are migrants and their family, victims of conflicts and household members with low income and vulnerable livelihoods. It is very important to carefully detect these vulnerable groups, so that nutrition or food interventions can be targeted towards the most vulnerable.
2.1.4 Consequences
Insufficient nutrition and health can negatively influence different factors, for example; labor productivity, school results and cognitive development and can also have a negative impact on the environment and natural resources. A lot of food insecure and poor people already live in ecological vulnerable places. Trying to get food secure by unsuited practices or wrong land use can cause land degradation which makes them even more vulnerable.
Labor productivity can be negatively influenced directly or indirectly by too low food intakes. Indirectly because time and money is invested in taking care of ill family members which results in less money and time that can be invested in labor. Less labor time results in lower labor activity. Nutritional status and labor productivity are thus positively correlated. A lower labor productivity will on his turn result in a lower income, so having less money for buying enough food, which results again in a low food intake. In other words it’s a vicious circle.
People can also try to get food secure by migration to other places for work, income or food aid. If younger, more innovative, better educated male members are migrating this leads to a depletion of physical and skilled labor. Mostly they migrate to slums in the city, where they are again confronted with food security problems. Other results of male migration are a higher number of female headed households, a higher dependency ratio and changes on the labor market.
16 LITERATURE REVIEW 2.1.5 Levels
To summarize, food security can be studied on world, regional, country, province, household and individual level.
The possibility of the earth to produce enough food for the growing world population is an important question on world level.
On a regional level, the self sufficiency or dependency of import or aid from other regions is considered.
Food balance sheets can be used on country level. Food balance sheets are published by the FAO every year per country or per region and can be used to calculate food deficits or surpluses to determine the necessary food aid or imports. A food balance sheet is a balance between the amount of available food (production, import, export, supply changes) and the needs for processing, losses, seeds and use as animal feed and human consumption.
On province level, problems like transport difficulties can result in a decreased access to food even if the country has enough food to feed the population.
Enough food may be available at country or province level but because of economical, physical factors etc. households may not have access to it. Within a household, so on individual level, there may also be food distribution differences. Only when all individuals have sufficient access to food, the household may be considered food secure. Although there are intra-household inequities, most of the time food security is measured at the household level because households are the social institutions through which most individuals get access to food. Except in case of emergencies, the households are responsible for food distribution (Maxwell et al., 2003). However, for individually targeted interventions, such as supplementary feeding, household measures are not appropriate, that’s why measurements on individual level also need to be considered.
To conclude if a nation is food secure, there are 2 different possibilities. In a traditional way, the bigger unit (country, region) is considered to be food secure if this unit has access to sufficient food to feed the subcompartments (household). National food security defined in this traditional way, does not exclude that some people die from hunger. It is a necessary but insufficient condition for food security on household level. Another way is to start from the subcompartments, so only when every subcompartment is food secure, the bigger unit is considered food secure. National food security defined in this way means that every individual of the country has access to enough food on every moment (Christiaensens and Tollens, 1995).
17 LITERATURE REVIEW 2.1.6 Some measurement methods
To evaluate the food consumption of an individual, household or group of people, different methods exist. There are two basic categories of methods: the record methods and recall methods. The record methods collect information on the moment of intake while the recall methods record information about food intake in the past (Bingham et al., 1998). In the following paragraphs 7 different methods are discussed.
The record methods discussed are the weighed food intake method and diary method.
The other methods are recall methods which can be divided in short term or long term recall methods. The short term recall evaluates the consumption of 1 day in the past while the long term recall evaluates the consumption over a longer period in the past (Willet, 1990). The short term methods discussed are the 24-hour recall and Household dietary diversity score (HDDS). The other methods: Household Food Insecurity Access Scale (HFIAS), The Coping Strategies Index (CSI) and World Food Program’s (WFP) method are long term recall methods.
All methods can also be divided in three groups, not according to record or recall method, but according to the strategy used:
• methods based on coping strategies: the CSI and the HFIAS • methods based on dietary diversity: the HDDS and the WFPs method • methods based on food intake: the 24-hour recall, the weighed intake method and diary method
The first two groups of methods are relatively new methods while the third group of methods are already known for a longer period and most applied in research on food consumption.
Older methods used to evaluate the food security access component are household income or expenditure measurements. These methods are less accurate compared to methods concerning consumption, because having enough money or being able to buy enough food is not the same as taking up sufficient food of a good quality.
A general disadvantage of methods evaluating the consumption of only one day is that the period may be too short to get a correct image of the intake because of daily and seasonal variation of the human diet (Bingham et al., 1998) or because of being an unusual day (e.g. parties, funerals, illnesses, market days) (Gibson and Ferguson, 1999).
An advantage of methods evaluating the consumption of the previous day is that there is a more accurate collection of information because the individual can correctly remember the food consumed the day before (Thompson and Byers, 1994).
Methods that evaluate the consumption over a longer reference period in the past result in less accurate information due to imperfect recall (Swindale and Bilinsky, 2006).
18 LITERATURE REVIEW 2.1.6.1 Methods based on coping strategies
Coping strategies/behavior, are “the things that people do when they cannot access enough food” (Maxwell et al., 2003).
Coping strategies are easy to observe and collecting information on coping strategies is quicker, simpler and cheaper than on actual household food consumption levels. Hence, these methods are more appropriate to detect the most vulnerable in emergency situations than the classical consumption methods, which are too time-consuming and expensive (Maxwell et al., 2008). When the methods based on coping strategies are used, first, series of questions of how the households manage to cope with a shortfall in food for consumption need to be set up. Therefore, it is important to have knowledge of the local situation, for example: what are the possible coping strategies used, which coping strategies are rather normal or rather exceptional to use. Different methods were used to measure the coping behavior to see whether all the methods give the same result and which method has the best correlation with other indicators of food security, such as dietary intake measured with the 24-hour recall and socio-economic parameters.
A. Coping Strategies Index (CSI)
Concept
The CSI was developed based on a collaborative research project, implemented by WFP and CARE in Kenya, Uganda and Ghana and has now been used for early warning and food security monitoring and assessment in at least seven other African countries (Maxwell et al., 2003).
The CSI measures behavior. There are namely a number of fairly regular behavioral responses called coping strategies, that people use to manage household food shortages. So the more the people adopt coping strategies, the less food secure they are.
The CSI consists of a series of questions about how households can cope with a shortfall in food for consumption and results in a numeric score.
Maxwell (2003) proposed a set of 13 coping strategies that fall into four categories (table 2.1). Those that change dietary intake, those that increase the amount of food available at the household level, those that reduce the number of people to provide for and those that ration the food available to the household.
As a food security situation worsens, households are more likely to employ strategies that are less reversible, and therefore represent a more severe form of coping and greater food insecurity (Corbett, 1988; Devereux, 1993). Coping behaviors that are relatively easily reversible (such as eating less- preferred foods or reducing portion sizes) occur most frequently and are also perceived by households as being among the less severe. Those behaviors commonly perceived as more severe (skipping entire days or sending children to beg) occur at relatively low frequency but have high associated severity scores. As the more extreme coping behaviors occur at such low frequency, the severity score acts as a
19 LITERATURE REVIEW scaling factor, and does not greatly impact mean CSI values (Maxwell et al., 2008).
The CSI was developed as a context-specific indicator of food insecurity that counts up and weights coping behaviors at the household level. To have a context-specific list of coping strategies, the list as proposed by Maxwell (2003) should be adapted to the local circumstances and practices. This can be done by organizing focus group discussions, preferably with women, because they know more about household food consumption. Questions that are not relevant in the region/area should be deleted and other locally used strategies have to be added.
Important is that the questions reflect a broad opinion and that these are the strategies they use in times of scarcity and not in a normal way (Maxwell et al., 2003). The severity of the strategies also needs to be determined during this focus group discussions. Strategies are then grouped in four categories of roughly the same level of severity with the first and the last category being the least and most severe strategies respectively.
According to Maxwell et al. (1999), the CSI is significantly correlated with other indicators of food security, including dietary intake, per capita expenditure on food and various anthropometric measures.
The best person to ask about coping strategies is the person in charge of preparing and distributing food in the household. Which is typically the mother, the wife or household female head.
Group of coping strategy Coping strategies A. Dietary change 1. Rely on less preferred and less expensive foods? B. Increase short-term household food 2. Borrow food, or rely on help from a friend or relative? availability 3. Purchase food on credit? 4. Gather wild food, hunt, or harvest immature crops? 5. Consume seed stock held for next season? C. Decrease numbers of people 6. Send children to eat with neighbors? 7. Send household members to beg? D. Rationing strategies 8. Limit portion size at mealtimes? 9. Restrict consumption by adults in order for small children to eat? 10. Feed working members of HH at the expense of non-working members? 11. Ration the money you have and buy prepared food? 12. Reduce number of meals eaten in a day? 13. Skip entire days without eating? Table 2.1: Generic List of Coping Strategies (Maxwell et al., 2003)
Application
After the locally relevant coping strategies are identified and categorized in four categories of severity, the next step is to determine the frequency.
20 LITERATURE REVIEW Research has found that the best way to assess the frequency of the coping strategies is not to count the number of times a household has used them, but to ask a household respondent for a rough indication of the relative frequency of their use over the previous month. Precise recall is often difficult over a longer period, but asking for the relative frequency provides adequate information (Maxwell et al., 2003).
There are various ways that a relative frequency count can work, for example by asking roughly what proportion of the days of the previous month people had to rely on various coping strategies, ranging from never (0 times), once in a while (1 to 2 times), pretty often (3 to 10 times) to often ( more than 10 times). The questions always ask about some time period beginning from today and counting backwards (Maxwell et al., 2003).
Calculation of the CSI
To calculate the CSI, the relative frequency and the severity should be scored. For scoring the frequency, the mid-point of the range of days in each category is assigned as the value for the category (table 2.2). For scoring the severity, the results of the focus group severity ranking are used. That is, all the least severe strategies in category 1 are weighted 1, the next category 2, etc. and the most severe strategies in category 4 are weighted 4.
One should take care that both the values of the frequency and severity influence the CSI score in the same direction.
The CSI is then calculated by multiplying the relative frequency with the severity weight for each question and making the sum. The higher the CSI score, the more food insecure a household is (Maxwell et al., 2003).
Score according to mid-point value Relative frequency categories of the range of each category Never (0 times last month) 0 Once in a while (1-2 times last month) 1.5 Pretty often (3-10 times last month) 6.5 Often (more than 10 times last month) 20 Table 2.2: Example of assigning numeric values to relative frequency (Maxwell et. al, 2003)
Interpreting the CSI score
Although the absolute context-specific CSI score does not tell much, it can be used to compare different households. Households with a lower CSI score are more food secure than households with higher CSI scores (Maxwell et al., 2003). Monitoring changes in the CSI score indicates whether household food security status is declining or improving. The higher the score, the greater the coping, and hence the higher the level of food insecurity (Maxwell et al., 2008).
The CSI score has proven to be useful in measuring localized food insecurity, that’s why it’s often called the context-specific CSI but it has not been useful to compare the relative severity of different
21 LITERATURE REVIEW crises and has therefore not been particularly useful for geographic targeting or resource allocation (Maxwell et al., 2008). Maxwell et al. (2008) identified a sub-set of individual coping behaviors common to all surveys (in more or less the same order of frequency, and a relatively same severity) and suggested a “reduced” indicator based on these common behaviors. The reduced CSI reflects food insecurity nearly as well as the context-specific CSI. So the reduced CSI could be used to compare the types of food security across different contexts.
B. Household Food Insecurity Access Scale (HFIAS)
Concept
The HFIAS consists of a series of questions identified by the Food and Nutrition Technical Assistance Project (FANTA) and its partners and is used to distinguish the food secure from the food insecure households across different cultural contexts. The HFIAS is based on the idea that the experience of food insecurity causes predictable reactions and responses that can be captured and quantified through a survey and summarized in a scale.
The information generated by HFIAS can be used to assess the prevalence (e.g., for geographic targeting) as well as the changes (e.g. for monitoring and evaluation of the household food insecurity situation) of a population over time (Coates et al., 2007).
The questions are asked with a recall period of four weeks. First, the respondent is asked whether the condition in the question happened in the past four weeks (yes or no). If the respondent’s answer to the occurrence question is positive, a frequency question is asked to determine whether the condition happened rarely (once or twice), sometimes (three to ten times) or often (more than ten times) in the past four weeks. As shown in table 2.3, the 9 questions can be grouped in 3 universal domains of household food insecurity experience(FANTA, 2004; Coates, 2004; Swindale and Bilinsky, 2006):
A. perceptions of insufficient quantity of food B. perceptions of inadequate quality of food C. anxiety or uncertainty about whether the food budget or supply is adequate to meet basic requirements
The questions address the situation of all household members and do not distinguish adults from children or adolescents. The questions should be directed to the person in the household who is most involved with the food preparation and meals, mostly women.
22 LITERATURE REVIEW Domain Occurrence question A. Anxiety and uncertainty about 1. Did you worry that your household would not have enough the household food supply food? B. Insufficient quality (includes 2. Were you or any household member not able to eat the variety and preferences of the kinds of foods you preferred because of a lack of type of food) resources? 3. Did you or any household member have to eat a limited variety of foods due to a lack of resources? 4. Did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other types of food? C. Insufficient food intake and its 5. Did you or any household member have to eat a smaller physical consequences meal than you felt you needed because there was not enough food? 6. Did you or any household member have to eat fewer meals in a day because there was not enough food? 7. Was there ever no food to eat of any kind in your household because of a lack of resources to get food? 8. Did you or any household member go to sleep at night hungry because there was not enough food? 9. Did you or any household member go a whole day and night without eating anything because there was not enough food? Table 2.3: Occurrence questions in 3 different domains of food insecurity (Fanta, 2004 and Coates, 2004)
Calculation of the HFIAS score variable and HFIAS indicator
The respondents answers are coded according to the frequency of occurrence (never=0, rarely=1, sometimes=2 and often=3). The HFIAS score variable of the individual household is calculated by summing the codes for each question. The maximum score is 27 (response to all nine frequency questions was “often”) and the minimum score is 0 (the household responded “no” to all occurrence questions). The higher the score, the more food insecure the household.
The HFIAS indicator for a certain region can then be calculated as the mean HFIAS score of all households (Coates et al., 2007).
Interpretation
To interpret the HFIAS, another variable is used in addition to the HFIAS score, namely the Household Food Insecurity Access Prevalence (HFIAP).
For each question, dependent on the occurrence frequency, the households are categorized in four groups of household food insecurity (food secure, mild, moderately and severely food insecure).
The more a household experienced more severe conditions and/or experienced them more frequently, the more food insecure the household is categorized (table 2.4).
23 LITERATURE REVIEW Based on the codes assigned in table 2.4, a HFIA category variable is determined for each household (food secure=1, mildly food insecure=2, moderately food insecure=3 and severely food insecure=4).
• HFIA category = 1 if the codes for [(Q1=0 or1) and Q2 to Q9=0] • HFIA category = 2 if the codes for [(Q1=2 or 3 or Q2=1, 2 or 3 or Q3=1 or Q4=1) and Q5 to Q9=0] • HFIA category = 3 if the codes for [(Q3=2 or 3 or Q4=2 or 3 or Q5=1 or 2 or Q6=1 or 2) and Q7 to Q9=0] • HFIA category = 4 if the codes for [Q5=3 or Q6=3 or Q7=1, 2 or 3 or Q8=1, 2 or 3 or Q9=1 , 2 or 3]
The HFIA prevalence is then calculated for each category of food (in)security. For example, the percentage of severely food insecure households is calculated by dividing the number of households with HFIA category number four by the total number of household and multiplying by hundred (Coates et al., 2007).
Question Frequency Number Rarely (code= 1) Sometimes (code =2) Often (code=3) 1 FS Mildly FI Mildly FI 2 Mildly FI Mildly FI Mildly FI 3 Mildly FI Moderately FI Moderately FI 4 Mildly FI Moderately FI Moderately FI 5 Moderately FI Moderately FI Severely FI 6 Moderately FI Moderately FI Severely FI 7 Severely FI Severely FI Severely FI 8 Severely FI Severely FI Severely FI 9 Severely FI Severely FI Severely FI Table 2.4: Categories of food insecurity (Coates et al., 2007) with FS= food secure households and FI=food insecure households 2.1.6.2 Advantages and disadvantages of the coping strategy methods
Questions about “shame about socially unacceptable strategies” in a survey sometimes found that the shameful or socially unacceptable actions and feelings were very sensitive issues and that it was difficult to elicit an accurate response. FANTA concluded that not enough field-based success existed for a ‘generic’ question to be included in the HFIAS questionnaire, so the question has been dropped from this revised version of the HFIAS (Coates et al., 2007), which may be a disadvantage.
Another disadvantage of the HFIAS is that the list of questions is a fixed list and not context specific.
The CSI is more context specific than the HFIAS, because with the CSI the list of questions is adapted to the local situation while HFIAS uses a fixed standard list of questions. So, the CSI is better and stronger to compare households within one region while HFIAS is stronger to compare different regions with each other (Maxwell et al., 2008) unless the reduced CSI is used.
24 LITERATURE REVIEW The CSI is a relatively quick and simple indicator of household food security behavior (Maxwell et al., 2008). Although the CSI has proven useful as a context-specific indicator however, it has been criticized for being relatively unhelpful in comparative analysis (Kennedy, 2002). This problem can be solved by using a reduced index based on common coping strategies.
The more context-specific behaviors help to identify severely food insecure households, which is important for example in identifying the most vulnerable groups for household targeting purposes. For geographic targeting and resource allocation, it requires a comparison among different contexts, therefore the reduced CSI is more applicable. In early warning, assessing the impact of a shock or the impact of a food security intervention, the context-specific CSI is more meaningful (Maxwell et al., 2008).
The disadvantage of coping strategies can be that the recall information is not accurate. The respondent may, for example, cite more coping strategies than actually took place because this may be in her favor when the recall information will be used for selecting beneficiaries for food distribution. To minimize the learning effect of the individual respondents, this tool should not be used too frequently in the same households (Maxwell et al., 2003).
2.1.6.3 Methods based on dietary diversity and food frequency
The next two methods are based on dietary diversity and food frequency. Dietary diversity is defined as the number of different foods or food groups eaten over a reference time period, not regarding the frequency of consumption. Food frequency is defined as the frequency (in term of days of consumption over a reference period) that a specific food item or food group is eaten at the household level (WFP, 2007). Dietary diversity and food frequency have proven to be among the most common and valid indicators of nutrient adequacy and/or energy intake (Weismann et al., 2006; Dewey et al., 2005; Hoddinott and Yohannes, 2002; Ruel, 2002).
A. Household dietary diversity score (HDDS)
Concept
The Household Dietary Diversity Score (HDDS) is developed by FANTA (Swindale and Bilinsky, 2006). This method measures the household dietary diversity, on average, among all members, as a measure of household food access. A higher number of different food groups consumed means an improved household food access (Swindale and Bilinsky, 2006).
To better reflect a quality diet, the number of different food groups consumed is used rather than the number of different foods because the different foods might all belong to the same food group, meaning little diversity in both macro- and micronutrients and so a lower quality diet. The HDDS makes use of 12 food groups as listed below. To collect the household dietary diversity data, a list of
25 LITERATURE REVIEW foods for every food group is used and locally available foods are added to the list. Data are collected using the previous 24-hours as a reference period.
• Cereals • Root and tubers • Vegetables • Fruits • Meat, poultry, offal • Eggs • Fish and seafood • Pulses/legumes/nuts • Milk and milk products • Oil/fats • Sugar/honey • Miscellaneaous
Calculation of the HDDS
The HDDS variable is calculated for each household by summing the values of the different food groups. Values for each food group are zero when nobody consumed any food item within that food group and 1 when at least one member consumed one food item in that food group. The value of the HDDS variable ranges from 0 (none of the food groups consumed) till 12 (every food group consumed).
The HDDS indicator is the average HDDS for the sample population which is calculated by dividing the sum of the different HDDS variables for all the households by the number of households in that sample population.
Interpretation of HDDS
A higher HDDS means that more different food groups are consumed, so that there’s an improved household food access. To use the HDDS indicator as an indicator for improvements in food security, a target is needed with which the indicator can be compared. There are two possible targets. A first target is to use the average HDDS of the richest 33 percent of households, assuming that poorer households will diversify food expenditures as their income rises. When no income data are available, the average diversity of the 33 percent households with highest diversity can be taken as a target.
B. World Food Programme’s (WFPs) method
Concept
The objective of the World Food Programme’s (WFPs) method is to establish the prevalence of food insecurity (Wiesmann et al., 2008). The WFPs method consists of two steps. The first step is the construction of the Food Consumption Score (FCS) and the second step is the classification of the
26 LITERATURE REVIEW households based on the FCS. The analysis is restricted to the quantitative aspect of food security.
Data are collected on a 7-day recall of frequency of consumption of several food groups at the household level. Foods that are consumed in small quantities (lower than 15 gram) are excluded from the FCS.
Calculation
All the foods are divided in eight different food groups. The FCS is then calculated by summing the consumption frequencies of the foods within the same group. Any consumption frequency greater than 7 is recoded as 7, which is called truncation. The value of each food group is then multiplied by its weight. The sum of the weighed food group scores gives then the FCS (Wiesmann et al., 2008).
Because each food group has a weighing factor, the score is a frequency-weighted diet diversity score. The different food groups and the weight of each group is given in table 2.5.
Notice that the previous method, HDDS, does not take into account the frequency of food consumption, uses 12 food groups, includes also foods consumed in small quantities (lower than 15 gram) and is an unweighted food frequency indicator.
Group number Food group Weight 1 Main staples 2 2 Pulses 3 3 Vegetables 1 4 Fruit 1 5 Meat and fish 4 6 Milk 4 7 Sugar 0.5 8 Oil 0.5 Table 2.5: Aggregate food groups and weights to calculate the Food Consumption Score (WFP, 2007)
Interpretation
Different thresholds of the FCS, divide the households in three food consumption groups. When the FCS is between 0 and 21, the household is categorized as poor, between 21.5 and 35, at the borderline and when the FCS is above 35, as acceptable. For populations with high sugar and oil consumption, the thresholds for the three consumption groups can be raised from 21 and 35 to 28 and 42 (WFP, 2007).
Three different cut-off points derived from a basic minimum dietary energy requirement of approximately 2100 kcal/capita/day, corresponding to shortfalls of 0, 10 and 30% relative to requirements should match the Food Consumption Groups derived from the Food Consumption Score (WFP, 2005) as shown in table 2.6.
27 LITERATURE REVIEW Calorie consumption Shortfall in percent Calorie consumption groups (kcal/capita/day) < 1470 >30 Poor 1470 till < 2100 30 till > 0 Borderline ≥ 2100 0 Acceptable Table 2.6: Food consumption shortfalls (WFP, 2005)
Findings of research in Burundi, Haiti and Sri Lanka showed however, that the cut-off points used by the WFPs method to define poor, borderline and adequate food consumption groups seriously underestimate the food insecurity measured by calorie consumption per capita, in other words, only a low share of calorie deficient households being identified by the FCS classification. One solution is to adjust the cut-off points to capture the prevalence of calorie deficiency, but when food frequencies with consumption quantities of 15 gram or less are excluded from the FCS classification, the cut-offs recommended by the WFPs method and the adjusted cut-off to capture the prevalence of calorie deficiency are relatively close. However, a country-specific adaptation of the cut-offs to exclude small quantities is necessary.
In accordance with the above findings, the WFPs method recommends the exclusion of small quantities when collecting food frequency data for the FCS (Wiesmann et al., 2008).
2.1.6.4 Advantages and disadvantages of dietary diversity and food frequency methods
The HDDS is a good indicator for several reasons: a more diversified diet, e.g. is associated with a number of improved outcomes (birth weight, child anthropometric status, etc.) and is highly correlated with factors as household income. Another advantage is that the questions on dietary diversity can be asked at household level so that food security can be examined on household level (Swindale and Bilinsky, 2006). A disadvantage may be that the questions refer to the household as a whole and not to any single member of the household. Another disadvantage is that it also includes foods consumed in quantities lower than 15 gram. As mentioned by Wiesmann et al. (2008), this results in an underestimation of the calorie deficient households because eating only a small quantity of many different food groups results in a high dietary diversity but not in a high calorie intake.
According to Wiesmann et al. (2008) food frequency scores (WFP) are superior to simpler measures of diet diversity, such as the food group count in HDDS.
Limitations of the WFPs method are that first of all it does not take into account the quality of the diet, secondly there’s a lack of precise information on the effects of excluding small quantities from food frequencies and finally as stated before there are disadvantages and advantages compared to methods that collect information during the previous 24-hours (Wiesmann et al., 2008).
28 LITERATURE REVIEW 2.1.6.5 Methods based on food intake
The following 3 methods are based on food intake. Worldwide the 24-hour recall is the most applied method for research on food consumption. However, other methods are the weighed intake and the diary method.
A. Weighed intake method
The weighed intake method is a precise and simultaneous registration of what an individual drinks and eats. Everything that is consumed is described precisely and weighed before consumption. The method is carried out by trained people.
B. Diary method
The diary method requires the interviewee to record everything they consumed throughout the day in a kind of diary.
C. 24-hour recall
Concept
Moderate deficiencies of micronutrients, like zinc and iron, have far-reaching consequences on health and nutrition deficiencies are widespread in developing countries, where staple diets are frequently plant based and the consumption of expensive animal foods is low (Gibson and Ferguson, 1999). In these areas where diets are not very diverse and especially plant based, a 24-hour recall is especially suitable. The recall is repeated on at least 2 days to allow making an estimation of the distribution of intakes of individuals and to estimate the proportion of the population that is at risk for inadequate intakes.
The 24-hour recalls normally uses a standardized protocol build up of four stages. The first stage is obtaining a complete list of all foods and beverages consumed during the preceding day. In the second stage, detailed descriptions of all the foods and beverages consumed, including cooking methods and brand names, are recorded together with time and place of consumption. In the third stage the amounts of all foods and beverages consumed and all the ingredients used in the mixed dishes are collected. In the last stage, the recall is reviewed.
Unlike the other methods, the 24-hour recall requires some preparation (Gibson and Ferguson, 1999).
In order to estimate the portion sizes as accurate as possible researchers can develop picture charts (drawings or photographs) presenting the most often consumed foods in different calibrated proportions. All the photographs can be assembled in a photo book. Showing this photo book to the respondents helps them to make better estimations of actual eaten portion sizes (e.g. photographs of a plate with small, medium and large portion of rice).
29 LITERATURE REVIEW A selection of local utensils such as glasses, plates and spoons should be purchased for estimating the amount of food or beverages actually consumed, although it is preferable to ask the respondents to supply their own utensils where possible. The local utensils must be calibrated with a graduated measuring cylinder and water before being used. A graduated plastic cylinder is also useful for estimating portion sizes of liquids and flowing solids.
Monetary value can be used to estimate the amount consumed of take-away foods, some commercial foods and street foods.
For converting the portion sizes of foods consumed into weight equivalents, several procedures can be used:
• direct weighing: recording the weight in grams of actual foods by using dietary scales • volume equivalents: recording the volume of water that is equivalent to the volume of food or beverage item consumed and then converting the volume to grams • monetary value: converting the monetary value of a purchased food item into weight equivalents
Direct weighing of foods is the easiest way to determine the weight equivalents of the portion sizes consumed. Note that if the food item contains an inedible portion, it is important to weigh the edible portion too.
Volume equivalents are useful when the volume instead of the weight of the actual food can be recorded by measuring into a graduated measuring cylinder or a calibrated utensil. Alternatively, a volume of water equivalent to the volume of actual food can be measured by using a measuring cylinder or calibrated utensil but must be converted into weight equivalents. This is done by weighing an equal volume of the actual food or beverage prepared by using a local recipe. Then you know the volume and the weight of the food item and can calculate the conversion factor.
Monetary value is determined by selecting several vendors in the study area (Gibson and Ferguson, 1999). Then weighing five to ten times an equal monetary value of each food item from each vendor. Then summing the total weight of each food item for each monetary value purchased from all the vendors. Then the total weight is divided by the number of portions weighed of each food item. The average then represents the average weight of that food item for that monetary value. This data is collected together in a price-weight conversion list.
Interpretation
When the 24-hour recall is done, food composition tables are used to convert food consumption data into energy and nutrient data. Food composition values represent estimates of the nutrient composition of a food item. As a result, the energy and nutrient intake of all the households interviewed can be calculated.
30 LITERATURE REVIEW 2.1.6.6 Advantages and disadvantages of food intake methods
An advantage of all the food intake methods, is that these methods can estimate the prevalence of micronutrient deficiencies without the need to collect biological samples. Because collecting biological samples is often not accepted by rural populations in developing countries. Another advantage is that these methods can be used to develop dietary strategies like changing traditional methods for preparing indigenous foods. Although these strategies only have a long term effect, they are more sustainable, culturally acceptable, economically feasible than strategies like supplementation and fortification.
The weighed intake method has the advantage to give very precise information independent of the respondent’s capacity to memorize. A disadvantage is that the method is highly time-demanding, expensive and hard for the interviewer (Gibson and Ferguson, 1999). Another disadvantage is that the record method can influence the pattern of food consumption because it is done at the moment of food intake, which diminishes the reliability of the information (Willet, 1990). People have a tendency to differ their normal pattern of consumption when someone is watching.
An advantage of the diary method is that it provides information that may be forgotten during the interview. Because the interviewer is not present at the moment of consumption, the consumption behavior will not be influenced either. A disadvantage is that it requires some effort from the user and some information may be forgotten or incomplete. Also it requires a lot of work to analyze the information. The method relies on the ability and motivation of the user to write down the information (Kirakowski and Corbett, 1990).
The 24-hour recall method has the advantage of being less time consuming, less expensive, easier and faster compared to the weighed food method. Notice that the 24-hour recall does not affect the consumption pattern because information is collected after the food is consumed.
Disadvantages of the 24-hour recall are the errors that affect the quality of the dietary data collected. The errors that may occur are respondent biases (Gibson, 1987), interviewer biases, unintentional omission of foods, incorrect estimation of portion sizes, incorrect conversion of household measures into grams.
31 LITERATURE REVIEW 2.2 Food security and nutrition in DRCongo
Little data about the food security and nutrition situation in DRCongo is available and data that is available must be carefully interpreted. This is due to the malfunctioning and the limited resources available for the services of (agricultural) statistics in DRCongo, the inaccessibility too many parts of the country, the underpayment, the mainly informal economy, the wars of 1996-2003, the remaining instability in many parts of the country etc. An example of the low reliability of data are the food balance sheets of DRCongo, which are based on the official statistics of the agricultural production in DRCongo. They are discussed in 2.2.1.3 and give a food supply of 1486 kcal per day for an adult person in 2005. But how can someone survive with only 1486 kcal a day? According to human nutrition and physiology specialists, this is not possible, only if you sleep all day to reduce the physical effort to a minimum.
Although the problems just mentioned, we attempt to give an overview of the most recent surveys and data about the food security and nutrition situation in DRCongo. Most of these surveys were however done in and around Kinshasa, while our research is done in Kisangani and Yaoseko.
2.2.1 Food security and food consumption
In this part, some parameters are given that reflect the evolution of the food security situation in DRCongo, followed by some food insecurity trends and causes. Finally, the food consumption trends starting from the eighties till 2005 are described.
2.2.1.1 Some international parameters
A. The global hunger index
The global hunger index (GHI) is the mean of the proportion of people that is undernourished, the prevalence of underweight in children under five and the proportion of children dying before the age of five. In 1990 the GHI in DRCongo was 25.5 and rose to 39.1 in 2009. Compared with other countries this was the highest increase (figure 2.1), mainly because of a rise in the proportion of the people that is undernourished ( 76% of the population). Also violent conflicts and political instability have given rise to widespread poverty and food insecurity (Grebmer et al., 2009).
Figure 2.1: GHI winners and losers from 1990 GHI to 2009 GHI (Grebmer et al., 2009)
32 LITERATURE REVIEW B. Food Insecurity State in the World (FAO, 2001)
According to “the State Of Food Insecurity in The World of 2001” (FAO, 2001), DRCongo is the country with the biggest increase in proportion of undernourished people in the period from 1990-92 till 1997-99, being 29% of 17 million people. The number of people undernourished tripled in DRCongo during this period (FAO, 2002). In 2003, 70% of the people in DRCongo was malnourished. And as mentioned above 76% of the population was undernourished in 2009. DRCongo must be the most important target for halving the number of malnourished people in the world against 2015 or later, the goal of the World Summit for Food (Tollens, 2003).
2.2.1.2 Food insecurity trends and causes in DRCongo
Food insecurity concerns both food supply and demand. The supply depends primarily on local food production and food importations while the demand depends primarily on the number of people and their purchasing power. Purchasing power and income determine food access. The general poverty of the population is a serious limit on food consumption.
Traditional agricultural production, fishing activities and animal production all showed a downward trend since 1998, while according to the population growth (2.7% per year according to the World Bank,2008), there should actually be an increase. There was a decrease of 20% for cereals, 12% for roots and tubers and 6% for vegetables. The production of cassava, which covers 70 to 80% of the diet of the Congolese, decreased with 20% due to diseases and parasites (Tollens, 2003). The reason for the unstable supply of agricultural products and food differs from one province to another. The stability of the supply suffered a lot because trade across the equator, taking advantage of the changing seasons has decreased and often even stopped. This caused a high instability of prices on the market and substitution between food. Besides this, the robberies of 1991 and 1993 and the non- payment of the salaries caused unemployment and erosion of the purchasing power of the population and also diminished access to food. In addition, civil wars had a big influence on household incomes which further decreased the access to food.
All these factors, e.g. the disappearance of wage labor in the formal sector, the non-payment of salaries, the erosion of the purchasing power due to inflation and the currency depreciation, the stop of almost every investment, etc. resulted in the fact that Congo now ranks between the five poorest countries of the world. The GDP per capita was in 2000 only 80 US$. Almost all the social segments have become vulnerable. The principal cause of the chronic food insecurity in DRCongo is the absolute poverty of the population (Tollens, 2003).
Since the eighties, food import has been important for the food security in Kinshasa (Goosens et al., 1994), especially the import of cereals (wheat and rice) and frozen products of animal origin (fish and meat). But with the war and the economic crisis, less products of animal origin have been imported. The total import (in calories) hasn’t diminished, but the composition has changed: more cereals, less meat of good quality (chicken, beef), more fish and more offals. The consumption of meat has become very rare. Most often, only little pieces of meat are present in the sauce (Tollens, 2003).
33 LITERATURE REVIEW 2.2.1.3 Food consumption trends
The insufficient inputs, the deterioration of the road infrastructure for the evacuation of the harvest and the deficiency of finance of the production, do not permit an optimal exploitation of the agricultural potential of the Oriental Province as in the rest of the country. Which leads to a qualitative and quantitative nutrition deficiency, under the still growing population of the Oriental Province (PNUD/UNOPS, 1998).
A. The eighties
Nutrition questionnaires conducted in the eighties by the Congolese department of rural agricultural development in the Oriental Province to determine the nutritive value of the diet of the population based on the quantities of every food consumed per day and per household, gave the following results (PNUD/UNOPS, 1998):
From a quantitative point of view (grams/household/day):
• Carbohydrate-rich foods contributed for 72 %, of which 56 % staples (mainly cassava and plantain banana), 24 % vegetables (mainly cassava leaves) and 17% cereals (mainly rice) • Lipid-rich foods contributed for 10% (mainly palm oil (75%), palm nuts (6%) and groundnuts (15%)) • Protein-rich foods contributed for 18% (mainly beans (38%), smoked fish (9%) and fresh fish (6%))
From a qualitative point of view (calories/household/day):
• Carbohydrate-rich foods contributed for 54% (mainly cassava and plantain banana) • Lipid-rich foods contributed for 30% (mainly palm oil and groundnut oil) • Protein-rich foods contributed for 16% (mainly beans, salted fish and hunted animals)
With the erosion of the purchasing power during the last decennium, the diets of the population have significantly changed in both quantity and quality.
B. The nineties
Nutrition questionnaires conducted in July 1995 and April 1996 (before the 1996-2003 civil strife) by ‘la Mission de Programmation du Plan de reliance du Secteur Agricole et Rural’ showed that the mean energy content of the diet in the Oriental Province was 1758.24 kcal/inhabitant/day, which is far below the 2300 kcal recommended by FAO (PNUD/UNOPS, 1998). The survey further showed that this type of diet was mainly composed of :
• Carbohydrate-rich foods, covering 68% of the diet • Lipid-rich foods, covering 26% of the diet • Protein-rich foods, covering only 6% of the diet
34 LITERATURE REVIEW Compared to the eighties, the kcal coming from carbohydrate-rich foods increased with 14% and hence dominated the diet even more, the amount of lipid-rich foods decreased with only 4% and the amount of protein-rich foods decreased with 10%. This diet with only 6% of protein-rich foods, was a protein deficient diet.
C. Food balance sheets 1995-2005
The food balance sheets (FAO, food balance sheet, Congo, 1995-2005) are used to explain the further food consumption trends due to a lack of more recent surveys. In contradiction to the data of the surveys conducted in the eighties and the nineties, which only concern the Oriental Province, the data of the food balance sheets represent the whole country. According to the food balance sheets, the food supply per capita was 1790 kcal in 1995, which is quite similar to the calorie intake measured by ‘la Mission de Programmation du Plan de reliance du Secteur Agricole et Rural’ in the survey of 1995-1996.
As shown in figure 2.2 the food supply per capita per day further decreased from 1790 kcal in 1995 to 1486 kcal in 2005. As already mentioned in the beginning of this chapter, if this should be true, all Congolese would already be death by now. Again, we remark that the statistics of the agricultural production in DRCongo, on which the food balance sheets are based, only have a low reliability, because of the limited resources that the services of agricultural statistics have in DRCongo. For example, the agricultural production that was obtained by a project done in 1987-1989 in Bandundu and Lower-Congo was compared with the official statistics. The conclusion was that the food balance sheets, based on official statistics underestimate the apparent food consumption (Tollens, 2002).
2000 1800 1600 1400 1200 1000 Grand Total + 800 Vegetal Products + 600 kCal/capita/day 400 Animal Products + 200 0
year Figure 2.2: Supply of kcal/capita/day in DRCongo from 1995 till 2005 (FAO, 2005) made by author
The food balance sheets can further be analyzed to see the contribution of different food groups to the Congolese diet (see figure 2.3). The food supply in DRCongo contains mainly vegetal products. In 2005, the vegetal products accounted for 97.8% of the total energy supply, while the animal products only accounted for the residual 2.2%.
The absolute supply of vegetal products in 2005 was decreased with 300 kcal per capita per day since 1995 and 200 kcal of this decrease was in the starchy roots supply. The starchy roots and cereals
35 LITERATURE REVIEW were the main contributors of the vegetal products supply, with 56.5% and 20.2% respectively in 2005 (FAO, 2005). Although the absolute intake of cassava in 2005 decreased with 170 kcal per capita per day compared to 1995, cassava still contributed for 97.5% of the energy supplied by starchy roots. This confirms again the importance of cassava in the diet of the population (Goosens et al., 1994, Goosens, 1996, Kankonde and Tollens, 2001). In spite of the diversity of food crops in the Oriental province, cassava, which is produced in a sufficient quantity, is the basic food (staple food) that contributes to the survival of the majority of the population, and the cheapest (in price per calorie) energy source (PNUD/UNOPS, 1998). According to Tollens (2003) cassava has been the most important crop to avoid hunger and hence to save fugitive or moving populations. Because cassava can be harvested at any moment and can be conserved very well in the soil (till 24 months after planting), it’s an excellent food (and war) security crop.
Pulses, which are having an important nutritional value, only contributed for 2% to the total energy supply in 2005, of which more than half was contributed by beans (FAO, 2005). This remained more or less the same since 1995.
Groundnuts contribute to the protein and fat intake and are cultivated in all provinces of DRCongo (Tollens, 2004). Their absolute supply decreased with 20 kcal/capita/ day since 1995 and contributed with 77.6% to the energy supply of oil crops in 2005. The contribution of oil crops to the total energy supply in 2005 was 3.9%.
Vegetable oils had a contribution of 7.3% in 2005 of which palm oil made up the biggest part, with 75.9%. Although palm oil is still consumed in big quantities, the absolute palm oil supply decreased with 17 kcal per capita per day since 1995.
Vegetables and fruits only accounted for 1.1 and 2.6% of the total energy supply in 2005. The absolute vegetable supply only decreased with 4 kcal per capita per day since 1995 but the absolute fruit supply decreased with 40 kcal/capita/day. The decrease of fruit supply is mainly due to the 30 kcal/capita/day decrease of plantain bananas, they contributed for 1.2% of the total energy supply in 2005 and they still made up the biggest part of the energy supply by fruits, namely 46.2%. Desert bananas made up 20.5% of the total energy supplied by fruits in 2005. Plantain bananas are important in forest areas where they often make up the base of the nutrition of local populations. The problems associated with plantain and desert bananas are situated on the level of diseases (black sigatoka), insects and nematodes. Also transport and conservation can be a problem because they are very perishable. In addition, desert and plantain bananas are relatively expensive (Tollens, 2004). With regard to vegetables, especially cassava (Manihot esculenta) leaves, Amaranthus spp. and sweet potato (Ipomoea batatas) leaves were consumed. They are an important source of vegetal proteins, vitamins and minerals. With the rurbanisation of Kinshasa (ruralisation of the city), a big part of the urban population tried to gain their livings with small agricultural activities such as gardening of vegetables (Tollens, 2003).
36 LITERATURE REVIEW Other field crops which are of less importance on a national scale but which have a certain local importance are: wheat, sugarcane, potato, sweet potato and yam (Tollens, 2004).
Alcoholic beverages accounted for 1.5% of the total energy supply in 2005.
The energy supplied by animal products, did not change much since 1995, 2.2% of the energy was contributed by fish, meat, poultry, eggs and milk in 2005. Livestock development is possible in DRCongo because of its large grasslands and woodlands. The pastoral potential is 30 to 40 million cattle, while the country barely has 1 to 1.3 million big livestock. Especially the eastern part of the country (Ituri and Kivu) has a big potential. But the general poverty and big importation of frozen meat products of low quality at unbeatable prices, prevent the development of this sector. It’s especially small livestock and poultry that gained importance in urban and peri-urban regions, where it’s intended for auto consumption and sale. The principal constraint for livestock development is the demand.
Figure 2.3: Supply of the percentage of kcal per capita per day in DRCongo in 2005 (FAO, 2005), made by author 2.2.2 Nutritional outcomes
Important information has been collected by doing nutritional surveys. Recent surveys hold in DRCongo are cited here with regard to nutritional outcomes.
2.2.2.1 The Multiple Indicator Cluster Surveys (2001)
Nutrition questionnaires organized by the health ministry and NGO ‘s made it possible to gather information about the food security situation in DRCongo, in particular of children and women. The questionnaires used to be executed in and around Kinshasa but recently the questionnaires were also organized in other areas. In general, they concerned a restricted area and were hence not representative for the whole population. Nonetheless, these surveys gave an image of the food security situation on a certain moment in time for a certain group of the population.
37 LITERATURE REVIEW The survey of 2001 is the one that is part of the Multiple Indicator Cluster Surveys (MICS2) (Ministère du Plan et de la Reconstruction et al., 2002), which covered the entire national territory of DRCongo (9454 children) and was set up to determine the situation of children and women. We must remark that a sample size of 9454 children is quite small to be representative for a total population of 64.3 million people (World Bank, 2008).
The survey was executed under the auspices of the United Nations International Children’s Emergency Fund (UNICEF) with an important finance of the USAID. The results of this survey were then compared with the results of “the National Questionnaire on the Situation of Children and Women” of 1995. Three conventional anthropometric indicators were retained: stunting (height for age) or chronic malnutrition, wasting (weight for height) or acute malnutrition and underweight were measured (Tollens, 2003).
The analysis of data collected during these surveys showed that the nutritional situation in DRCongo remained very critical. With the indicators measured, it was clear that the nutritional situation stagnated or even got worse (Tollens, 2003).
2.2.2.2 L’Enquête Démographique et de Santé (2007)
‘L’Enquête Démographique et de Santé en République Démocratique du Congo’ (EDS-RDC) is the first survey of its kind realized in DRCongo in 2007 (Ministère du Plan et Ministère de la Santé, 2008). The survey was executed under the supervision of ‘le Ministère du plan’, with collaboration of ‘le Ministère de la Santé’.
The survey was financed by USAID, the Department for International Development (DFID), UNICEF, United Nations Population Fund (UNFPA), ‘Le Programme National Multisectoriel de Lutte contre le SIDA’ (PNMLS) and ‘le Projet d’Appui à la Réhabilitation du Secteur de la Santé’ (PARSS). The survey got technical assistance from the global program of Demographic and Health Surveys of Macro International, which is an american institution that is responsible for the international program of ‘L’Enquête Démographique et de Santé’ all over the world, of which the objective is to collect, analyze and spread demographic and health data, more in particular about fertility, household planning, health and nutrition of the mother and the child and AIDS around the world. Besides the indicators on fertility, child mortality, household planning, mother and child nutrition measured by the MICS in 2001, the EDS-RDC also provides, for the first time, information about the prevalence of AIDS among the adult population thanks to the introduction of biological tests.
The data collection took place twice. The first time, data was collected in the capital city, Kinshasa, from the 31st of January till the 7th of March 2007. The second data collection occurred outside Kinshasa from May till August 2007. In total 9002 household were selected, of which 8886 surveys succeeded (answer percentage of 99%). Within these households 9995 surveys of women between 15 and 49 years old and 4757 surveys of men between 15 and 59 years have been well conducted. We must remark that sample sizes of 9995 and 4575 are quite small to be representative of a total population of 64.3 million people (World Bank, 2008).
38 LITERATURE REVIEW 2.2.2.3 Comparison of nutritional indicators
The nutritional status of the children under five was measured with the anthropometric indicators: stunting (height for age) or chronic malnutrition, wasting (weight for height) or acute malnutrition, and underweight (weight for age) or global malnutrition. Data of the survey in 2007 is here compared with data of nutrition surveys previously executed in 1995 and 2001.
In 2001, the prevalence of both moderate and severe stunting was the highest with 38.2%, followed by 31.1% severe and moderate underweighted children and 16.9% severe and moderate wasted children (Tollens, 2003). Compared to the nutrition survey of 2007, the prevalence of the different malnutrition types was still in the same order since 2001 but the prevalence of moderate and severe stunting increased to 46% while the prevalence of moderate and severe underweight and wasting decreased to 25 and 10% respectively (table 2.7) (Ministère du Plan et Ministère de la Santé, 2008).
The percent of severe malnutrition types of children under five in 1995, 2001 and 2007, as presented in table 2.7, show that there was a worsening of the prevalence of severe wasting from 1995 to 2001 and a stagnation of the prevalence of stunting and underweight from 1995 to 2007. The prevalence of severe wasting or acute malnutrition, almost doubled in 2001 (6.6%) compared to 1995 (3.5%). Data of severe wasting in 2007 was lacking. Stunting or chronic malnutrition is seen as the most alarming indicator, because this indicator negatively influences the growth of the children and their intellectual capacity (Tollens, 2003). The prevalence of severe stunting decreased from 1995 to 2001 and again increased in 2007 to the same level as in 1995. As regards underweight or global malnutrition, the prevalence decreased from 10.2% in 1995 to 9.4% in 2001 to 8% in 2007.
Malnutrition type 1995 2001 2007 Stunting Unknown 38.2 46 Wasting 9.6 16.9 10 Underweight Unknown 31.1 25 Severe stunting 24.6 20.3 24 Severe wasting 3.5 6.6 Unknown Severe underweight 10.2 9.4 8 Table 2.7: The percent of malnutrition types of children under five in DRCongo in 19951, 20011 and 20072 (1Tollens, 2003 and 2Ministère du Plan et Ministère de la Santé, 2008)
Next to the anthropometric indicators, also other nutritional indicators were calculated. One indicator was the body mass index. In 2007, 19% of the women had a body mass index lower than 18.5 which showed that the nutritional status of the women was critical. The proportion of the women that suffered from malnutrition was higher in rural (21%) than in urban areas (16%) (Ministère du Plan et Ministère de la Santé, 2008). The underweight at birth was also critical. In 2001, 10.7% of the new born babies weighed less than 2.5 kg (Tollens, 2003). The number of children with oedema (sign for Kwashiorkor caused by a lack of proteins in the diet), increased from 2% in 1995 to 3.7% in 2001. This was certainly linked to the war and poverty
39 LITERATURE REVIEW progression so that parents could not buy foods rich in proteins (groundnuts, beans, milk, meat, fish) anymore (Tollens, 2003).
2.2.3 Distribution of malnutrition
If we look at the distribution of the global malnutrition in 2001 according to province (table 2.8), South Kivu and Katanga had the highest percent of children under five with severe malnutrition, 13.3% and 11.5% respectively, while the lowest percent of children under five with severe malnutrition were found in Kinshasa (Tollens, 2003). Because our research was situated in the Oriental Province, we look here more into detail to the mild, moderate and severe global malnutrition rates of children under five in the Oriental Province. In 2001, these rates were all higher in the Oriental Province than in Kinshasa, 32.7, 19.2 and 7.1 percent compared to 29, 14.2 and 4.2 percent respectively .
The distribution of the global malnutrition in 2001 according to rural or urban residence area shows that the percent of children under five with severe malnutrition living in a rural area is more than double of those living in an urban area (Tollens, 2003).
Mild malnutrition Moderate malnutrition Severe malnutrition Province Kinshasa 29 14.2 4.2 Lower Congo 33.2 13.7 10.8 Bandundu 31.8 23.7 10.7 Equator 30.8 22.6 8.8 Oriental Province 32.7 19.2 7.1 North Kivu 30.7 25.7 7.9 South Kivu 31.3 21.8 13.3 Maniema 24.6 28.4 9.1 Katanga 28.9 21.4 11.5 Oriental Kasaï 28.1 20.4 9.5 Occidental Kasaï 28.6 23.6 10.1 Residence area Urban 29 16.9 5.1 Rural 30.8 24 11.5 Table 2.8: Distribution of global malnutrition of children under five (%) according to province and residence area in DRCongo (Tollens, 2003)
All these surveys showed that the nutrition situation in DRCongo got worse concerning the acute malnutrition, the insufficient body mass index of women, the underweight at birth and the rural areas are more strongly affected compared to the urban areas.
40 LITERATURE REVIEW 3.METHODOLOGY
The preparation and progress of the field research is discussed in a first paragraph and the different parts of our questionnaire are discussed in the second paragraph. A last paragraph describes the data analysis.
3.1. Data collection 3.1.1. Research site
Our research took place in the 6 municipalities of Kisangani and in Yaoseko (see 1.2). The personal interviews took place during July-September 2009 in the respondent’s home because a familiar environment encourages participation (De Schampheleire and Van Looveren, 1995).
Before starting the research, an official document from the University of Kisangani containing the objectives of the survey and including the names of all the researchers was submitted for approval and then signed and labeled with a stamp by the mayor of Kisangani. This official document was copied for all the teams and shown to the head of each area.
Another official document was elaborated for our stay and research in Yaoseko. People in Yaoseko were already subject of research within the same project before, so we easily obtained full consent and collaboration of the village head and participants after clear presentation of our research objectives and protocols.
3.1.2. Sample design 3.2.2.1 For both samples
Because our research is focused on the nutrition and food security situation and due to the fact that mostly women are responsible for the preparation of food in DRCongo, only women older than 18 and with certain responsibilities in the household were interviewed.
3.2.2.2 Kisangani
A general list of the inhabitants of Kisangani was not available, so we opted for a cluster random sampling (Gibson and Ferguson, 1999), each cluster being a geographical unit or municipality of Kisangani. In this way, we obtained six clusters. Within each municipality, we informed us about the number of areas at the municipality administration and choose randomly 4 areas in each municipality. Once arrived in the target area, we randomly visited a first household. If the women/mother/wife was home and agreed, we started the interview. When she was not at home or disagreed, we visited the household next door, until finding a valuable participant. When the
41 METHODOLOGY interview was finished, we skipped three doors and visited the household four doors next to the first one. If the responsible women was at home and agreed to participate, we did the interview, if not, we visited the next door and so on until we reached 10 valuable interviews per area (Gibson and Ferguson, 1999).
In total 40 surveys were done in each municipality of Kisangani, which gave a total of 240 surveys.
3.2.2.3 Turumbu
In addition, we wanted to compare women living in a rural setting with those living in the city of Kisangani.
Yaoseko, is a rural village belonging to the Turumbu ethnic group. In Yaoseko, we randomly selected 130 responsible women out of 184 households (Haesaert, 2007). We selected Yaoseko as research village, first of all because of the willingness of the population to collaborate (experienced during previous project research) and secondly, because of the accessibility. The latter does not undermine the rural character of the village, but permitted us to reach Yaoseko in a relative easy way by motorbikes.
To minimize ethnical influences, we selected another sample of Turumbu women in Kisangani. All these women are member of the ‘Turumbu mutuelle’. Unfortunately no list of the members of the 'mutuelle’ was available. So to find the women of this ‘mutuelle’ in Kisangani, we contacted ‘le Chef des Travaux’ (university assistant) Ramazani, which is head of the ‘mutuelle’. Ramazani, advised his assistant, mister Mundelendombe, to help us gathering those women together at one central point where all the teams worked together until our quota was reached.
In Yaoseko and Kisangani 130 and 122 Turumbu, respectively, were interviewed, giving a total of another 252 surveys.
Due to the large amount of data collected and a limited time to analyze all of them, the data that will be further discussed, in this master dissertation, are the data collected from the Turumbu people in Yaoseko and Kisangani.
3.1.3. Equipment
To estimate the portion sizes, a combination of 3 methods was used as proposed by Gibson and Ferguson (1999), namely a photo-book, a price-weight conversion list and household utensils.
For developing the photo book, photographs were taken of different portions of prepared staples: boiled rice, ‘lituma’ (pounded plantains, Musa paradisiaca and/or cassava,Manihot esculenta) and ‘fufu’ (paste made of cassava and/or maize flour), of beans (figure 3.1) and of vegetables: ‘pondu’ (cassava leaves), ‘muchicha’ (Amaranth spp.), ‘matembele’ (sweet potato leaves) and spinach. The photographs were taken with local people, who were asked to put a self-prepared small, medium and big portion on a plate. We also took pictures of different sizes of boiled plantain, boiled cassava and
42 METHODOLOGY ‘chikwangue’ (cassava paste) at the market place. Each time the portion was weighed and a photograph was taken.
Figure 3.1: From left to right: small, medium and large portion of beans in our photo book (Source: own research, 2009)
To set up the price-weight conversion list, the foods most often consumed in the season of the survey were weighed, measured and also photographed on different markets (Tshopo 15ième avenue, Tshopo 11ième avenue, marché Kabin, marché central and marché Balese) in Kisangani (annex 1). For Yaoseko, the Kisangani the price-weight conversion list was used except for the weight of the leafy vegetables. The weights of the leafy vegetables in Yaoseko were equalized to the double of the weight for the same price in Kisangani. Surprisingly, prices of coffee, sugar, salt etc. did not differ much from the Kisangani prices. This can be explained by the rather small distance to the city. The weights of the foods in our price-weight conversion list were the weights of the edible part of raw foods because the nutrient values of most of the foods in the food composition table were those of the edible part of raw foods. The percentage of waste, and hence thus the edible part, could be calculated from the average recipes (for example: 100 gram ‘matembele’ bought on the market has on average 38,8 percent waste, and hence an edible part of 61.2 gram) or were estimated by buying, for instance fruit, taking the weight of the fruit as purchased, then eating the edible part of the fruit and weighing the leftovers. Subtracting the leftovers from the weight as purchased then gave the weight of the edible part. The weights of the staples rice, plantain and cassava were an exception in our price-weight conversion list. The weights of these staples, were the weights of the boiled staples and not of the raw product, because the nutrient values of boiled rice, boiled plantain and boiled cassava were also present in the food composition table. The weights of boiled rice, boiled plantain and boiled cassava were taken in the same way as for the raw foods at the different markets.
In addition, a variety of different calibrated spoons and plastic cylinders were carried along to help estimate portion sizes.
3.1.4. Training of interviewers
Every team consisted of a Belgian and Congolese researcher. The Congolese researcher did the interview in the local language and the Belgian researcher wrote down the answers. Respondents were free to choose the language of the interview (French, Lingala or Kiswahili).
Differences in age, education, socio-economic status, race, religion, gender etc. between the interviewer and the respondent, influence the answers given by the respondent (Kahn and Cannell, 1957). Kahn and Cannell (1957) remarked as well that the appearance does not matter in the case of well trained interviewers. This is why the selected interviewers were girls all having a high school
43 METHODOLOGY degree and feeling more confident with the research area.
Training the interviewers is a critical factor in doing the interviews (Gibson and Ferguson, 1999). The training was organized as follows. First, the general objectives of the study were explained, as well as the role and responsibilities of the interviewers. It was important that the interviewer understood the objectives of the research, because every time before starting the questionnaire, the interviewer had to introduce the questionnaire to the respondent. This introduction included the answers to the following questions: who is responsible for the research, what are the objectives of the research, what will be done with the results and why the respondent is chosen to participate (De Schampheleire and Van Looveren, 1995). Then, the questionnaire was discussed and the objectives of each question were explained. The interviewers were also asked to comment the questions and propose adaptations where necessary. This helped to avoid misinterpretations of the questions. After this first discussion, the interviewers received some time to study the questionnaire in depth and to propose other adaptations. Then the interviewers discussed the translation of the questionnaires in local languages.
After the required adaptations to the questionnaire were done, the questionnaire was pretested with respondents similar to those that will participate in the actual survey, with the purpose to identify any possible or remaining problems (e.g. are the questions well understood, not too difficult to answer and are the respondents willing to answer the questions). The questionnaire was then again adapted where necessary (De Schampheleire and Van Looveren).
We started the interviews with 2 teams. When the first 2 interviewers felt self-confident enough, they could continue on their own and the 2 Belgian researchers formed 2 new Congolese interviewers, so that finally we worked simultaneously with 4 teams. With these 4 teams, 20 interviews were taken on average a day and this during 6 days a week. Regularly the 2 Belgian researchers, repeated supervision with a previously trained interviewer to minimize inconsistencies arising from fatigue of the interviewers (De Schampheleire and Van Looveren, 1995).
44 METHODOLOGY 3.2. Questionnaire
The questionnaire consisted of 4 parts (annex 2). The first part was a demographic and socio-economic part, the second part concerned questions about WEPs, the third part dealt with food security and in the last part a food frequency table was filled in and a 24-hour recall was carried out.
The first part concerned demographic and socio-economic information of the respondent, like marital status; age; education level; ethnical group; religion; activities; number of household members; income; owning a field, garden, livestock or other commodities; being connected to the water or electricity network; building materials of the house and house property.
The second part concerned knowledge and use of WEPs in their nutrition, and perceptions, advantages and disadvantages of WEP-consumption. Processing of this data was not the purpose of this master dissertation.
The third part regarded questions about food security. The HFIAS and CSI methods were combined in order to detect the best suitable method for the region of Kisangani. The HFIAS method has a standard set of 9 occurrence questions (see 2.1.6.1). For the CSI, the question list has to be adapted to the local circumstances. Therefore we organized 3 focus group sessions where we discussed the coping strategies proposed by Maxwell (2003). The focus groups consisted out of women who voluntary chose to participate. First, we tried to find out which coping strategies were used by the women of the study area. If some on the list were not applied by these women, they were omitted and others not on the original list were added. In a second step, the women ranked the coping strategies in 4 classes of severity according to the method proposed by Maxwell (2003). The coping strategy questions for the survey were then selected upon the result of the three focus group discussions, which were quite similar. The questions for the CSI method that were exactly the same as questions of the HFIAS, were of course not asked twice to the respondents.
The fourth part was about nutrition and had two parts: a food frequency and an interactive 24- hour recall. The food frequency in our survey was a 7-day recall of the individual consumption frequency of several foods and food groups. The food frequency table was constructed in that way that the FCS could be calculated (see 2.1.6.3) at the individual level. Because the FCS does not take into account food products consumed in quantities lower than 15 gram, the food frequency was split up according to products consumed in quantities higher and lower than 15 gram (Wiesmann et al., 2008). If some foods (e.g. WEPs) were consumed by the respondent but not mentioned in the list, these items were added to the list. Before starting the 24-hour recall, questions were asked about parameters influencing the nutrient requirements such as physical activity, personal weight, pregnancy (and semester), breastfeeding and child age. Also the kind of day (funeral, party, market day) or illness can influence eating behavior and is important to know when evaluating the results of the 24-hour recall. The different steps of the standardized 24-hour recall, as described by Gibson and Ferguson (1999) were followed (see 2.1.6.5).
At the end of the interview, the recall was reviewed to ensure that all the items were correctly recorded
45 METHODOLOGY (De Schampheleire and Van Looveren, 1995). Each respondent was visited twice, on different days. All the questions of the questionnaire, as explained above, were asked on the first day. The second visit included a repetition of the 24-hour recall, to take into account possible unusual food intake due to e.g. parties, funerals, illnesses, market days etc. (Gibson and Ferguson, 1999), and a ranking exercise with wild and domesticated plants, which is not further processed in this master dissertation. The survey time in the second round was much shorter. To thank the women for their participation, we gave them a pocket of salt.
46 METHODOLOGY 3.3. Data analysis
In this paragraph, we describe how the wealth index, based on socio-economic data, and the food security indicators were calculated. Subsequently, data processing of the 24-hour recall and construction of the food composition table for our research area will be explained, followed by a description of the food intake program. The food intake program generates nutrient intakes per person per day as output, which can then be compared with the required nutrient intakes according to FAO/WHO norms.
3.3.1 Wealth index
Traditionally, income was the preferred indicator of measuring the economic status. But measuring household income is extremely difficult for a number of reasons, not in the least, because people often are reluctant to reveal their income (Gwatkin et al., 2007).
That’s why proxy indicators (easier to measure), such as occupation or educational level, were used. But neither occupation or educational level is a direct reflection of the economic situation. People that are higher educated often are more respected but this does not ensure that higher education is accompanied with a higher income (Gwatkin et al., 2007).
Other proxy indicators used to measure economic status are consumption or expenditures but a large amount of time and effort is needed to gather reliable data. In addition, some problems arise, for example; in low-income countries, consumption is often difficult to validate because of in-kind transactions(Gwatkin et al., 2007).
In the late 1990s, it was found that information about readily-observable household characteristics produced the same results as the consumption or expenditure measures (Rutstein and Johnson, 2004). Readily-observable household characteristics could be summarized in an index, which is known as the Demographic and Health Survey (DHS) wealth index, this index is accepted as a reliable proxy indicator for consumption and hence for economic status more generally (Rutstein and Johnson, 2004).
Examples of goods and services that may be associated with wealth are presented in figure 3.2. All of them are having a different non linear relationship. In this example, the higher the wealth, the higher the number of households having a TV or fridge and the lower the proportion of households with surface water sources (lake, stream, pond) as drinking water. For motorcycle, the prevalence first increases and then decreases as wealth increases.
47 METHODOLOGY
Figure 3.2: assumed distribution of assets and services (Rutstein and Johnson, 2004)
For construction of the wealth index, indicator variables are thus needed, which are associated with the household’s economic situation. The decision of which items should be included in the wealth index is rather pragmatic. Including a broad variety of assets is best because this increases the variation across the household wealth scores and hence facilitates a more regular distribution of the households (Rutstein and Johnson, 2004). In table 3.1 a summary is given of the assets and services that are usually collected in DHS surveys .
Type of flooring Refrigerator Water supply Type of vehicle Sanitation facilities Persons per sleeping room Electricity Ownership of agricultural land Radio Domestic servant Television Country-specific items Telephone Table 3.1:assets and services usually asked about in DHS surveys (Rutstein and Johnson, 2004)
Most of the indicator variables are categorical variables. For each category a weight should be applied and most of the times this is not obvious. More information to construct the index is given in Rustein and Johnson (2004). In our research, the wealth index was constructed by assigning ad hoc weights to the 5 indicator variables as summarized in table 3.2. Higher weights reflect an increasing wealth. The weight of the variable:
• construction materials consisted of the sum of the weights for the materials used for the roof, wall, door and window, • public services was calculated by summing the weight of having electricity (weight 10 if yes) and having pipeline drinking water (weight 10 if yes), • assets were calculated by making the sum of the number of each asset multiplied by their weight, • land was calculated by summing the weight of having a garden (weight 10) and having a field (weight 20) and of • animals was calculated by making the sum of the number of domestic animals multiplied by their weight.
48 METHODOLOGY Construction materials Public services Assets Land Animals Category W Category W Category W category W category W Straw 1 Yes 10 Other 1 No 0 Other 1 Electricity Plate 2 No 0 Buggy 2 Garden 10 Chicken 1 Roof Tiles 3 Pipeline 10 Radio 3 Field 20 Goat 10 Other 4 Drinking Surface 0 Television 4 Sheep 10 Clay 1 Water water Computer 5 Pig 10 Adobe brick 2 source Bicycle 6 Cow 20 Wall Baked brick 3 Solar 7 Concrete 4 Panel No 0 Motorcycle 8 Branches 1 Whale 9
Door/ Bamboo 2 Boat Window Wood 3 Car 10 Metal 4 Electric 11 Other 5 Generator Table 3.2: Ad hoc weights (W) for the variables of the wealth index according to category
In this research the weights given to the variables were the same in both samples while according to Arimond and Ruel (2004), the scores should be derived separately for urban and rural areas because the assets that differentiate the better off from the worse off are likely to differ. In addition, we retained all the factors in the wealth index, while Arimond and Ruel (2004) restricted the index to the 2 factors contributing the most to the variability within an area.
3.3.2 Food security
To evaluate the coping strategies used, the HFIAS and CSI were calculated based on the coping strategies question list as described in 2.1.6.1. The food diversity was evaluated based on the analysis of the food frequency table. The FCS was calculated as described in 2.1.6.3. The HDDS, based on a 24-hour recall period and the complete household, was not calculated. Instead, we calculated a dietary diversity score (DDS), based on the 7-day recall food frequency table, on an individual level. The same 8 food groups were used for calculating both the FCS and DDS. The DDS is similar to the HDDS in the fact that it does not take into account the frequency of food consumption, includes foods consumed in small quantities (lower than 15 gram) and is an unweighted food frequency indicator (see 2.1.6.3). The DDS can be calculated either with data from a 7-day recall period or with data from a 24-hour recall period (Armond and Ruel (2004) and Savy et al. (2006)).
49 METHODOLOGY 3.3.3 The 24-hour recall
The objective of the 24-hour recall was to calculate the nutrient intake per person per day. Therefore we used the food intake program developed by the statistical and nutrition department of the Faculty of Bioscience Engineering of the University of Ghent. This program needs 2 kinds of input. First, a food composition table to transform food intakes into nutrient intakes and secondly the individual food intakes/day/interviewee.
3.3.3.1 Elaboration of a food composition table for use in Kisangani and Yaoseko
All nutrient values in our food composition table are expressed per 100 gram of food. Next to energy (in kcal), macronutrients, vitamins and minerals are included in our food composition table. The macronutrients that are present in the table are total protein (g), protein from meat, fish and poultry (g), fat (g), carbohydrates (g) and phytate (mg). The vitamins included in the table are the water soluble vitamins A (µg retinol equivalents), D (µg) and E (µg tocopherol equivalents) and the fat soluble vitamins C (mg), B1 or thiamin (mg), B2 or riboflavin (mg), B3 or niacin (mg), B6 (mg), B9 or folate (µg) and B12 (µg). The minerals in our food composition table are calcium (mg), phosphorus (mg), potassium (mg), sodium (mg), iron (mg), iron from meat, fish and poultry (mg) and zinc (mg).
At first the food composition table of Tanzania was used (Lukmanji et al., 2008). If a food item could not be found in this table, we used the “Food composition table for use in Africa” (Leung et al., 1968). If the food item was still not found in these two tables, ‘Le table de composition d'aliments du Mali’ from Barikmo et al. (2004) was used, followed by the Belgian food composition table (1999). The nutritional value of palm wine was found in the article of Cunningham and Wehmeyer (1987). No allowance for bio-availability was made in the different food composition tables. Bioavailability is defined as the proportion of a nutrient in food that is utilized for normal body function. Many nutrients, including iron, calcium, zinc, niacin and folate, are not totally absorbed and utilized. The nutritional values given are the actual values of the raw ingredients.
Most of the nutrient values in our food composition table are those for raw products. For some products, such as rice, plantain and cassava, the nutrient values were present for both the raw and the cooked product to correct for water loss or gain during the cooking process. The nutrient values of the cooked products in our food composition table were corrected for heat- and water-labile vitamin losses according to food group and cooking method. The USDA table of nutrient retention factors (USDA, 2007) and tables with percentage losses of vitamins on cooking in the Tanzania food composition table (Lukmanji et al., 2008) were used to do this correction.
50 METHODOLOGY 3.3.3.2 Average recipes
Because the local mixed dishes are prepared in very different ways, we decided mainly to work with the individual recipes instead of average recipes. This was possible because people in Kisangani and Yaoseko do not tend to have large stocks at home, but are used to visit the market every day before cooking. They remember very good the amount spend for the different ingredients. Often the women consumed the local mixed dishes outside home and hence did not know the ingredients of the recipe. That is why we constructed average recipes of most consumed local mixed dishes.
To construct an average recipe for local mixed dishes, five women in the study area were asked to cook each required recipe (Gibson and Ferguson, 1999). In our case, average recipes of the four most eaten vegetables (‘pondu’, ‘muchicha’, ‘matembele’ and spinach) and of beans were constructed.
First, all the raw ingredients for the recipe were listed, then each raw ingredient was weighed and the weight of any inedible part was subtracted. Next, the total uncooked weight of the recipe could be calculated by summing the weights of all edible portions of the raw ingredients. Then, the empty cooking container with lid was weighed and the dish cooked. The different steps of the cooking process were noted. After cooking the dish, the weight of the container with dish was taken. By subtracting the weight of the cooking container from the total weight, the total weight of the cooked dish is obtained. Then the weight of each raw ingredient is calculated as percentage of total cooked weight, which we call percentage weight of the raw ingredient (the weight of the raw ingredient per 100 gram cooked dish). This was done 5 times for each vegetable and the average recipe was then calculated by taking an average of the percentage weights of the raw ingredients in these 5 recipes. To make this clear, we will illustrate this with an example in table 3.3. The table shows percentage weights of each raw ingredient in the average recipe of ‘pondu’.
Ingredients of ‘pondu’ Percentage weights raw cassava leaves 42 raw aubergine 5 raw Welsh onion 3 raw celery 1 raw pepper 1 onion 4 palm oil 12 Salt 1 water 31 Table 3.3: Percentage weights of the ingredients in 100 gram ‘pondu’
We also calculated an average recipe for the staples ‘lituma’ (with plantains only), mixed ‘lituma’ (with plantains and cassava together), ‘fufu’ of cassava (with cassava flour only), ‘fufu’ of maize (with maize flour only) and mixed ‘fufu’ (with maize and cassava flour together) in the same way as for the local mixed dishes. The percentage of raw ingredients on the final cooked weight and water gain or loss could be calculated. The average recipes of 100 gram ‘lituma’, mixed ‘lituma’, ‘fufu’ of cassava, ‘fufu’ of maize, mixed ‘fufu’, ‘pondu’, ‘muchicha’, ‘matembele’, spinach and beans were then entered in the food intake program.
51 METHODOLOGY 3.3.3.3 The food intake program
The food intakes were entered in the food intake program per person, per recall and per hour. The program then converted the food intakes into nutrient intakes.
First the food composition table and the average recipes of 100 gram ‘pondu’, ‘muchicha’, ‘matembele’, spinach, beans, lituma’, mixed ‘lituma’, ‘fufu’ of cassava, ‘fufu’ of maize, mixed ‘fufu’ were entered in the food intake program.
When someone consumed beans or the vegetables ‘pondu’, ‘muchicha’, ‘matembele’ and spinach out of home, the amount consumed was estimated with the calibrated weight (small, medium, large) of the pictures in the photo book as indicated by the interviewee. The food intake program then calculated the nutrient values, by using the average recipes entered in the program. The output of the food intake program then gave the nutrient content of the total recipe as the sum of the nutrient values of the percentage weights of the different raw ingredients for all the nutrients present in the food composition table (energy, macro-and micronutrients) separately. For example, when someone consumed 120 gram of the cooked dish ‘pondu’, we entered 120 gram of the cooked dish ‘pondu’ into the program. The program first calculated the weight of each raw ingredient present in 120 gram of the cooked dish ‘pondu’, by multiplying the percentage weight of each raw ingredient with the total weight of the dish consumed. For instance, the percentage weight of raw cassava leaves is 42%, and 42% of 120 gram is 50.4 gram (table 3.4).
Ingredients Ingredients (g) in 120 Ingredients of ‘pondu’ (percentage weights) gram cooked ‘pondu’ raw cassava leaves 42 50.4 raw aubergine 5 6 raw Welsh onion 3 3.6 raw celery 1 1.2 raw pepper 1 1.2 raw onion 4 4.8 palm oil 12 14.4 salt 1 1.2 water 31 37.2 Table 3.4: Weight of the raw ingredients in 120 gram 'pondu'
Then the weight of each raw ingredient is multiplied by their nutrient content for every nutrient present in the food composition table and finally, the sum is made of these nutrient contents of all the cooked ingredients for every nutrient. The total protein content of 120 gram of the cooked dish ‘pondu’ is then equal to the sum of the protein content of 50.4 gram cooked cassava leaves, of 6 gram cooked aubergine, of 3.6 gram cooked ciboule, of 1.2 gram cooked celery, of 1.2 gram cooked pepper, of 4.8 gram cooked onion, of 14.4 gram palm oil, of 1.2 gram salt and of 37.2 gram water. The 50.4 gram raw cassava leaves, for instance, contain 1.865 gram proteins, this is deducted from the food composition table, in which we could find that 100 gram cooked cassava leaves contain 3.7 gram proteins (table 3.5). In the food composition table, all the nutrient values are expressed per 100 gram ingredient and nutrient data of uncooked ingredients were adjusted for vitamin losses during cooking (see 3.3.3.1).
52 METHODOLOGY Proteins (g) in 100 gram Proteins (g) in 120 gram Ingredients of ‘pondu’ cooked ingredients cooked ‘pondu’ cooked cassava leaves 3.7 1.865 cooked aubergine 1.0 0.060 cooked Welsh onion 1.8 0.065 cooked celery 0.69 0.008 cooked pepper 0.9 0.011 cooked onion 1.3 0.062 palm oil 0 0.000 salt 0 0.000 water 0 0.000 Table 3.5: Protein content of cooked ingredients in 100 gram cooked ingredients and 120 gram cooked pondu.
For the staples ‘fufu’ and ‘lituma’, the amount consumed was also estimated with the calibrated weight (small, medium, large) of the pictures in the photo book as indicated by the interviewee. The food intake program calculated the nutrient intake for the staples ‘fufu’ and ‘lituma’ in the same way as for the beans and vegetables consumed out of home, making use of the average recipes.
The staple foods rice, plantain and cassava were boiled to deduct the water gain or loss in the boiled staple foods and hence the percentage of raw product on total cooked weight. For instance, local raw rice takes up a lot of water and hence, the total weight of boiled rice is far above the weight of the raw rice. Rice, plantains and cassava were weighed on the market in both raw and cooked form. To estimate the amount consumed, we used the calibrated cooked weight (small, medium, large) of the pictures in the photo book as indicated by the interviewee. Rice, plantains and cassava were also present in the food composition table in both the raw and cooked form and hence, the food intake program could immediately convert the cooked food intakes into nutrient intakes.
For fruits and ready-to-eat food, the price-weight conversion list could be used to convert the monetary value into the weight of the edible part. Both the edible part of fruits and ready-to-eat food were present in the food composition table. The weights of the edible part were then entered into the food intake program and could be converted into nutrient intakes.
For the self-prepared mixed dishes, the total weight of each ingredient in the recipe (as mentioned in the 24-hour recall by the interviewee) was recorded in monetary value. The monetary value could then be easily converted to weights of the raw edible part of the ingredients by our price-weight conversion list. The amount of an ingredient the woman consumed individually was then calculated by multiplying the number of spoons she consumed and dividing by the total number of spoons prepared (as the woman indicated with the photo book). This percentage (consumed number of spoons/total prepared number of spoons *100) was then taken of all the raw ingredients. If the total number of spoons was not known, the adult equivalents were used to estimate the portion the mother consumed. According to Sterken (1998), the nutrient intake depends on the age of each person. The adult equivalents were calculated by making a sum of the number of household members each multiplied by their conversion factor (table 3.6, Sterken (1998)) depending on their age. Then the food intake of the woman was calculated by multiplying the weight of each raw ingredient with the conversion factor of the woman divided by the adult equivalents.
53 METHODOLOGY The weights of all the ingredients the woman consumed individually were then entered separately in the food intake program.
Age (years) conversion factor 0-5 0.3 6-14 0.65 14-60 1.0 >60 0.8 Table 3.6: Conversion factors (Sterken, 1998)
3.3.3.4 Evaluating the nutrient intakes
A. Evaluating energy and macronutrient intake
The energy intake per person and per recall were calculated by making the sum of the energy content of all foods consumed, per person and per recall, as calculated by the food intake program. The same is true for all the macronutrient intakes. The average energy and macronutrient intake of both recalls was then calculated. The second recall was missing for 10 women of city sample and for 2 women of village sample. For these women the second recall was equalized with the first one.
For further analysis of the macronutrient intake, it was expressed in two ways, in absolute values (grams) and in percentage of the total energy intake by converting the macronutrient intake from grams to kcal (4 kcal/g for protein and carbohydrates and 9kcal/g for fat) and then calculating the percentage of total energy intake for each macronutrient.
To evaluate the average energy and macronutrient intake, they were compared with the recommended energy and macronutrient intake.
The energy requirement was calculated by multiplying the physical activity level (PAL) grouped as light, moderate or heavy, with the basic metabolic rate (BMR) (WHO, 1985). The dietary energy requirements calculated in this way may only be applied on group level and not on individual level (FAO/WHO/UNU, 2001).
To estimate the BMR, predictive equations were used depending on gender, body weight and age (table 3.7, Schofield (1985)).
Age category Female BMR in kcal/day 10-17 13.384*W+692.6 18-29 14.818*W+486.6 30-59 8.126*W+845.6 >60 9.082*W+658.5 Table 3.7: Basic Metabolic Rate (Schofield, 1985); W= weight in kg
The PAL is split up in three categories according to a sedentary, active or vigorous lifestyle and is similar for men and women (table 3.8). The gender effect comes to expression when the BMR is
54 METHODOLOGY calculated because of the higher body weight of men.
PAL category PAL range PAL mid-point Light 1.40-1.69 1.55 Moderate 1.70-1.99 1.85 Heavy 2.00-2.40 2.20 Table 3.8: Physical Activity Level (FAO/WHO/UNU, 2001)
For pregnant or breastfeeding women, an extra amount of energy is needed. Energy requirements of pregnant women are population-specific, because of differences in body size, lifestyle and underlying nutritional status. Even within a society, there is a high variability in gestational weight gain rates, in energy expenditure of pregnant woman and hence in energy requirements. The energy cost is not equally distributed because the protein deposition, fat deposition, basic metabolic rate and total energy expenditure also differ according to the trimester of pregnancy. For a mean gestational weight gain of 12 kg, the extra cost during the first, second and third trimester of pregnancy are respectively 85 kcal/day, 285 kcal/day and 475 kcal/day (FAO/WHO/UNU, 2001).
The mean amount of breast milk produced daily is similar among different population groups (Prentice et al., 1986; Butte et al., 2002) so the main determinants of energy requirements for breastfeeding women are the duration of breastfeeding and extent of exclusive breastfeeding. During the first six months after delivery exclusive breastfeeding is recommended and complementary foods together with breastfeeding is recommended after 6 months (WHO, 2001). For well-nourished women the additional energy requirement during the first six months of lactation is 505 kcal/day (FAO/WHO/UNU, 2001), for the second six months the energy requirements depend of the rates of milk production. In this research, sometimes breastfeeding was even done till 24 months after delivery. Extra energy (505 kcal/day) was only taken into account for the first 12 months after delivery assuming that hereafter the amount of breastfeeding is low and the child eats mainly complementary food.
The macronutrient requirement has a minimum and maximum value and is a percentage of the calculated energy requirement (table 3.9).
Macronutrient Minimum % kcal Maximum % kcal Fat 15 30 Protein 10 15 Carbohydrate 55 75 Table 3.9: Recommended minimum and maximum percent energy contribution to total energy intake of fat, protein and carbohydrate intake (Kolsteren, 2010)
B. Evaluating the micronutrient intake
The micronutrient intakes per person and per recall were calculated by the food intake program by making the sum of the micronutrient content of all foods consumed per person and per recall.
Gibson and Ferguson (1999) describe different methods that are available to evaluate nutrient intakes but none of these methods can identify the individuals in a population who have a specific nutrient
55 METHODOLOGY deficiency, this is only possible by doing biochemical and clinical assessments together with dietary investigation.
The method we used in this research to evaluate nutrient intake is the “reference nutrient intake (RNI) as a cut-off value”, the RNI was set as a cut-off value and the percentage of individuals with a usual dietary intake of nutrients below the cut-off were calculated. This method can only be used if the food intake for each subject is measured over more than one recall (Gibson and Ferguson, 1999). The RNI’s are derived from measurements of nutrient requirements based on an age-and sex-specific population. The measurements result in a distribution that is available for a number of nutrients. The mean of the normal distribution is the average requirement of that population and the standard deviation is the variability of the average requirement for that population. The recommended dietary intake level is defined as the dietary intake level that meets the daily requirements of that nutrient for almost all (97-98%) apparently healthy individuals in that population (FAO/WHO, 2004). It is important to note that a nutrient intake below the RNI level does not necessarily mean that it is inadequate to meet the requirements of that individual because the reference level of nutrients (except for energy) exceeds the actual requirements of most individuals. The RNI’s used in this research were based on a vitamin and mineral requirements report of a joint FAO/WHO expert consultation (FAO/WHO, 2004). A disadvantage of this method is that the calculated percentage of individuals with intakes below the RNI will change according to the number of 24-hour recalls available for each individual. The size of the error in the prevalence estimate depends on the intrasubject variation in intake for any given nutrient and thus the number of recalls. In our research, we performed two 24-hour recalls per person. This permitted us to correct for the intrasubject variation by calculating the usually dietary intake of nutrients using the MSM program (EFCOVAL, 2009).
The MSM program can be used for estimating the usually dietary intake of nutrients for populations as well as for individuals. MSM calculates the dietary intake of nutrients for individuals first and then constructs the population distribution based on the individual data. The procedure of the MSM is briefly explained here. The estimation of the usual dietary intake occurs in three steps. First, the probability of eating a certain food on a random day is estimated for each individual. In the second step, the usual amount of food intake on a consumption day is estimated. In the first and second step, each time the data is first transformed to another scale to estimate the intra- and inter-individual variances. Then a correction is made for these variances before transforming the data back to the original scale. In the third step, the resulting numbers of the first two steps are multiplied by each other to estimate the usual daily nutrient intake for each individual.
Subsequently, the usual daily nutrient intakes calculated with the MSM program could be evaluated. This was done in two ways, first by using “the RNI as a cut-off value”- method as explained above. Secondly, the usual daily nutrient intakes were evaluated by calculating the nutrient adequacy ratio (NAR). The NAR is an index that evaluates the adequacy of the nutrient intakes and is the ratio of the intake of a nutrient, relative to the recommended intake. The NAR has been used for several times in research in Niger and Mali to evaluate the quality of the diet (Hatloy et al., 1998; Tarini et al., 1999 and Torheim et al., 2004).
56 METHODOLOGY 3.3.4 Programs used for statistical analysis
Before the data could be analyzed, the data were converted into codes. Therefore a codebook was set up. For every question, all possible answers were given a different code in the codebook. After all the answers of the questions were converted into codes, these codes were then entered in the program excel. Simple calculations of most of the data was done in excel
Data of the 24-hour recall were entered in the food intake program to convert food intake into nutrient intake. Each nutrient value was then calculated of the total intake per person for each recall. For the micronutrients, the usual intakes per person and per recall were calculated with the Multiple Source Method program based on the total micronutrient intake per person per recall. The macronutrient and energy calculations were done in excel.
For statistical analysis the statistical program S-PLUS was used. The basic information for statistics was found in the publication of Ottoy et al. (2005).
The test statistic was practically the same for all statistic tests. Starting from a representative sample, a null hypothesis (H0) was formulated which presumes no association.
Two types of tests were used, the parametric and non-parametric tests. For doing parametric tests, there are high assumptions. The variable should be measured on an interval or ratio scale and the sample distribution must be normal, which is the case if the variable is normally distributed in whole the population or the sample size is high enough (central limit theorem, minimal 30 cases). When the assumptions for parametric tests are not fulfilled non-parametric tests must be done which are less powerful and have a higher probability that a null hypothesis is wrongly not rejected.
The tests that were executed were the Pearson χ2-test (chi-square) test if the variables had a nominal or ordinal level, the Students t-test if the means of an interval or ratio variable were compared of two groups (nominal or ordinal variable). If the assumptions for the Students t-test were not fulfilled, the non-parametric Wilcoxon test was done.
If the means of an interval or ratio variable in different groups (nominal variable) were compared, an analysis of variance (ANOVA) test was done. If the assumptions for the ANOVA test were not fulfilled non-parametric tests were done. When the null hypothesis of the ANOVA test was rejected because of a significant F-value, a multiple comparison procedure, Tukey test, was executed to find which groups had significant different means.
Then finally when a linear association between two or more interval or ratio variables was analyzed, a linear model between a dependent (Y) and one (simple regression) or more independent variables
(Xi’s) (multiple regression) was created.
57 METHODOLOGY 4.RESULTS AND DISCUSSION
From now on, the women interviewed, belonging to the ethnic group Turumbu and living in the city of Kisangani are called “city sample” and the women interviewed, belonging to the ethnic group Turumbu and living in the village Yaoseko are called “village sample”.
In this chapter we first take a look at the socio-demographic profile of the households in our survey. In the second paragraph, we look if the food security situation of the city sample is significantly different from the village sample, by comparing the different food security indicators measured in both samples. Next, the number of food (in)secure households/women is calculated according to the different categories of each food security indicator to see which food security indicator classifies most of the households/women as food (in)secure. Finally, the influence of socio-demographic parameters on food security is tested. . The third paragraph first discusses the energy intake, macronutrient intake and contribution of the different foods/food groups consumed to every macronutrient based on the 24-hour recall. Subsequently, we analyze if the food security indicators are related to the energy intake. Next, the socio-demographic parameters and other factors influencing the energy intake are identified and finally, the macronutrient intakes as well as the comparison of the actual energy intake with the recommended energy intake are analyzed according to income category, age category and education level. Besides the energy and macronutrient intakes, also the micronutrient intakes are calculated based on the 24-hour recall. The micronutrient intakes in our samples are compared with the recommended nutrient intakes based on the FAO/WHO report (2004) and where possible the results of the (in)adequate micronutrient intakes are related with the consumption frequency of the most important foods in our research area.
4.1. Socio-demographic profile of households 4.1.1. Socio-demographic characteristics
Some socio-demographic characteristics discussed here, are summarized in table 4.1.
The mean age of women in the city sample is 44 years and significantly higher than in the village sample, where the mean age is 30 years.
The average household size is 9.4 and 8 in the city and village sample, respectively. In our research a household is a domestic unit consisting of the members of a family who live together even with nonrelatives such as servants. So a household does not only include the parents and their children but also other family members. This can explain the significantly higher household size as well as the higher adult equivalents in the city sample than in the village sample. For example, a nephew who was born in the village finds a job in the city and moves in with his uncle in the city. This is confirm to a 2007 survey (Ministère du Plan et Ministère de la Santé, 2008) where the household size in the urban
58 RESULTS AND DISCUSSION areas is slightly higher than in the rural areas.
As regards the education level of the women interviewed, almost no one is higher educated. Women that finished secondary school are the largest group (43%) in the city sample, while in the village sample, most of them finished only primary school (72%). It is remarkable that the percent of women without any education level in the city sample (21%) is more than double of those in the village sample (9%).
According to the marital status, most of the women belong to a monogamous household in both samples. Polygamy occurs 3 times more in the village sample (21%) than in the city sample (7%) and significantly more women are bachelor or widow in the city sample compared to the village sample.
The purpose of the sampling was to interview only women belonging to the ethnic group Turumbu, which was successful for the city sample. In the village Yaoseko, which is situated in the ‘Collectivité’ Turumbu, the nature territory of the Turumbu, 81.5% of the women belongs to the ethnic group Turumbu. This can be explained by the fact that some of these women are married to a Turumbu man but are themselves not Turumbu.
As regards the religion, most of the women in both samples are Protestant or Catholic or adepts of Simon Kimbangu. In the city sample, significantly more women belong to an “Eglise de reveille”.
Variable City sample Village sample p-value n (number of women interviewed) 122 130 mean age 44.1 ±14.8* 30.4±11.0* 0 average household size 9.4±5.0* 8±4.5* 0.03 adult equivalents 7.13±3.73* 5.67±3.28* 0 education (% women) Not educated 22.1* 9.2* 0 Primary school 34.4* 71.5* 0 Secondary school 42.6* 19.3* 0 Higher education 0.9* 0* 0 marital status Bachelor 9,8* 1,5* 0 (% women) Monogamy 51,6* 71,5* 0 Polygamy 6.6* 20.8* 0 Divorced 4,1 0,8 0.09 Widow 27,0* 5,4* 0 religion (% women) Protestant 33.6 43.8 0.10 Eglise de reveille 35.2* 18.5* 0 Catholic 16.4 13.8 0.57 Adepts of Kimbangu 4.1 10 0.07 Table 4.1: Socio-demographic characteristics of the city and village sample,*significant difference between both samples (T-test, α=0.05)
Next, we made a QQ-plot to check the age distribution of both samples and according to this distribution, three age categories were created for some further data analysis (table 4.2). Of all the women interviewed, there were significantly more women are older than 40 in the city sample, while in the village sample, significantly more women are younger than 20 and between 20 and 40 years old.
59 RESULTS AND DISCUSSION As mentioned above, the mean age of the women interviewed in the city sample is significantly higher than in the village sample.
Age category (years) % women city sample % women village sample p-value ≤ 20 7.4* 22* 0 21- 40 35.5* 58.3* 0 >40 57.1* 19.7* 0 Table 4.2: Women grouped in age categories, *significant difference between both samples (T-test, α=0.05) 4.1.2. Composition of the households
The households are characterized by their youth, in both samples, only a small percentage of men and women are older than 65 and most of the household members are younger than 15. The same results were obtained by the UNDP in 2008. The percent of children with age below five is higher in the village sample than in the city sample, this is conform to a survey conducted in 2007 (Ministère du Plan et Ministère de la Santé, 2008) (table 4.3).
Age Average% in Average % in the p-value Gender (years) the city sample village sample Men > 65 0,3 0,1 0.30 15-65 19,2 19,9 0.32 5 till 15 22,3* 14,8* 0 <5 6,4* 11,3* 0.03 Women > 65 0,9* 0,1* 0 15-65 24,1* 22,5* 0.02 5 till 15 21,3* 17,5* 0.01 <5 5,6* 13,8* 0 Table 4.3: Household composition for the city and village sample, *significant difference between both samples (T- test, α=0.05) 4.1.3. The wealth index
The wealth index of the city sample is 59.14±55.65 and is significantly higher (T-test with p- value=0.0056) than the wealth index of the village sample, which is 43.08±31.02. The wealth index is categorized in quintiles for further data analysis as recommended by Rutstein and Johnson (2004). The lowest quintile of households has a comparable wealth index in both samples while the highest wealth index quintile is higher for the city sample than the village sample (table 4.4). This suggests a higher wealth of the wealthiest households in the city sample than in the village sample (see 3.3.1).
Wealth index quintiles Wealth index city sample Wealth index village sample Lowest 20% 29 28 Second 20% 40 33 Third 20% 53 40 Fourth 20% 72 50 Highest 2 0 % 360 297 Table 4.4: Wealth index quintiles of the city and village sample
60 RESULTS AND DISCUSSION 4.1.4. Agricultural activities and income sources
Table 4.5 gives an overview of the percentage of households engaged in different agricultural activities in both samples. There are more households with a field and less households with a garden in the village sample compared to the city sample. The percentage of households that goes fishing, hunting, picking mushrooms and WEPs and collecting insects is higher in the village sample than in the city sample.
Agricultural activity % households city sample % households village sample Χ2 (p-value) Field 29.5* 94.6* 0 Garden 86.9* 39.2* 2*10^-14 Animal breeding 40,2 51,5 0.09 Fishing 12,3* 76,9* 0 Hunting 9,0* 68,5* 0 Picking mushrooms 49,2* 94,6* 2*10^-15 Picking WEP 45,1* 92,3* 2*10^-15 Collecting insects 34,4* 93,1* 0 Table 4.5: Agricultural activities in the city and village sample, *significant difference between both samples (Χ2-test)
The agricultural activities can serve for auto-consumption, sale or both, as shown in table 4.6. Products from the field are mostly sold and auto-consumed in both samples. Products from the garden, WEPs and insects are mostly auto-consumed in the city sample, while these products are mostly both auto-consumed and sold in the village sample. Mushrooms are mostly auto-consumed in both samples. The purpose of fishing and hunting is sale and auto-consumption in most households of both samples.
% households city sample % households village sample Purpose Auto-consumption Sale Both Auto-consumption Sale Both Activity Field 8.3 0 91.7 2.4 0.8 95.1 Garden 79.2 0 20.8 31.41 0 64.71 Picking WEP 83.6 1.8 14.5 42.5 6.7 50.8 Collect insects 54.8 0 45.2 10.7 1.7 87.6 Picking mushrooms 83.3 0 16.7 55.3 0 44.7 Fishing 46.7 0 53.3 16 0 84 Hunting 27.3 0 72.7 6.7 0 93.3 Animal breeding1 44.9 4.1 36.7 14.9 3 79.1 Table 4.6: Purpose of agricultural activity in the city and village sample, 1sum is not 100% because people that just started did not consume or sell anything
Table 4.7 presents the percentage of households that cultivate certain crops in the field. Except for tomatoes and rice, all other crops are cultivated by significantly more households in the village sample than in the city sample, which can be explained by the fact that a significantly higher percentage of households in the village sample owns a field.
61 RESULTS AND DISCUSSION % households % households p-value Crops city sample village sample Cassava 22.1* 62.8* 0 Maize 19.7* 61.5* 0 Leafy vegetables 20.5* 78.4* 0 Plantain banana 14.8* 39.3* 0 Potatoes 9.0* 39.3* 0 Tomatoes 13.1 50.8 0.51 Rice 7.4 9.3 0.58 Other 18.9* 37.7* 0 Table 4.7: Cultivated crops in the city and village sample, *significant difference between both samples (T-test, α=0.05) 4.1.5. Annual household monetary income
The mean annual household monetary income and most executed primary and secondary activity by women of both samples is presented in table 4.8. The mean annual household income in the city sample is significantly higher than in the village sample. On average, the income per household per year is 1391 US$ and 856 US$ in the city and village sample respectively, this is around 116 US$ and 71 US$ per month per household in the city and village sample respectively. According to the World Bank (2008), the minimum salary per person is 15 US$ a month in the public sector and around 50 US$ in the private sector. So if we convert the salary according to the world bank from individual to household level (7.1 and 5.7 adult equivalents in the city and village sample, respectively), we get a minimum salary of 107 US$ and 86 US$ in the city and village sample, respectively and a maximum salary of 355 US$ and 285 US$ in the city and village sample, respectively. So, in the city sample, the income is just above the minimum salary and in the village sample, the income is even lower than the minimum salary according to the world bank. A reason for this can be that the income in our research is underestimated because the women were the ones who estimated the household income and often they do not know the income of the other household members, they may not say it, etc.
The three most executed primary and secondary activities are housewife, agriculture (working in the field and/or garden) and small businesses. The main primary activity in the city sample is a small business, while this is agriculture in the village sample. In both samples, most of them have no secondary activity.
City sample Village sample p-value Mean annual household income (US $) 1391± 1426* 856 ± 1171* 0 Housewife 17.2* 2.3* 0 Primary activity Agriculture 13.1* 78.5* 0 (%women) Small business 45.9* 8.5* 0 No 83.6* 56.2* 0 Secondary activity Agriculture 7.4 4.6 0.36 (% women) Small business 2.5* 25.4* 0 Table 4.8: Mean annual household income (US$), primary and secondary activity in the city and village sample, *significant difference between both samples (T-test, α=0.05)
62 RESULTS AND DISCUSSION Subsequently, the women are split up into income categories, according to income quartiles, for some further data analysis (table 4.9).
Income Income Mean annual household income Mean annual household income quartile category (US$) interval in the city sample (US$) interval in the village sample Lowest 25% 1 <398 <128 Second 25% 2 >398 till 904 >128 till 502 Third 25% 3 >904 till 2082 >502 till 994 Last 25% 4 >2082 >994 Table 4.9: Income quartiles in the city and village sample 4.1.6. Pregnancy and breastfeeding
Of all the women interviewed, almost halve of them are breastfeeding in the village sample, which is significantly higher than in the city sample (table 4.10).
% women % women p-
city sample village sample value Pregnant Total 4.10 10 0.07 First trimester 0.82 0.77 0.96 Second trimester 0.82* 6.15* 0.02 Third trimester 2.46 3.08 0.77 Breastfeeding Total 14.75* 47.69* 0 Table 4.10: Pregnant and breastfeeding women in the city and village sample, *significant difference between both samples (T-test, α=0.05)
63 RESULTS AND DISCUSSION 4.2. Food security indicators
In this part, we look first if the food security situation of the city sample is significantly different from the village sample, by comparing the different food security indicators measured in both samples. To see which food security indicator classifies most of the households/women as food (in)secure, the number of food (in)secure households/women is calculated according to the different categories of each food security indicator. Finally, the influence of socio-demographic parameters on food security indicators is tested.
4.2.1 Comparison of the food security indicators in the city and village sample
All the food security indicators calculated indicate that the food security situation between the city and village sample is significantly different(table 4.11).
The CSI and the HFIAS are significantly higher in the village sample than in the city sample. A higher CSI of the village sample means that these households use more coping strategies than households of the city sample (Maxwell et al., 2003). While a higher HFIAS means that the households of the village sample have a lower access to food than those of city sample (Coates et al., 2007). Hence, both the CSI and the HFIAS indicate that the households of the village sample are less food secure than those of the city sample.
The lower FCS and/or DDS of the village sample compared to the city sample means that women of the village sample have a lower food diversity and hence are less food secure than those in the city sample (Swindale and Bilinsky, 2006 and Wiesmann et al., 2008).
All the measured food security indicators give the same result: the households/women in the village sample are less food secure than those in the city sample.
Indicator City sample Village sample T-test (p-value) CSI 55.25±37.21* 79.69±43.85* 0 HFIAS 8.16±4.39* 10.38±4.55* 0.0001 FCS 58.38±20.34* 52.64±17.55* 0.018 DDS 6.47±0.92* 6.00±0.46* 0 Table 4.11: Food security indicators in the city and village sample, *significant difference between both samples (T- test, α=0.05)
64 RESULTS AND DISCUSSION 4.2.2 The number of food (in)secure
In this paragraph, the number of households/women that are food (in)secure are calculated according to the different categories of the food security indicators measured. The food security indicators are categorized using certain cut-off values as explained below.
The cut-off values used for categorizing the CSI are chosen arbitrarily as shown in table 4.12. In the city sample, half of the households interviewed belong to the lowest CSI category (=the least making use of coping strategies), which is the category of the most food secure households. In the village sample, almost half of the households belong to the second category and are hence less food secure compared to the city sample. The percentage of households belonging to a certain CSI category decreases starting from the first category for the city sample and starting from the second category for the village sample, which means that the worse the CSI category (= using more coping strategies and hence being less food secure) the lower the percentage of households that belongs to that CSI category.
Food security % households % households p-value Cut-off Category indicator city sample village sample CSI 0-50 most food secure 1 53.3* 25.4* 0 51-100 2 36.9 47.7 0.08 101-150 3 8.2* 18.5* 0.02 >150 least food secure 4 1.6* 8.4* 0.04 Table 4.12:Percentage of households in different CSI categories, *significant difference between both samples (T-test, α=0.05)
The different HFIAS categories are calculated as explained in 2.1.6.1 (table 4.13). In contradiction to the CSI, most of the households in both samples belong to the severely food insecure category (= the least access to food) and the percentage of households belonging to a HFIAS category increases as the HFIAS category gets worse (=less access to food and hence less food secure).
Food security % households % households p-value Category indicator city sample village sample HFIAS food secure 5.7 4.6 0.69 mildly food insecure 7.4* 1.5* 0.03 moderately food insecure 29.5 25.4 0.47 severely food insecure 57.4 68.5 0.07 Table 4.13: Percentage of households in different HFIA categories, *significant difference between both samples (T-test, α=0.05)
The three food consumption groups are calculated according to the WFPs method as explained in 2.1.6.3 (table 4.14). In our research area, there is a high palm oil consumption (see 4.3 and 4.4), so the cut-off values for high sugar-oil consumption are better suited to separate the women in the different food consumption groups. The results are similar to those obtained with the CSI except that there are no significant differences between both samples. The first similarity is that in both samples, most of the women belong to the
65 RESULTS AND DISCUSSION acceptable group (= highest food diversity) and hence, are more food secure. Secondly, the percentage of women belonging to a food consumption group decreases as the food consumption group is worser (=lower food diversity and hence being less food secure).
Food consumption % women % women p-value Cut-off Category group city sample village sample Acceptable >42 most food secure 74.4 65.9 0.15 Borderline 28.5-42 22.3 27.9 0.31 Poor 0-28 least food secure 3.3 6.2 0.28 Table 4.14: Percentage of women according to the Food consumption groups, *significant difference between both samples (T-test, α=0.05)
The average diversity of the 33 percent households with highest diversity is taken as a target to calculate the three categories of the DDS as explained in the 2.1.6.3 (Table 4.15). The amount of women with a high diversity equals the amount of women with a middle diversity (p-value=0.07) in the city sample and is much higher than those with low diversity. In the village sample, the amount of women with high diversity and with low diversity (p-value=0.7) is the same and much lower than those with middle diversity. Similar to the CSI and WFPs method, the lowest percentage of women belong to the least food secure category.
Number of Category % women % women p- DDS food groups city sample village sample value High diversity 7-8 food groups most food secure 51.64* 11.54* 0 Middle diversity 6 food groups 40.16* 78.46* 0 Low diversity 0-5 food groups least food secure 8.20 10 0.62 Table 4.15: women grouped in different categories of food security indicators, *significant difference between both samples (T-test, α=0.05)
We can conclude that there is a big difference in the percentage of women/households being food (in)secure between the HFIAS and the other food security indicators. According to the CSI, the WFPs method (that measures the FCS) and the DDS, the percentage of women/households that belongs to the least food secure category is the lowest and the percentage of women/households that belongs to the most food secure category is the highest (except DDS in village sample). The opposite is true for the HFIAS, which classifies the lowest percentage of households as the most food secure and the highest percentage of households as the least food secure. After comparing these food security indicators with energy intake (see 4.3.2), we will give a suggestion about which indicator is the best one to use depending on the goal of measurement.
66 RESULTS AND DISCUSSION 4.2.3 Socio-demographic parameters influencing food security
In this paragraph, the influence of socio-demographic parameters on food security is tested with ANOVA tests and significance level α=0.05.
The CSI is significantly influenced by the education level (p-value=0.022) and income (p- value=0.0014) of women in the city sample. In the village sample, neither education level or income influences the CSI. Women in the city sample that finished primary school use more coping strategies than those that are not educated and hence are less food secure (Maxwell et al., 2003). Households of the second income quartile in the city sample also use more coping strategies than those of the lowest income and those of the higher income quartiles.
The HFIAS is significantly influenced by income (p-value=0.029) and marital status (p-value=0.006) in the city sample but not in the village sample. Households in the city sample of the highest income quartile have a lower HFIAS and hence a higher access to food (more food secure) than those of the second income quartile. Women of the city sample that are divorced have a higher HFIAS and hence lower access to food ( less food secure) than bachelors.
The FCS is significantly influenced by income in the city sample (p-value=0.024) as well as in the village sample (p-value=0.041). The FCS of households in the highest income quartile is significantly higher than the second income quartile in both samples. So women of the highest income quartile have a higher food diversity and are hence more food secure than those of the second income quartile. Women of the city sample that finished secondary school have a significantly higher FCS than those that are not educated or finished primary school and hence are more food secure. In the village sample, age (p=0.012) also significantly influences the FCS. Women between 20 and 40 years old have a higher FCS and are hence more food secure than those older than 40.
The DDS is significantly influenced by education level (p-value=0.052) in the city sample. In the village sample, none of the socio-demographic parameters influences the DDS. Women who finished secondary school have a significantly (p-value=0.021) higher DDS than those that finished primary school and are hence more food secure.
We can conclude that not all the food security indicators are influenced in the same way by the socio- demographic parameters and that the influence is also different between both samples. For the village sample, only the FCS is influenced by income and age. While in the city sample, all the indicators are influenced by some socio-demographic parameters.
67 RESULTS AND DISCUSSION 4.3. Energy and macronutrient intake based on the 24-hour recall
In the previous paragraph, households/women were categorized as more or less food (in)secure according to the food security indicators. These indicators are based on coping strategies or food frequency question lists and hence, can be quickly implemented but do not tell anything about the dietary intakes. This is why, we also did a 24-hour recall, based on dietary intakes, which is still the best but most time-consuming method to analyze the nutritional situation of households/women. First we look what the energy intake, macronutrient intake and contribution of the different foods/food groups consumed to every macronutrient is in both samples based on the 24-hour recall. Subsequently, we look if the food security indicators, based on the coping strategy or food frequency list, are related with the energy intake, based on the 24-hour recall. This relation together with the number of food (in)secure households/women belonging to each food security category (as calculated in 4.2.2), helps us to decide which indicator, depending on the goal of the measurement, is best suited. Next, the socio-demographic parameters and other factors influencing the energy intake are identified and finally, the macronutrient intakes as well as the comparison of the actual energy intake with the recommended energy intake are analyzed according to income category, age category and education level.
4.3.1 Comparison of the energy and macronutrient intake in the city and village sample
The energy intake per person per day is 1812±887 kcal on average in the city sample and is not significantly different from the village sample, where the energy intake is on average 1883±834 kcal per person per day(T-test, p-value=0.51). Although, the energy intake of 1800 kcal per person per day in this research is higher than the 1486 kcal per person per day in the food balance sheet of DRCongo (FAO, 2005), it does not meet the recommended energy intake as calculated in our research (see 4.3.5) and is still below the daily recommended energy intake of 2300 kcal as defined by the FAO (PNUD/UNOPS, 1998).
We first look at the energy contribution of the macronutrients (figure 4.1) and compare this with the recommended minimum and maximum percentages of total energy intake (Kolsteren, 2010). The highest energy intake comes from carbohydrates in both samples and is significantly higher in the village sample (52.9%) compared to the city sample (49%) (T-test, p-value=0.01). Although the carbohydrate intake has the highest energy contribution, it does not meet the recommended minimum percent of 55 in both samples.
Proteins have the lowest energy contribution in both samples and this energy contribution is significantly higher in the city sample (8.8%) than in the village sample (6.9%) (T-test, p-value=0). The protein energy contribution is lower than the recommended minimum percent of 10 for both samples and may be explained by the low consumption of animal products.
68 RESULTS AND DISCUSSION The energy contribution of fat, on the other hand, exceeds the recommended maximum percent of 30 in both samples (42.2% and 40.2 % in the city and village sample respectively) and is not significantly different between both samples (T-test, p-value=0.18). As we will show later, the high palm oil consumption in our research area is mainly responsible for this high fat consumption.
energy energy from from protein protein 8,8 6,9 energy energy from energy from energy carbo- from carbo- from hydrate fat hydrate fat 49,0 42,2 52,9 40,2
Figure 4.1: Macronutrient energy contribution(%) in the city sample (left) and the village sample (right)
Next, we take a look at the sources of energy in both samples in terms of foods and food groups (figure 4.2). At first sight, we see that most of the energy is contributed by oils and fats, followed by roots and tubers, followed by cereals in the city sample and mostly by roots and tubers, followed by oils and fats in the village sample. A striking difference between both samples is the, almost 4 times higher, energy contribution of cereals in the city compared to the village sample and the, almost 2 times higher, energy contribution of roots and tubers in the village sample compared to the city sample. Fish, meat, poultry, eggs (F,M,P,E) and milk and legumes, pulses and nuts, which are all protein rich foods, contribute more to the energy intake in the city sample compared to the village sample. The energy contribution of vegetables is slightly higher in the village sample compared to the city sample. Alcoholic beverages and fruits contribute respectively 3 and 2 times more to the energy intake in the village compared to the city sample. Although the energy contribution of mushrooms, bush meat and WEPs is low in both samples, all of them are higher in the village compared to the city sample. Mushrooms and bush meat contribute 2,3 times and WEPs even 10 times more to the energy intake in the village sample compared to the city sample.
Subsequently, the energy contribution of the different foods/food groups in our research is compared with the 2005 food balance sheets for DRCongo (FAO, 2005) (see figure 2.6). It must be kept in mind that our research only concerns a limited area in DRCongo while the food balance sheets are situated on country level and that these food balance sheets only have a low reliability and underestimate the apparent food consumption (see 2.2.1.3). As regards the energy contribution of cereals, this equals the 20% of the 2005 food balance sheets in the city sample; but is much lower in the village sample, only 5.31%. On the other hand, as concerns the energy contributed by roots and tubers, the village sample approaches more the 58% of the 2005 food with its 44% compared to the city sample, where it is only 24.36 %. Also the energy contributed by fruits and alcoholic beverages (3.21% and 1.51% respectively) in the village sample is similar to the 3% and 1.5% respectively of the food balance sheets, while this is much lower in the city sample (1.67% and 0.46% respectively). The lower energy contribution of alcoholic beverages in the city
69 RESULTS AND DISCUSSION sample may be due to the fact that alcohol consumption in the city sample is underreported because in people in the city sample are more ashamed to say that they drink alcohol. With regard to legumes, pulses and nuts the energy contribution in the city sample (6.77%) is much higher and in the village sample (0.81%) much lower compared to the 2005 food balance sheets (2%). Finally, the energy contribution of oil and fat, which is mainly palm oil, is 3 times higher in our research (35%) compared to the 2005 food balance sheets (11%). The energy contribution of vegetables is higher in our research (2.65% and 3.31% in the city and village sample, respectively) compared to the 2005 food balance sheets (1.1%) and of F,M,P,E and milk is higher in the city sample (3.95%) and lower in the village sample (1.45%) compare to the 2005 food balance sheets (2.2%).
Figure 4.2: Energy contribution (%) of different foods in the city sample (left) and the village sample (right), with F,M,P, E being fish, meat, poultry, eggs
Now, we will look what the contribution is of the different foods to every macronutrient. The main food sources of carbohydrates (figure 4.3) are roots and tubers (43%), followed by cereals (36%) in the city sample and roots and tubers (79%) in the village sample. While the carbohydrate contribution of roots and tubers is much higher in the village than in the city sample, cereals and legumes, pulses and nuts have a higher carbohydrate contribution in the city than in the village sample. Although only contributing in a low amount, the carbohydrate contribution of mushrooms and WEPs is respectively 2.5 and 22 times higher in the village compared to the city sample.
Figure 4.3: Carbohydrate contribution of different foods in the city sample (left) and the village sample (right)
70 RESULTS AND DISCUSSION Concerning the main food source of fat (figure 4.4), this is obviously oil and fat (which is mainly palm oil) in both samples, 77% and 85% in the city and village sample respectively. Other sources contributing the most to the fat intake are legumes, pulses and nuts, roots and tubers and F,M,P,E and milk in the city sample and fruit in the village sample. This time the mushrooms, bush meat and WEPs have respectively, a 3, 2.2 and 4.25 times higher fat contribution in the village sample than in the city sample.
Figure 4.4: Fat contribution of different foods in the city sample (left) and the village sample (right)
As regards the protein contribution of the different foods, we immediately see that there is a more equally contribution of the different foods to the protein intake in contradiction to the other macronutrients, but we still notice a difference between both samples (figure 4.5). The most striking differences are the higher protein contribution of F,M,P,E and milk, cereals and legumes, pulses and nuts in the city compared to the village sample. In the village sample, on the other hand, the protein contribution of vegetables, roots and tubers and bush meat are much higher in the village compared to the city sample. For the first time, caterpillars also have a clear contribution to the protein intake and this in both samples. The mushrooms, bush meat and WEPs have a respectively 3.7, 3.3 and 9.1 times higher protein energy contribution in the village sample than in the city sample.
Figure 4.5: Protein contribution of different foods in the city sample (left) and the village sample (right)
71 RESULTS AND DISCUSSION We can conclude that the higher protein energy contribution of the city sample compared to the village sample is caused by the higher intake of cereals, F,M,P,E and milk and of legumes, pulses and nuts because they contribute more to both the energy and protein intake in the city sample (see figures 4.2 and 4.5). With regard to the village sample, the roots, tubers and vegetables are responsible for the higher carbohydrate energy contribution compared to the city sample. This can be explained by the fact that the percentage of households that cultivates cassava, potatoes, leafy vegetables and tomatoes is significantly higher in the village than in the city sample (see table 4.6) and that people in the village sample consume mostly their own cultivated crops and because cereals are more expensive than roots and tubers, people in the village sample prefer to sell the cereals and consume the roots and tubers. several reasons. The fat energy contribution was not significantly different between both samples and the fat contribution of oils and fats (palm oil) was very high in both samples, which is quite obvious. Finally, although only consumed in small quantities, the energy contribution of mushrooms and bush meat is 2.3 times and of WEPs 11.4 times as high in the village sample compared to the city sample (figure 4.2). This can be explained by the fact that more people in the village sample pick mushrooms, go hunting and pick WEPs in the village sample compared to the city sample (see table 4.5).
72 RESULTS AND DISCUSSION 4.3.2 Relation between the food security indicators and energy intake
The average energy intake of women in the city and village sample is 1812 ± 887 kcal and 1883 ± 834 kcal per day respectively (see 4.3.1). The relation between this energy intake and the food security indicators is tested here for each indicator separately with a linear regression model. The results are presented in table 4.16.
Sample Food security indicator b-value Standard error R2 F-test (p-value) City sample HFIAS -50.1 17.9 0.06 0.0059 CSI -4.5 2.1 0.04 0.0368 FCS 15.4 3.8 0.12 0.0001 DDS 312.6 83.6 0.11 0.0003 Village sample HFIAS -41.2 15.8 0.05 0.0102 CSI -3.5 1.6 0.03 0.0348 FCS 18.6 3.9 0.16 3.9*10^-6 DDS 234.0 159.1 0.02 0.1438 Table 4.16: Linear regression models of food security indicators with energy intake
The linear regression models shows that there is a significant linear relation between every food security indicator and energy intake except for the DDS in the village sample. The b-values for both the HFIAS and the CSI are negative, which means that a the higher the HFIAS and CSI, the lower the energy intake. This can be explained by the fact that a higher HFIAS means a lower access to food and hence, being less food secure. While a higher CSI means using more coping strategies and hence, being less food secure. On the other hand, the b-values of both the FCS and DDS are positive, in other words, the higher the FCS and DDS, the higher the energy intake. A higher FCS and DDS are associated with a higher food diversity and being more food secure. So a higher food diversity is associated with a higher energy intake, which is in accordance with Wiesmann et al. (2008) and Swindale and Bilinsky (2006). We can conclude that all the indicators associate being less food secure with a lower energy intake.
Although these significant relations, the association in these linear models is rather weak because of the low determination coefficients (R2). In other words, other factors, excluding the food security indicators, have an important influence on the energy intake. This can be socio-demographic parameters (see 4.3.3), perceptions about eating (eating more is more healthy), religion (fasting period), etc.
To know if there is a difference in influence on the energy intake between the different categories of the food security indicators, the indicators are converted into categorical variables (see tables 4.12 to 4.15) and the relation with energy intake is tested with an ANOVA test. Except for the categorical CSI, all other indicators significantly influence the energy intake (p-values= 0.010, 0.001 and 0.029 respectively) in the city sample. While in the village sample, only the categorical CSI and FCS have a significant effect on energy intake (p-values=0.01 and 0 respectively). To know which levels of the food security indicators are significantly different in energy intake, the
73 RESULTS AND DISCUSSION Tukey test for the multiple comparisons of means is used.
As regards the CSI in the village sample, the energy intake of women belonging to the second CSI category (1721±729 kcal) and the highest CSI category(1408±405kcal) is significantly lower than those of the lowest CSI category (2267±981 kcal). This is conform to our expectations of the CSI because women belonging to the lowest CSI category, use the least coping strategies and are hence the most food secure. These results show that the group that uses the coping strategies the least have the highest energy intake. Concerning the HFIAS in the city sample, women that are severely food insecure (1622±783 kcal) have a significant lower energy intake than those that are mildly food insecure (2373±860 kcal) and those that are food secure (2466±1078 kcal). In other words, women that are less food secure have a lower energy intake than those that are more food secure.
As regards the DDS in the city sample, the energy intake of women belonging to the high food diversity group (2028±912 kcal) is significantly higher than women of the low food diversity group (1057±318kcal). In other words, women with a higher dietary diversity have a higher energy intake. But we must be careful with the interpretation of the DDS because according to Wiesmann et al. (2008), the DDS underestimates the number of calorie deficient women because it takes into account both foods consumed in quantities higher and lower than 15 gram. Regarding the FCS, women belonging to the acceptable group have an energy intake of 1913±917 kcal and 2087±867kcal in the city and village sample respectively , which is significantly higher than those at the borderline with an intake of 1582±747 kcal and 1491±580 kcal in the city and village sample respectively, that is significantly higher than those of the poor category with 1168±735 kcal in the city sample. In other words, women belonging to the acceptable food consumption group, which are having the highest food diversity and are hence the most food secure, have the highest energy intake.
The conclusion is that all indicators give as result that the most food secure households/women have the highest energy intake. The choice which indicator to use depends on the goal of the measurement. In an emergency situation on the one hand, the goal is to identify the least food secure. The CSI, WFPs method and DDS are then the best methods. According to these methods, the percentage of households/women belonging to the least food secure category is the lowest (see tables 4.12, 4.14 and 4.15). Hence, we are certain that the ones identified as least food secure are certainly the least food secure. However, an effort is necessary to make a question list adapted to the local situation when using the CSI. Although making this list requires some time, the methods based on coping strategies as well as the food diversity methods are the most effective ones to use in an emergency situation, because they are less time-consuming compared to dietary intake methods (see 2.1.6).
In case of a nutrition research on the other hand, for example, when we want to identify the ones that are food secure, the HFIAS is the best method. The HFIAS classifies the lowest percentage of households as the most food secure (see table 4.13). Hence, we are certain that the ones classified as food secure are certainly food secure. The HFIAS uses a fixed standard list of questions and is stronger to compare different regions with each other (see 2.1.6).
74 RESULTS AND DISCUSSION 4.3.3 Socio-demographic parameters and other factors influencing energy intake
First, the influence of age and income on energy intake is tested with a linear regression model for age and income separately(table 4.17).
Sample Parameter b-value Standard error R2 F-test (p-value) City sample Age -23.8 5.05 0.1565 7*10^-6 Income +0.0002 0 0.1114 0.0002 Village sample Age -16.6 6.62 0.05 0.0133 Income 0.0002 0.0001 0.03 0.0390 Table 4.17: Linear regression models with energy intake
The linear regression model shows that there is a significant linear relation between age and energy intake and between income and energy intake in both samples. The b-values for age are negative, which means that older people have a lower energy intake than younger people, while the b-values for income are positive, meaning that the higher the income, the higher the energy intake. Although the determination coefficient (R2) is higher for the linear models of the city sample than the models of the village sample, the value is still low and hence, the linear association is weak. Only 15.65% and 11.14% of the variance is explained by the models of age and income respectively in the city sample and only 5 and 3% respectively in the village sample.
Next, the influence of the socio-demographic parameters on energy intake is tested with an ANOVA test. Again, age and income are tested, to see which age and income categories effect the energy intake, but for doing an ANOVA test, age and income must be converted into categorical variables (see table 4.2 and table 4.9). The results are presented in table 4.18. In the city sample, marital status, income category, age category and education level all significantly influence energy intake. While in the village sample, only age category influences the energy intake. To know which levels of the above mentioned variables are significantly different in energy intake, the Tukey test for the multiple comparisons of means is done.
Sample Variables F-value p-value City sample Marital status 4.81 0.0005 Income category 5.33 0.0018 Age category 8.82 0.0003 Education level 7.44 0.001 Village sample Age category 3.22 0.04 Table 4.18: ANOVA test of socio-demographic parameters influencing energy intake
Regarding the marital status in the city sample, women belonging to a polygamous household have a significant higher energy intake (3692±1276kcal) than women who are not married (1749±1105kcal), widows (1416±534kcal) and divorced women (1379±719kcal), while women belonging to a
75 RESULTS AND DISCUSSION monogamous household still have a significant higher energy intake (2022±892kcal) than widows (1416±534kcal). Neither of the women that are not married, widow or divorced have a husband. Because in DRCongo, the husband is mostly responsible for a big part of the income, the lower energy intake of these women may be explained by the fact that they do not have a husband and hence, less money to buy food. This may also explain why in the village sample marital status does not influence the energy intake. Most of the foods consumed by the people of the village sample are foods of their own cultivation and hence they do not depend on their husband to get money for buying food.
Women belonging to a household of the highest income category have a higher energy intake (2347±1113kcal) than those of the third (1595±819kcal) and lowest income category (1565±660 kcal) in the city sample. Women of the richest households in the city sample may have more money to buy food and hence, have a higher energy intake. Income does not influence the energy intake in the village sample because of the auto-consumption.
As regards the education level in the city sample, the energy intake is significantly higher of women that finished secondary school (2147±970 kcal) compared to women without any education (1480±663kcal) and those that that only finished primary school(1594±781kcal). So a higher education level, and hence having more knowledge results in a higher energy intake in the city sample.
Concerning the age categories, women older than 40 in the city sample have an energy intake of 1541±724kcal, which is significantly lower than the energy intake of women between 20 and 40 years old in the city sample (2173±980kcal). Women older than 40, in both the city (1541±724kcal) and village sample (1549± 698 kcal). also have a significant lower energy intake than women younger than 20 in both the city (2266±833 kcal) and the village sample (2180±783 kcal). The reason why older women have a lower energy intake can be explained by the fact that the older people are, the less energy they need, the less they are pregnant, the less they are breastfeeding, maybe they are less active and hence less hungry, etc.
The influence of other factors, such as pregnancy, breastfeeding, physical activity level and kind of day (market day, funeral, illness, birthday etc.) on energy intake is tested with an ANOVA test. Surprisingly, there is no significant influence of the physical activity level and pregnancy on energy intake in both samples. Only the breastfeeding women have a significant higher energy intake (2262±944 kcal) than non-breastfeeding women (1733±861 kcal) in the city sample (ANOVA-test, p- value=0.019).
76 RESULTS AND DISCUSSION 4.3.4 Macronutrient intake according to income, age and education level
We already know that most of the energy is contributed by carbohydrates, followed by fats and that the lowest energy contributor is the macronutrient protein in both samples (see 4.3.1). Also we saw that the carbohydrate energy contribution is significantly lower in the city sample compared to the village sample and that the opposite is true for the protein energy contribution, while the energy contributed by fat is not significantly different between both samples. Here, we will look more into detail, how many women actually have a macronutrient energy contribution lower than the minimum recommended amount, higher than the maximum recommended amount and between the minimum and maximum recommended amount. This is done per sample for all the women together and also for each sample per income category, age category and education level.
4.3.4.1 Comparison of the macronutrient intake according to income category
First, the percentage of women, with a protein, fat and carbohydrate intake above the maximum, between the maximum and minimum and under the minimum recommended amount is calculated according to the different income categories for both samples (table 4.19). The majority of the women has a protein intake below the recommended amount and a fat intake above the recommended amount independent from income category in both samples. As regards the carbohydrate intake, most of the women have a carbohydrate intake lower than the recommended amount, independent from the income category in city sample. While in village sample, the majority of the women belonging to the two lowest income categories (categories 1 and 2) have a carbohydrate intake below the recommended amount and the majority of the women belonging to the two highest income categories (categories 3 and 4) have a carbohydrate intake according to the recommended amount.
Income categories city sample Income categories village sample Macro- Level 1 2 3 4 1 2 3 4 Nutrient (n=25) (n=31) (n=31) (n=30) (n=30) (n=59) (n=7) (n=32) Protein >15% 1.71 0.85 2.57 0.85 0 0.78 0 0 10- 7.69 5.13 4.27 11.11 3.13 5.47 0 4.69 15% <10% 11.97 20.51 19.66 13.68 20.31 39.84 5.47 20.31 Fat >30% 17.95 23.93 25.64 22.22 21.88 34.38 4.69 19.53 15- 2.56 2.56 0.86 3.42 1.55 9.38 0.78 4.69 30% <15% 0.86 0 0 0 0 2.34 0 0.78 Carbo- >75% 1.71 0.85 0 1.71 0.78 3.91 0 2.34 hydrate 55- 6.84 11.97 5.13 8.55 9.37 17.97 3.13 12.5 75% <55% 12.82 13.67 21.37 15.38 13.28 24.22 2.34 10.16 Table 4.19: Macronutrient energy contribution according to income category (% of women)
77 RESULTS AND DISCUSSION As calculated before (see 4.3.3), only in the city sample, the income category significantly influences the energy intake. Now, we look which macronutrient intakes are responsible for these significant differences in energy intake according to the income categories within the city sample. To detect any significant differences an ANOVA test is done with significance level α=0.05 and subsequent Tukey tests to find which categories are significantly different.
We see that women of the highest income category have a significant higher fat and carbohydrate intake than women with of the 3rd and of the lowest income category, which is in accordance with the differences in energy intake (see 4.3.3). As regards the protein intake, women of the highest income category have a significant higher protein intake than those of all the other income categories (table 4.20).
So, the intake of all macronutrients is higher in the highest income category than in the lowest income category. This means that richer people consume higher quantities of the same food, and not that they increase the dietary diversity by consuming other/better food products. The fact that richer people consume higher quantities of the same foods means that, if there is a nutrition problem, then it is still a problem of insufficient energy intake. Because when people have more money, in the first step they buy more of the same food. Once they have a sufficient energy intake and again have more money, only then they will increase the dietary diversity by buying other and better food products, for example, animal products which are rich in proteins.
The quantity of each macronutrient is, although not significantly, higher in income category 2 than 3, while we would expect it the other way around. This can be explained by the fact that these categories can not really be distinguished from each other, so it would have been better to take category 2 and 3 together.
Sample Macronutrient Income category P- (gram) 1 (lowest) 2 3 4 (highest) value City Protein 37.7±20.82 38.3±20.32 35.7±22.92 55.9±25.11 0.002 sample Fat 74.3±40.82 89.4±44.6 83.5±51.42 122.5±80.11 0.010
Carbohydrate 201.3±84.52 237.1±93.4 179.2±83.02 276.9±111.71 0.001
Table 4.20: Macronutrient intake according to income categories in the city sample, 1, 2 different numbers meaning significant difference in the same row 4.3.4.2 Comparison of the macronutrient intake according to to age category
Here, we also first calculated the percentage of women with a protein, fat and carbohydrate intake above the maximum, between the maximum and minimum and under the minimum recommended amount is calculated, but this time according to the age categories for both samples (table 4.21). The majority of the women have a protein intake below the recommended amount and a fat intake above the recommended amount, independent of the age category in both samples. Regarding the carbohydrate intake, the majority of the women in the city sample have a carbohydrate intake below
78 RESULTS AND DISCUSSION the recommended amount independent of the age category, while in the village sample, the main part of the women between 20 and 40 years old have a carbohydrate intake below the recommended amount .
Age categories city sample Age categories village sample Macro- Level ≤ 20 21-40 >40 ≤ 20 21-40 >40 Nutrient (n=6) (n=46) (n=69) (n=14) (n=88) (n=25) Protein >15% 0 1.65 4.13 0 0 0 10-15% 1.65 10.74 14.88 0 7.88 5.51 <10% 3.31 25.62 38.02 11.02 61.42 14.17 Fat >30% 4.96 37.19 48.76 8.66 56.69 15.75 15-30% 0 0 8.26 2.36 10.24 3.15 <15% 0 0.83 0 0 2.36 0.79 Carbo- >75% 0 0.83 3.31 0 5.51 1.57 Hydrate 55-75% 0 11.57 19.83 5.51 26.77 10.24 <55% 4.96 25.62 33.88 5.51 37.01 7.88 Table 4.21: Macronutrient intake according to age category (% of women)
As calculated before (see 4.3.3), the energy intake of both women in the city sample and in the village sample is influenced by age category. To detect which macronutrient intakes explain these significant differences in energy intake according to the age categories an ANOVA test is done with significance level α=0.05 and subsequent Tukey tests to find which categories are significantly different. As regards the fat intake, women older than 40 have a significantly lower fat intake than women between 20 and 40 years old and than women younger than 20 in both samples. This in accordance to the differences in energy intake (see 4.3.3). As regards the protein and carbohydrate intake, women older than 40 have a significant lower intake than women between 20 and 40 years old in the city sample(table 4.22).
So the intake of all macronutrients is lower for oldest women, and hence this means that they eat less of the same food but do not diminish the dietary diversity.
Sample Macronutrient Age category P- (gram) ≤ 20 21-40 >40 value City sample Protein 46.2±24.4 49.3±24.82 35.6±21.21 0.007 Fat 129.2±48.52 115.9±68.92 73.2±42.41 0.0001 Carbohydrate 261.7±97.7 256.1±106.52 194.9±88.11 0.003 Village sample Fat 100±45.12 95.9±57.42 67.3±35.51 0.048 Table 4.22: Macronutrient intake according to age category in the city and village sample, 1,2 different numbers meaning significant difference in the same row 4.3.4.3 Comparison of the macronutrient intake according to education level
Again, the percentage of women that take in proteins, fats and carbohydrates above the maximum, between the maximum and minimum and under the minimum recommended amount is calculated for both samples, but now according to education level for both samples (table 4.23). The majority of the
79 RESULTS AND DISCUSSION women have a protein and carbohydrate intake under the recommended amount and a fat intake above the recommended amount, independent of education level in both samples.
Macro- Education level city sample Education level village sample Level Nutrient 1 (n=26) 2 (n=42) 3 (n=53) 1 (n=12) 2 (n=91) 3 (n=24) Protein >15% 2.5 2.5 0.8 0.8 0.0 0.0 10-15% 5.0 8.3 15.0 1.5 10.0 1.5 <10% 14.2 24.2 27.5 6.9 61.5 17.7 Fat >30% 18.2 32.2 39.2 5.4 60.7 14.6 15-30% 3.5 2.8 3.3 1.5 10.0 4.6 <15% 0.0 0.0 0.8 2.3 0.8 0.0 Carbo- >75% 0.8 0.8 2.5 1.5 3.9 1.5 Hydrate 55-75% 8.3 12.5 10.8 3.1 33.0 7.7 <55% 12.5 21.7 30.0 4.6 34.6 10.0 Table 4.23: Macronutrient intake according to education level (% of women)
The energy intake is significantly different between the different education levels in the city sample, but this is not the case for village sample (see 4.3.3). To detect which macronutrient intakes explain these significant differences in energy intake according to the education levels, an ANOVA test is done with significance level α=0.05 and subsequent Tukey tests to find which categories are significantly different.
The results show that women that finished secondary school have a significantly higher carbohydrate intake than women that finished primary school or non educated women in the city sample, this is in accordance to the differences in energy intake (see 4.3.3). As regards the fat intake, women that finished secondary school have a significant higher fat intake than non-educated women (table 4.24).
Sample Macronutrient Education level P- (gram) no 1 2 value City Fat 69.8±44.72 85.8±60.6 107.7±59.61 0.018 sample Carbohydrate 191.8±82.92 182.6±69.92 264.2±110.71 0 Table 4.24: Macronutrient intake according to education level in the city sample, 1,2 significant difference in the same row We can conclude that all the macronutrients intakes are significantly higher of the richest people in the city sample compared to those with a lower income. Hence, richer people consume higher quantities of the same foods and do not have a higher dietary diversity. This shows that there is still a problem of insufficient energy intake. All the macronutrient intakes are significantly lower in the city sample and the fat intake is significantly lower in the village sample for the women older than 40 compared to the women younger than 40. And those that finished secondary school in the city sample have a significantly higher fat intake than those that are not educated and significantly higher carbohydrate intake than those that finished primary school and those that are not educated.
80 RESULTS AND DISCUSSION 4.3.5 Comparison of the energy intake with the recommended energy intake
In this paragraph, we first compare the actual energy intake with the recommended energy intake. Next, we split them up by age category, income category and education level, to see who, according to these the categories has a significant lower actual than recommended energy intake.
The recommended energy intake is calculated for both recalls by multiplying the BMR with the physical activity level (see 3.3.3.4) and the actual energy intake is calculated by taking the mean of the energy intake of both 24-hour recalls. The actual energy intake is not significantly different between both samples but significantly lower than the recommended energy intake in both samples (table 4.25). The recommended energy intake for the village sample is higher than the daily recommended energy intake of 2300 kcal as defined by the FAO (PNUD/UNOPS, 1998) and is significantly higher than for city sample (T-test, p-value=0). This can be explained by the higher PAL of the women in the village sample. The higher PAL is due to the fact that women in the village work more on their field/garden, walk longer distances to get potable water, etc.
Actual energy Recommended energy T-test Sample intake (kcal) intake (kcal) (p-value) City sample 1812±887* 2294±432* 0 Village sample 1883±834* 2564±381* 0 Table 4.25: T-test, α=0.05, to compare the actual and recommended energy intake, * significant difference between the actual and recommended energy intake
Subsequently, a two-sample T-test is done to compare the actual energy intake with the recommended energy intake according to age categories, income categories and education level for each sample separately.
As concerns the age categories, in the city sample only women older than 40 have an actual energy intake significantly lower (1541±724kcal) than the recommended energy intake (2234±381kcal). In village sample both women older than 40 and women between 20 and 40 have an average energy intake (1549±699 and 1824±855kcal respectively) lower than the recommended energy intake (2472±402 and 2617±353kcal respectively).
According to the income categories, except for the highest income category in city sample, the actual energy intakes are significantly lower than the recommended energy intakes for all income categories. In other words, only the richest households in the city sample have a sufficient energy intake. All the other categories that are poorer, have less money to buy food and hence an energy intake lower than recommended intake.
As regards the education level, except for the women that finished secondary school in the city sample, the energy intake is significantly lower than the recommended energy intake for every education level.
81 RESULTS AND DISCUSSION 4.4. Micronutrient intake based on the 24-hour recall
Besides the energy and macronutrient intakes, also the micronutrient intakes are calculated based on the 24-hour recall. The main objective of this part is to compare the micronutrient intakes of our samples with the recommended nutrient intakes based on the FAO/WHO report (2004). Therefore we first present these recommended micronutrient intakes and where possible relate the results of the (in)adequate micronutrient intakes with the consumption frequency of the most important foods in our research area.
4.4.1 The recommended micronutrient intakes
In this paragraph, we make use of the usual intakes (as explained in 3.3.3.4) to determine the percentage of women with inadequate micronutrient intake. First we calculated the percentage of individuals with intakes below the cut-off (=FAO/WHO recommended intakes). Secondly we calculated the NAR as the ratio of the micronutrient intake, relative to the recommended micronutrient intake (as explained in 3.3.3.4). To compare the percentage of women with inadequate intake in the city and village sample, T-tests are done with significance level α=0.05.
Because pregnant and breastfeeding women have different nutrient requirements, all the women that are pregnant or breastfeeding a child till 12 months are excluded from the micronutrient analysis. In the city sample and village sample, there are respectively 5 and 13 pregnant women and 11 and 38 women breastfeeding. The sample size of the city and village sample remains thus 97 and 77 respectively.
The RNI values used in this research are based on a vitamin and mineral requirements report of a joint FAO/WHO expert consultation (FAO/WHO, 2004). Because of the age difference in the city and village sample, the requirements of some nutrients are different as presented in the table 4.26.
Vit Vit Vit Vit Vit Vit Vit Vit Vit Sample Folaat Ca P K Na Fe Zn A D E C B1 B2 B3 B6 B12 µg µg TE mg mg mg mg Mg µg µg Mg mg mg mg mg mg RE RNI city 500 7.5 7.5 45 1.1 1.1 14 1.4 400 2.4 1150 550 3500 1600 20.35 9.8 sample RNI village 500 5 7.5 45 1.1 1.1 14 1.3 400 2.4 1000 550 3500 1600 29.4 9.8 sample Table 4.26: Recommended micronutrient intakes for non-pregnant and non-breastfeeding women (FAO/WHO, 2004)
82 RESULTS AND DISCUSSION 4.4.2 The consumption frequency of most important foods
In table 4.27, an overview is given of the consumption frequency of the most important foods of our research area during the past seven days for both samples separately. When both samples are compared, we see that the women in the city sample consume less frequently cassava leaves, fruits, poultry, palm oil and palm wine and more frequently rice, beans, groundnuts, pulses, banana, avocado and milk powder compared to the village sample. To try to explain these difference, 2 columns are added to the table. In the “weight for 100 kcal” column, the amount that should be consumed of each food item (in gram) to take in 100 kcal of this food item is calculated on the basis of our food composition table for use in Kisangani and Yaoseko . In the “price for 100 kcal” column, the weight to reach a 100 kcal intake of each food item is converted into a monetary value (CF=Congolese frank, with 1 US$=770 CF) by our price-weight conversion list. The prices are the same for the city and village sample, except for the green leafy vegetables, the prices in the column should be divided by two for the village. The monetary value for 100 kcal is calculated because the people in our research area often reason in monetary values to decide whether or not they will buy something.
As concerns the staples, cassava is the most frequently consumed staple in both samples. This can be explained by the fact that the price for 100 kcal of cassava is the cheapest of the all staples mentioned, maize disregarded. The reason for the lower consumption frequency of rice in the village than in the city sample can be the highest price of rice for 100 kcal compared to the other staples and as we mentioned before the people in the village sample will rather sell cereals and consume the cheaper roots and tubers (see 4.3.1). With regard to the pulses, groundnuts are most frequently consumed in both samples and again, the price for 100 kcal of groundnuts, which is the lowest of all pulses mentioned, can explain this higher consumption frequency. Green leafy vegetables and palm oil, are consumed daily in both samples. Palm oil is the cheapest food source of all foods mentioned in table 4.27 to take in 100 kcal. Palm wine has a significantly higher consumption frequency in the village sample than in the city sample, this can be explained by the fact that the palm wine produced in the village sample only has a low marketability since you have to drink it fresh and since the transport facilities do not permit the fresh palm wine to arrive in unspoiled condition in the city. Milk powder which is less available in the village, is hence, less frequently consumed in the village compared to the city sample. Finally, although significantly more people in the village sample go fishing, hunting and collecting insects (as discussed above, see table 4.6), the consumption frequency of meat, fish and insects is not significantly higher in the village sample than in the city sample. This may be due to the fact that the purpose of fishing, hunting and collecting insects in most of the households is not only auto-consumption but also sale (as discussed above, see table 4.7).
This table gives an idea about the food consumption frequency and can, as mentioned in 2.1.6.3, tell something about the food security of the household, but does not say anything about the quantity of the food consumed.
83 RESULTS AND DISCUSSION Food group Food item Consumption Consumption p- Weight Price frequency frequency value for 100 (CF) for city sample village sample kcal 100 kcal Staples Maize 3.2 2.5 0.06 28 11 Rice 3.3* 0.6* 0 63 43 Cassava 8.6 9 0.47 76 12 Plantain 2.9 3.3 0.23 91 39 Total 18.6 17.5 0.28 Pulses Beans 1.4* 0.3* 0 30 25 Groundnut 3.3* 2.4* 0.003 18 19 Soya 0.3 0.2 0.4 24 56 Total 4.7* 2.9* 0 Green leafy Cassava 0.004 3.4* 4.1* 270 711 vegetables leaves Sweet potato 0.52 1.9 1.8 286 501 leaves Amaranth 0.97 435 1411 1.7 1.7 Spinach 323 1361 Total 6.8 7.4 0.21 Vegetables Tomatoes 3.8 3.0 0.19 476 265 Mushroom 0.8 1.3 370 Total 11.6 10.4 0.10 Fruit Banana 2.4* 1.6* 0.006 112 272 Avocado 1.3* 0.5* 0 63 76 Total 6.3* 9* 0 Eggs Total 0.7 0.5 0.43 65 Poultry Total 0.3* 0.6* 0.046 50 70 Meat and Total 0.29 3.8 4.4 fish Oils and fats Palm oil 6.1* 6.7* 0.03 12 5 Alcoholic Palm wine 0.003 0.4* 1.1* 79 beverages Sugar Sugar/honey 3.9 3.6 0.43 26 40 Insects Caterpillars 3.5 3.8 0.30 60 139 Milk Milk powder 1.4* 0.6* 0.005 20 163 Table 4.27: Comparison of consumption frequency of most important foods in the city sample and village sample (T- test, α=0.05), weight (g) and price (CF) for 100 kcal of the most important foods with CF=Congolese Frank, 1the price for 100 kcal of green leafy vegetables should be divided by two for the village sample., * significant difference in consumption frequency between both samples
84 RESULTS AND DISCUSSION 4.4.3 Comparison of the usual and the recommended micronutrient intake
Here we compare the usual and recommended micronutrient intakes. When the actual micronutrient intake is significantly lower than the recommended micronutrient intake, it “may” be due to a lower consumption frequency of certain foods rich in those particular vitamins and minerals, if that lower consumption frequency is accompanied by a total lower intake of foods rich in those particular vitamins and minerals. Another and even more important reason can be the fact that the total energy intake is lower than the recommended one (as calculated in 4.3.5). In the city and village sample the actual energy intake is respectively 79% and 73% of the recommended energy intake (see 4.3.5). Hence, it is logical that the intakes of many micronutrients will be lower than the recommended micronutrient intakes.
A first remark for the following discussion is that when foods rich in particular vitamins and/or minerals are mentioned for our research area, this is based on the vitamin and mineral content of these foods in our food composition table for use in Kisangani and Yaoseko. A second remark is that all the micronutrients are expressed in absolute weights and not relative to the total energy intake. Because the total energy intake in both samples is not significantly different, the micronutrient intakes in absolute values may be compared between both samples.
4.4.3.1 Fat-soluble vitamins
VITAMIN A
Vitamin A plays a role in night vision, for the differentiation process of epithelial tissues, for reproduction, etc. Vitamin A deficiency is the most important cause of blindness in children, is an important cause of stunting and contributes to an overall increase in mortality (Kolsteren, 2010).
The most important sources of vitamin A in the region are palm oil, green leafy vegetables (in order from higher to lower content: spinach, cassava leaves, sweet potato leaves and muchicha), tomatoes, plantain banana, fruits (papaya and mango) and animal sources such as egg yolk and caterpillars.
Vitamin A is expressed in microgram retinol equivalents and all women have an intake of vitamin A higher than the recommended amount (table 4.28). The very high vitamin A intake is also reflected in the high NAR, which is higher than the NAR of any other nutrient. Both the usual intake and NAR are significantly higher in the village sample than in the city sample (both p-values=0).
As shown in table 4.27, palm oil and green leafy vegetables are consumed almost daily in both samples and the frequency of palm oil, cassava leaves and fruit consumption of women in the village sample is higher than in the city sample and may explain their higher vitamin A intake and NAR.
Furthermore we can say that palm oil, being the best source of vitamin A in our research area, is also the cheapest source of energy (see table 4.27), this results not only in too high fat intakes but also in
85 RESULTS AND DISCUSSION too high vitamin A intakes. As mentioned above palm oil is responsible for almost 35% of the energy intake and 80% of the fat intake (see figures 4.2 and 4.4 respectively). To meet the recommended vitamin A intake, only 10 grams of palm oil should be consumed per day but in our research, one women consumes on average 74 grams of palm oil per day, which contains 642 kcal and 7 times as much vitamin A than is recommended. Hence, we should look if there is no risk for vitamin A toxicity. Because vitamin A is fat soluble and can be stored, primarily in the lever, a routine consumption of large amounts of vitamin A supplements, namely a daily intake of 7500 microgram for 6 years, can result in toxic symptoms, including liver damage, headaches, vomiting, bone abnormalities, etc. However, the high vitamin A intake in this research does not reach the daily 7500 microgram and does not come from vitamin A supplements but from the ingestion of food sources and this is rarely associated with toxicity (FAO/WHO, 2004).
VITAMIN D
Vitamin D is derived from cholecalciferol, which results from the conversion of pre-vitamin D in the skin by ultra-violet light. Vitamin D deficiency is caused primarily by insufficient exposure to sunlight and results in rickets or osteomalacia (Kolsteren, 2010).
The richest food sources of vitamin D are fish, egg yolk and milk powder but the main source of vitamin D is personal production (Kolsteren, 2010.)
Vitamin D is expressed in micrograms and as can be expected from the very low NAR-value (table 4.28), which is even the lowest of all nutrients, all women have an inadequate intake of vitamin D. Although women of the city sample have a significant higher vitamin D intake than those of the village sample (p-value=0.005), the NAR between both samples is not significantly different. This may be explained by the lower RNI of the village sample compared to the city sample.
The inadequate intake of vitamin D is due to the low amount of milk powder and fish consumed. Of milk powder only, one should consume daily 86 gram and 57 gram in the city and village sample respectively to reach the recommended vitamin D intake, while their actual milk powder intake is respectively 2.31 and 0.10 grams. The higher intake of vitamin D in the city sample can be explained by their more frequent milk powder consumption compared to the village sample (see table 4.27).
Although the insufficient intake of foods rich in vitamin D, we can not conclude that there is a vitamin D deficiency problem because of the high exposure of their skin to ultra-violet light, contributing to the personal production of vitamin D.
VITAMIN E
Vitamin E is, together with vitamin C, an important anti-oxidant, the only difference is that vitamin E is lipophilic and therefore found in membranes and lipoproteins, while vitamin C is water-soluble and therefore found in the aqueous phase (FAO/WHO, 2004).
86 RESULTS AND DISCUSSION Groundnuts, sweet potatoes, palm oil, soya, milk powder and green leafy vegetables (mainly spinach and sweet potato leaves) are good sources of vitamin E in the region.
Vitamin E is expressed in α-tocopherol units. A large percentage of women have an inadequate intake of vitamin E, 88.7 and 84.4% for the city and village sample respectively but the usual intake and NAR for vitamin E is not significantly different between both samples (table 4.28).
The intakes are not significantly different because there is no difference in consumption frequency of the staples, soya and green leafy vegetables, which are vitamin E rich foods consumed in the highest amounts. Although the consumption frequency of groundnuts and palm oil is significantly higher in the village sample and of milk powder significantly higher in the city sample, this does not result in a significant higher vitamin E intake, just because they are only consumed in a small quantities. One should consume 83 gram of groundnuts or 188 gram of palm oil or 370 gram of milk powder daily to reach the recommended vitamin E intake, which are all unreasonable high intakes. This is why dietary diversification is so important.
Vitamin A Vitamin D Vitamin E Sample (µg RE/day) (µg/day) (mg TE/day) City RNI 500 7.5 7.5 sample intake VITAMIN C Vitamin C or ascorbic acid is necessary for the formation of collagen, which binds cells together. People that are deficient in vitamin C develop scurvy. They have fragile capillaries, poor scar formation and slow healing of the wounds (Kolsteren, 2010). Green leafy vegetables (from higher to lower vitamin C content: amaranth, cassava leaves, spinach, sweet potato leaves) and fruits (papaya, lemon, orange, pineapple, mango…), are two rich sources of vitamin C consumed by women in the region. Vitamin C is expressed in milligrams in table 4.29. All women of the village sample have a vitamin C intake higher than the recommended amount and their usual intake is significantly higher than city sample (p-value=0), in which 10.3% has an inadequate vitamin C intake. This significant difference is also reflected in the higher NAR value of the village sample than the city sample (p-value=0). 87 RESULTS AND DISCUSSION The green leafy vegetables are consumed daily by the women in both samples as shown in table 4.27. The ten percent of women with inadequate intake in the city sample can maybe be explained by the lower consumption frequency of cassava leaves (3.4 and 4.1 times a week in the city and village sample respectively) and/or fruit (6 times a week compared to 9 times a week in village sample). The lower consumption of cassava leaves in the city sample compared to the village sample may be due to the availability. Most of the people in the village sample consume the cassava leaves they cultivated while most of the people in the city sample have to buy them if they want to eat them. VITAMIN B1 AND VITAMIN B6 Vitamin B1 or thiamine is highly soluble in water and has coenzyme functions in the metabolism of carbohydrates. People that are deficient in vitamin B1 develop Beri Beri (Kolsteren, 2010). Vitamin B6 has coenzyme functions in the metabolism of amino acids and glycogen. Vitamin B6 deficiency usually occurs in association with a deficit of other B vitamins (FAO/WHO, 2004). In our research area, beans, maize, maize flour, cassava flour and groundnuts are rich in both vitamins while soya, sweet potato leaves and amaranth are very rich sources of vitamin B1 and cassava, cassava leaves, bananas, milk powder, meat and fish are very rich in vitamin B6. Milling of the cereals, which is for example the case for white rice and white bread, destroys vitamin B1 (Kolsteren, 2010). The intake of both vitamin B1 and vitamin B6 is expressed in milligrams. The intake of both vitamins is significantly lower in the city sample than in the village sample (both p-values= 0.02), as well as the NAR values (p-values= 0.02 and 0.0002 respectively). In the city sample, 75.3 and 68 % of the women have an inadequate intake of vitamin B1 and B6 respectively, while this is lower in the village sample with 63.6 and 44.2% respectively (table 4.29). Although the frequency of consumption of groundnuts, beans, banana and milk powder is even higher in the city sample than in the village sample (table 4.27), this does not result in a higher intake of vitamin B1 or B6. The inadequate intake can be explained by the fact that huge amounts of for example groundnuts or beans or milk powder should be consumed to have an adequate vitamin B1 intake, namely 366 or 550 or 550 gram respectively. The reason for the higher intake of vitamin B6 in the village sample may be the lower RNI in the village sample compared to the city sample and/or the higher consumption frequency of cassava leaves in the village sample. VITAMIN B2 Vitamin B2 or riboflavin has coenzyme functions in oxidation and reduction reactions. Vitamin B2 deficiency occurs almost always in combination with a deficiency of other B vitamins and is mainly caused by inadequate dietary intake (Kolsteren, 2010). The richest sources of vitamin B2 in the diet of Congolese women are green leafy vegetables ( amaranth and sweet potato leaves), milk powder and soya. The vitamin B2 intake is expressed in milligram and for the first time the amount of women with an 88 RESULTS AND DISCUSSION inadequate intake of vitamin B2 is higher in the village sample than in the city sample (60% versus 24%) but their intake and NAR are not significantly different (table 4.29). Although the consumption frequency of milk powder, a vitamin B2 rich source, is higher in the city sample compared to the village sample, this does not result in a significant difference in intake of vitamin B2. We can explain this by the fact that milk powder is consumed only in a low amount compared to the consumption of amaranth, sweet potato leaves and soya, which are other rich sources of vitamin B2 with no difference in consumption frequency between both samples (table 4.27). With our food composition table we could calculate that one should consume 100 gram of milk powder but only 15 gram of amaranth and 37 gram of sweet potato leaves to have an adequate vitamin B2 intake. VITAMIN B3 Vitamin B3 or niacin plays a role in cellular oxidative processes. The niacin present in cereals is bound and has hence a low bio-availability. Meat, poultry and fish are products with a high niacin bioavailability(FAO/WHO, 2004) but these are only consumed in a small amount in our research area. Although the essential amino acid tryptophan can be converted into niacin in the human body, people with a monotonous maize based diet can develop niacin deficiency because the maize protein zein is very deficient in tryptophan. Vitamin B3 deficient people develop pellagra with diarrhea, dermatitis and dementia (Kolsteren, 2010). The vitamin B3 intake is expressed in milligram and the usual intake and NAR of vitamin B3 does not differ between city and village sample and almost all women have an inadequate intake (table 4.29). The fact that almost all women have an inadequate vitamin B3 intake is due to the fact than only low amounts of meat, poultry and fish, all products with a high niacin bio-availability, are consumed. The consumption frequency of meat and fish is not significantly different between both samples and although poultry is more often consumed in the village than in the city sample, this does not result in a difference in vitamin B3 intake because of the low amount of poultry consumed (table 4.27). FOLATE Vitamin B9 or folate deficiency is common in people that consume a limited diet. Pregnant and breastfeeding women are at risk to develop folate deficiency because pregnancy and lactation increase the folate requirement. The risk of fetal neural tube defects increases 10-fold when women go from an adequate to poor folate status during pregnancy. Spina bifida is a neural tube defect which results from improper closure of the spinal cord and is one of the most common congenital abnormalities associated with folate deficiency (FAO/WHO, 2004). Good sources of folate in the region are pulses (from higher to lower content: beans, cowpea, soya, groundnut), green leafy vegetables (from higher to lower content: spinach, cassava leaves, amaranth, sweet potato leaves), avocado and milk powder. 89 RESULTS AND DISCUSSION The folate intake is expressed in microgram and inadequate for all women of the city sample and is together with the NAR significantly (both p-values=0) lower than the village sample, in which almost all women (96%) have an inadequate intake (table 4.29). This illustrates that a higher frequency of consumption can not guarantee a higher amount consumed because in the city sample, products rich in folate, such as pulses, avocado and milk powder are more often consumed than in the village sample, but the usual intake of the city sample is significantly lower than in the village sample. Only the higher consumption frequency of cassava leaves in the village sample than in the city sample can explain the higher folate intake. But to reach the recommended folate intake by the consumption of cassava leaves only, one should consume 513 gram of cassava leaves, which is unreasonable high. VITAMIN B12 Foods rich in vitamin B12 are meat, fish, eggs and milk powder. The vitamin B12 intake is expressed in microgram (table 4.29). Almost all women of both samples have an inadequate vitamin B12 intake but the intake and NAR is significantly higher in the city sample than in the village sample (both p- values=0). The inadequate intake of vitamin B12 is because of the low amounts of meat, fish, eggs and milk powder consumed. The only source rich in vitamin B12 that is more often and in higher quantities consumed in the city than in the village sample is milk powder, 2.3 gram and 0.1 gram per person per day in the city and village sample respectively. This difference probably explains the difference in vitamin B12 intake between both samples. To reach the recommended intake of vitamin B12 with milk powder only, one should consume 133 grams of milk powder per day, which is a very high amount. Vitamin C Vitamin B1 Vitamin B2 Vitamin B3 Vitamin B6 Folate Vitamin B12 Sample (mg/day) (mg/day) (mg/day) (mg/day) (mg/day) (µg/day) (µg/day) City RNI 45 1.1 1.1 14 1.4 400 2.4 sample Intake CALCIUM A very low calcium intake is related with osteoporosis (Kolsteren, 2010). Soya, green leafy vegetables (cassava leaves and spinach), milk powder and fish are rich sources of calcium consumed by the 90 RESULTS AND DISCUSSION women in our research area. The calcium intake is inadequate for all women in both samples and both the intake and NAR are significantly higher in the village sample than in the city sample (p-value=0) (table 4.30). This can be explained by the lower RNI of the village compared to the city sample and/or the higher consumption frequency of cassava leaves in the village sample. PHOSPHORUS Phosphorus is present in almost all foods. Pulses (from higher to lower content: soya, groundnut, beans), milk powder, meat, fish, poultry and eggs are rich sources of phosphorus. Almost half of the women in the city sample and quarter of the women in the village sample have an inadequate phosphorus intake but this is not significantly different between both samples (table 4.30). Because phosphorus is present in almost all foods, there are two possibilities to explain the inadequate intake. One reason could be that the diet that is not enough diversified and because the percentage of women with an inadequate phosphorus intake is higher in the city sample, we could say that there are more women with a diet that is not enough diversified in the city sample. Another reason could be the fact that the total energy intake is lower than the recommended intake, as mentioned above. POTASSIUM Plantain banana, pulses (from higher to lower content: soya, beans, groundnuts), green leafy vegetables (from higher to lower content: cassava leaves, amaranth, spinach, sweet potato leaves), fruits (from higher to lower content: avocado, goyava, banana, papaya), milk powder, meat and fish are all rich sources of potassium. In case of a diversified diet, a potassium deficit seldom appears because the kidneys adapt the excretion of potassium to the amount of potassium needed by the body. Only when vegetables are cooked to long lot’s of potassium can be lost. The potassium intake and NAR of the village sample is higher than of the city sample (both p- values=0) with 75% of the women having an inadequate intake in the village sample and all women in the city sample (table 4.30). This inadequate intake can again be explained by a diet that is not enough diversified on the one hand and the significant higher intake of the village sample shows that the diet in the village sample is more diversified than in the city sample. On the other hand, by the too low total energy intake. SODIUM Sodium is also present in almost all foods (of which the richest sources are milk powder, salt and salted fish) and hence a sodium deficit is rather rare. It is possible to develop a sodium deficit with severe diarrhea, which is followed by dehydratation. None of the women in the village sample has an inadequate intake of sodium, this intake and also the NAR are significantly (both p-values=0.0004) higher than in the city sample, in which 28% of the women have an inadequate intake (table 4.30). Because sodium is present in all food items, it is 91 RESULTS AND DISCUSSION difficult to explain the difference between both samples. Calcium Phosphorus Potassium Sodium Sample (mg/day) (mg/day) (mg/day) (mg/day) City RNI 1150 550 3500 1600 sample intake IRON AND ZINC The RNI for iron and zinc depends on the bioavailability of these minerals, i.e. the amount that is actually absorbed and used by the body, which is lower than the total mineral intake. The bioavailability depends on different factors, e.g. composition of the diet, nature of the food ingested, chemical form of the nutrient and psychological status of an individual (Gibson and Ferguson, 1999). There are dietary modifiers known to enhance and inhibit the absorption of iron and zinc. Bioavailability of iron Iron is present in two different forms in foods, heme iron (found in meat, poultry and fish) and non- heme iron (found in both animal and plant-based foods). The major source of heme iron in foods is muscle protein, 40% of the iron is said to be heme iron. Heme iron is less affected by dietary modifiers and is more readily absorbed than non-heme iron. In normal persons 15-25% and in iron-deficient persons 25-35% of the heme iron is absorbed, while this is only 2-20% for non-heme iron. Major enhancers of nonheme iron absorption are muscle proteins and ascorbic acid. Ascorbic acids enhancing effect is most apparent if the muscle protein consumptions is low (Allen and Ahluwalia, 1997). An important inhibitor of nonheme iron absorption is phytate. The nutrient intakes are calculated from food composition tables where no correction is made for bioavailability and before transforming the nutrient intakes into usual intakes, neither a correction is made for bioavailability. Because the RNI for iron and zinc depends on the bioavailability of these minerals, the bioavailability of iron and zinc needs to be calculated. The FAO/WHO (1988) model estimates the iron availability and is based on iron absorption from typical meals in Asia, India, Latin America and Western countries. They classify meals into three categories of low, intermediate and high iron bioavailability based on their content of flesh foods versus plant-based foods and the content of ascorbic acid-rich foods. In this research the bioavailability of nonheme iron is determined by cut-off values of meat, fish and poultry protein and ascorbic acid derived from Monsen et al. (1978) as shown in table 4.31. This method approximates those based on the FAO/WHO classification. 92 RESULTS AND DISCUSSION Ascorbic acid Meat, fish and poultry protein (gram/1000 kcal) (mg/1000 kcal) <9 9-27 >27 <35 5 10 15 35-105 10 15 15 >105 15 15 15 Table 4.31: estimated percentage bioavailability of nonheme iron for iron-deficient, nonanemic persons with differing intakes of meat, fish and poultry protein (g) and ascorbic acid (mg) per 1000 kcal (Gibson and Ferguson, 1999). The RNI of 10% bioavailable iron is used in both samples because the meat, fish and poultry protein is lower than 9 gram per 1000 kcal and ascorbic acid is between 35 and 105 mg per 1000 kcal in both samples (table 4.32). Ascorbic acid Meat, fish and poultry protein Sample (mg/1000 kcal) (gram/1000 kcal) City sample 46.19 4.28 Village sample 73.37 3.39 Table 4.32: mean ascorbic acid (mg), meat, fish and poultry protein (g) intake per 1000 kcal Bioavailability of zinc Phytate forms insoluble complexes with zinc and nonheme iron and is the main determinant of zinc absorption. A critical component to estimate the bioavailability of zinc is the phytate-to-zinc molar ( ) ratio ( ( ) ). If the ratio is 퐩퐡퐲퐭퐚퐭퐞 퐦퐦퐨퐥 퐳퐢퐧퐜 퐦퐦퐨퐥 • >15, then the diet has a low zinc bioavailability • within range of 5-15, then the diet has a moderate zinc bioavailability • <5, then the diet has a high zinc bioavailability The phytate-to-zinc molar ratio is higher than 15 in both samples (28.77 and 22.44 in the city and village sample and respectively), which means that the Congolese women have a low-bioavailability of zinc in their diet, hence the RNI of zinc for low bioavailability is used. Intake of iron and zinc The most important function of iron is the transportation of oxygen in the red blood cells. When not enough iron is available, this can lead to hypochromic microcytic anaemia in the latest stage of iron deficiency (Kolsteren, 2010). Pulses (from higher to lower content: soya, beans and groundnut) are the richest sources of iron consumed in our research area, followed by milk powder, green leafy vegetables (from higher to lower content: cassava leaves, amaranth, spinach, sweet potato leaves), fish and meat. All women have an inadequate iron intake as shown in table 4.33. Compared to the other nutrients, iron is an exception in the fact that the usual intake is significantly higher in the village sample than in the city sample (p-value=0) but the NAR is significantly lower in the village sample than in the city sample (p-value=0.021). So although the iron intake in village sample is higher than in city sample, the iron intake in village sample is much lower than the recommended intake than in the city sample, 93 RESULTS AND DISCUSSION which can be explained by the higher RNI for iron in the village sample compared to the city sample (table 4.33). The higher consumption frequency of beans, groundnut and milk powder in the city sample does not result in a higher intake of iron in the city sample compared to the village sample. The higher consumption frequency of cassava leaves in the village sample may explain the higher iron intake compared to the city sample. Zinc is an essential component of a large number of enzymes participating in the synthesis and degradation of carbohydrates, lipids, proteins and nucleic acids. Zinc also contributes to the maintenance of cell and organ integrity and plays an important role in genetic expression and in the immune system. All these functions account for an essential role of zinc for all life forms. Zinc deficiency is accompanied by growth retardation, delayed sexual maturation, skin lesions, diarrhea etc (FAO/WHO, 2004). Zinc is present in small amounts in many foods such as pulses (from higher to lower content: soya, groundnut and beans), milk powder, meat and fish. The zinc intake is inadequate for all women in the village sample and almost all women in the city sample, which have a significant higher zinc intake and NAR than women in village sample (both p- values=0.0003) as shown in table 4.33. The inadequate intake may be explained by the low- bioavailability of zinc in their diet. The higher consumption in the city compared to the village sample may be explained by their more frequent consumption of pulses and milk powder. Sample Iron (mg/day) Zinc (mg/day) City sample RNI 20.35 9.8 intake We can conclude that only for vitamin A, all women have an adequate intake. The intake of vitamin A is 7 times higher than the recommended intake because of the high daily palm oil consumption. As concerns vitamin C and sodium, most of the women have an adequate intake. For vitamin C this can be explained by the daily high amount of green leafy vegetables and fruits consumed and for sodium this can be explained by the fact that sodium is present in almost all foods. For all the other vitamins and minerals, most of the women in both samples have an intake lower than the recommended intake, this is due to the low consumption of foods rich in these vitamins and minerals or due to the fact that the total energy intake is lower than the recommended energy intake. For vitamin B3, vitamin B12, vitamin D, calcium and iron, this can be explained by the low consumption of animal products. Due to the high amount of vegetal products in their diet, zinc only has a low bioavailability and hence all women have an inadequate zinc intake. Because phosphorus and potassium are present in almost all foods, the inadequate intake can be explained by a diet that is 94 RESULTS AND DISCUSSION not enough diversified or by the fact that the total energy intake is too low. The vitamins and minerals for which the intake is higher in the city than in the village sample are: • vitamin D • vitamin B12 • zinc The higher intake of vitamin D and vitamin B12 can be explained by the higher consumption of milk powder and for zinc by the higher consumption of both pulses and milk powder. The vitamins and minerals for which the intake is higher in the village than in the city sample are: • vitamin A • calcium • vitamin C • potassium • vitamin B1 • sodium • vitamin B6 • iron • folate The higher consumption of palm oil, cassava leaves and fruit can explain their higher vitamin A intake. The higher consumption of cassava leaves, because of the higher availability of cassava leaves in the village compared to the city sample, results in a higher folate intake, in a higher iron intake, together with a higher fruit consumption frequency in a higher vitamin C intake, together with the lower RNI in a higher vitamin B6 and calcium intake. The higher potassium intake shows that the diet in the village sample is more diversified than in the city sample. The diet in the village sample is more diversified in terms of micronutrients than in the city sample. When we compare this with the methods that measure the dietary diversity in terms of food groups (see table 4.14 and 4.15), we see that only the DDS is significantly different between both samples. Namely, the percent of women with the highest food diversity is lower in the village sample than in the city sample which is in contradiction to what we supposed according to the micronutrient intake. This can be explained by the fact that the DDS includes both foods consumed in quantities higher and lower than 15 grams. For example, person 1 consumes 1 gram of 6 different food groups and person 2 consumes 100 gram of these 6 different food groups. Both persons have the same DDS but the micronutrient intake of the second person is much higher than the first person. Hence, a higher DDS does not per se mean a higher the micronutrient intake and hence, it is better to do both methods and not to trust on one of the methods only. 95 RESULTS AND DISCUSSION 5.CONCLUSION This main objectives of this master dissertation were to evaluate the diets and the food security situation of the Turumbu women in the city Kisangani and in a rural village Yaoseko. The data was collected by doing 122 surveys in Kisangani or the city sample and 130 surveys in Yaoseko or the village sample. The socio-economic profile of the households in our survey is as follows: the mean age, average household size and mean income are higher in the city sample than in the village sample. The main primary activity in the city sample is a small business while this is agriculture in the village sample. A significant higher percentage of households in the village goes fishing, hunting, collects insects, picks mushrooms, picks WEPs, owns a field and cultivates crops. Green leafy vegetables and cassava are the most cultivated crops and serve for both auto-consumption and sale. To evaluate the food security situation, we collected information about dietary diversity by a food frequency list to calculate the DDS and the FCS (two food security indicators). As the Congolese have developed coping strategies to cope with food security problems, two other food security indicators, the CSI and HFIAS based on a coping strategies question list were calculated. All the measured food security indicators gave the same result: the households/women in the village sample are less food secure than those in the city sample. Because these indicators do not give detailed information about dietary intakes, two 24-hour recalls were done per women interviewed to calculate the mean daily energy, mean daily macronutrient and usual daily micronutrient intake. Comparing the mean daily energy intake with the food security indicators leads to the conclusion that the most food secure households/women have the highest energy intake according to all food security indicators measured. Hence, all of them are good food security indicators and the choice which food security indicator to use depends on the goal of the measurement. The CSI, WFPs method and DDS are the best methods to use in an emergency situation because the percentage of households/women belonging to the least food secure category is the lowest and hence it is certain that the people identified as least food secure are the least food secure. In case of a nutrition research on the other hand, for example, when the goal is to identify the ones that are food secure, the HFIAS is the best method because it classifies the lowest percentage of households as the most food secure. The mean daily energy intake per person is 1812 kcal in the city sample and is not significantly different from the village sample, where the mean daily energy intake is 1883 kcal per person. This energy intake is higher than the 1486 kcal per person per day in the 2005 FAO food balance sheets but does not meet either the recommended energy intake as calculated in our research nor the daily recommended energy intake of 2300 kcal as defined by the FAO. The food balance sheets are based on the official statistics of the agricultural production in Congo and must be carefully interpreted due to the malfunctioning of the services of (agricultural) statistics in Congo, the inaccessibility too many parts of the country, the mainly informal economy, etc. The highest energy intake comes out of carbohydrates in both samples but does not meet the 96 CONCLUSION recommended minimum percent of 55 in both samples. The carbohydrate energy contribution is significantly higher in the village sample compared to the city sample. This can be explained by the higher consumption of roots, tubers and vegetables in the village. Proteins have the lowest energy contribution in both samples and this energy contribution is lower than the recommended minimum percent of 10 in both samples. The protein energy contribution is significantly higher in the city than in the village sample and can be explained by the higher intake of cereals, fish, meat, poultry, eggs and milk and of legumes, pulses and nuts. The energy contribution of fat exceeds the recommended maximum percent of 30 in both samples and is not significantly different between both samples. The high energy contribution of fat can be explained by the high palm oil (=cheapest source of energy) consumption. Looking at the influence socio-economic parameters on energy and macronutrient intake shows that income has no influence on the energy and macronutrient intake in the village sample. This is due to the fact that people in the village less depend on monetary income for food consumption and mainly auto-consume food products from the field. The intake of energy and all the macronutrients, according to income category, is significantly higher of the women belonging to the highest income category in the city sample. Although, this energy intake is significantly higher than the recommended energy intake, there is still a problem of insufficient energy intake because richer people still consume higher quantities of the same foods and do not have a higher dietary diversity. As regards the education level in the city sample, the energy and carbohydrate intake is significantly higher of women that finished secondary school compared to women without any education and those that that only finished primary school. Concerning the age categories, women of the highest age category (older than 40) have a lower intake of energy and all macronutrients than younger women in the city sample and a lower intake of energy and fat in the village sample. This can be explained by the fact that the older people are, the less energy they need, the less they are pregnant, the less they are breastfeeding, etc. Calculating the micronutrient intakes leads to the conclusion that only for vitamin A, all women have an adequate intake. The intake of vitamin A is 7 times higher than the recommended intake because of the high daily palm oil consumption. As concerns vitamin C and sodium, most of the women have an adequate intake. For vitamin C this can be explained by the daily high amount of green leafy vegetables and fruits consumed and for sodium this can be explained by the fact that sodium is present in almost all foods. For all the other vitamins and minerals, most of the women in both samples have an intake lower than the recommended intake, this is due to the low consumption of foods rich in these vitamins and minerals or due to the fact that the total energy intake is lower than the recommended energy intake. For vitamin B3, vitamin B12, vitamin D, calcium and iron, this can be explained by the low consumption of animal products. Due to the high amount of vegetal products in their diet, zinc only has a low bioavailability and hence all women have an inadequate zinc intake. Because phosphorus and potassium are present in almost all foods, the inadequate intake can be explained by a diet that is not enough diversified or by the fact that the total energy intake is too low. When both samples are compared, we see that the intake of vitamin E, vitamin B2, vitamin B3 and phosphorus is not significantly different between both samples. The intake of vitamin D, vitamin B12 and zinc is higher in the city than in the village sample. The higher intake of vitamin D and vitamin B12 can be 97 CONCLUSION explained by the higher consumption of milk powder and for zinc by the higher consumption of both pulses and milk powder . The intake of vitamin A, vitamin C, vitamin B6, folate, calcium, iron and potassium is higher in the village than in the city sample. The higher consumption of palm oil, cassava leaves and fruit can explain the higher vitamin A intake. The higher consumption of cassava leaves results in a higher folate intake, in a higher iron intake, together with a higher fruit consumption frequency in a higher vitamin C intake, together with the lower RNI in a higher vitamin B6 and calcium intake. The results of this research must be carefully interpreted. A first limitation is the fact that the calculation of the nutrient intakes is based on nutrient values of our food composition table for use in Kisangani and Yaoseko. Our table includes some missing values for some nutrients which results in an underestimation of the actual nutrient intakes. Because no food composition table is available for Congo, our food composition table was composed by selecting the foods consumed in Congo out of other food composition tables from neighboring countries with a similar diet. The use of different tables can lead to some mistakes in the calculation of the nutrient intakes, for example because each country has its own methods to determine the nutritional values of foods. Although these limitations, the food intakes were recorded as good as possible. We worked with individual recipes, so every time all the ingredients of the recipe were noted. Only if this was not possible, average recipes were used. Although the use of average recipes is less accurate, we did our best to calculate an average recipe by searching 5 voluntary women per recipe to cook the recipe and we stayed with them during the cooking process to note every step and weigh all the ingredients. Because the ingredients are always expressed in monetary values, we made a price-weight conversion list by weighing as many different food products as possible on every market in our research area. Another limitation is the fact that the data is collected by interviewing the women. When doing interviews, we rely on the honesty of the interviewee and we sometimes noticed that people did not give a correct answers to certain questions because they did not understand the question and were afraid to say that the question is not clear, because they were ashamed to answer (for example about alcohol consumption and monetary income), because they were not willing to answer, etc. which can also give incorrect results. To minimize incorrect answers of the interviewee for example, a photo book was made with different portion sizes to facilitate the estimation of the portion size by the interviewee. The diet of the women in our research area is plant based and little diversified. This results in energy and micronutrient intakes lower than the recommended amounts. Diversification of the diet could help to increase the micronutrient and total energy intakes. WEPs are very rich in several micronutrients and could be important for the diversification of the diet. Before implementing WEPs in the diet, the nutritional value of these plants should be correctly analyzed in the first place. In a next step, the perceptions of the WEPs by the local population should be studied to see if people are willing to eat WEPs. A next step can then be the domestication of these plants and the promotion of WEP consumption and cultivation. 98 CONCLUSION LIST OF REFERENCES Allen, L.H. and Ahluwalia, N. (1997). 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Heure début : Heure fin : A Données générales et socio-économiques A01 : Nom :…………………… A02 : Etat civil : 1 = célibataire ; 2= marié (1° épouse, 2° épouse, etc…) ; 3 = divorcé ; 4 = veuve A03 : Age :……………………. A04 : Niveau d’études : …………… A05 : Ethnie:…………………… A06 : Religion :……………………. A06 : Activité principale :………….. A07 : 2i è m e activité :………………… A08 : Nombre de membres vivant actuellement dans le ménage : > 65 ans : ……Hô ………Fê TOTAL : ……….. 15< >65 ans : …….Hô ………Fê 5 < >15 ans : …….Hô ………Fê < 5 ans : …….Hô ………Fê A09 : Est-ce que vous avez un champ ? OUI / NON Si oui, Appartenance ? 1 = Champ privé ou 2 = Champ en groupe/association ? Cultures dans le champs ? …………………. But : 1= l’autoconsommation ou 2 = vente ou 3 = les deux Cela suffit pour l’autoconsommation ou vous achetez en plus ? A10 : Est-ce que vous avez un jardin de case ? OUI / NON Si oui, Appartenance ? 1 = jardin privé ou 2 = jardin en groupe/association ? Cultures dans le jardin ? …………………. But : 1= l’autoconsommation ou 2 = vente ou 3 = les deux Cela suffit pour l’autoconsommation ou vous achetez en plus ? A11 : Est-ce que quelqu’un de votre ménage fait Activité 1 = oui Si oui : But : 1 = autocons ; 2 = non = vente ; 3= les deux a. L’élevage b. La pêche c. La chasse d. La cueillette des champignons e. La cueillette des PAS f. Le ramassages des insectes A12 : a) Sources de revenu du ménage (en ordre d’importance): 1………………………………estimation revenu annuel: .……… 2………………………………estimation revenu annuel: ……… 3………………………………estimation revenu annuel: ……… 4………………………………estimation revenu annuel:………. A 13 : Le revenu total de notre ménage est ………………….. ou soit compris entre A 14 : Est-ce que vous avez des animaux d’élevage ? OUI / NON Si oui, combien de a. Volailles : d. Porcs : b. Caprins : e. Bovins : c. Ovins : f. Autres (à spécifier) : Annex 2: Questionnaire A15 : Votre ménage dispose de combien de : a. Vélos : f. Radios : k : autre (à spécifier) b. Motos : g. Télévisions : c. Voitures : h. Groupes électrogènes : d. Pirogues : i. Panneaux solaires : e. Charrettes : j. Ordinateur : A16 : a. Vous êtes connecté à la SNEL ? OUI / NON b. Vous êtes connecté à la REGIDESO ? OUI / NON Si non, où est-ce que vous obtenez de l’eau à boire ? 1 = pompe à eau, 2 = puit/borne- fontaine ; 3 = source aménagé, 4= source non- aménagé, 5 = rivière, 6 = fleuve, 7 = marigot, 8 = autres (à spécifier) A17 : a. Vous êtes locateur ou propriétaire de la maison ? b. le toit de la maison ? 1 = paille/feuilles ; 2 = tôles ; 3 = tuiles ; 4 = autres (à spécifier) c. les murs de la maison ? 1 = argile armée ; 2 = briques adopes ; 3 = briques cuites ; 4 = beton ; 5 = autres (à spécifier) d. La maison dispose de combien de chambres (pièces) ? e. La porte : 0 = pas de porte, 1 = rameaux, 2 = bambou, 3 = bois, 4 = métal ; 5 = autre (à specifier) f. les fenêtres : 0 = pas de fenêtres, 1 = rameaux, 2 = bambou, 3 = bois, 4 = métal ; 5 = autre (à specifier) B Plantes Alimentaires Sauvages B01 : a) Quelles sont les fruits sauvages que vous connaissez* (des plantes qui ne sont pas cultivé, mais cueilli dans la forêt) ? (Notez les noms vernaculaires et la langue respective dans la première colonne du tableau suivant) b) Quelles sont les légumes sauvages que vous connaissez ? (Notez les noms vernaculaires et la langue respective après les fruits dans la première colonne du tableau suivant) c) Quelles sont les noix, épices, tubercules ou autres plantes alimentaires sauvages (PAS) que vous connaissez ? (Notez les noms vernaculaires et la langue respective dans la première colonne du tableau suivant) * connaître ne veut pas dire utiliser, mais on cherche la connaissance. B02 – B11 : Continuez après plante par plante et posez les questions B02 à B11 (voir tableau) N B01 B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 r. a b Part Péri Quan Quan Quan Quan Import Transform Stocka Autres Pla Lang ie ode tité tité tité tité ance ation* ge* utilisati nte ue utili de mang récolt achet vend ons* cité sé l’ann é é é u ée 1 2 3 … B02 : Partie utilisée (fruit entier, fruit sans peau, fruit sans graines, graine ou amande, feuilles jeunes, feuilles adultes, racine, écorce, tige, sève, etc…) B03 : Période de l’année que la plante est disponible B04 : Est-ce que vous la consommez parfois? Si oui, quantité consommée par année° (°si nécessaire, retrouver en déclenchant en jours, semaines, mois,…) Annex 2: Questionnaire B05 : Est-ce que quelqu’un de votre ménage cueille parfois cette plante? Si oui, quantité cueillie par année° et endroit de cueillette ( km) ? B06 : Est-ce que quelqu’un de votre ménage l’achète parfois au marché ? Si oui, quantité achetée par année + le prix moyen par unité B07 : Est-ce que quelqu’un de votre ménage vend parfois cette plant? Si oui, quantité vendue par année + prix moyen par unité + où ? B08 : Selon vous la plante est : 1=très important ; 2=important ; ou 3=peu important B09 : Est-ce que vous faites une transformation quelconque avec cette plante ? (p.e. fabrication du jus, huile, sel indigène, etc…) *Si oui, décrivez en bas du tableau B10 : Est-ce que vous stocker parfois la plante ? Si oui, combien de temps et comment ? *décrivez en bas du tableau B11 : Est-ce que à part le fait de le manger, vous utilisez cette plante encore d’une autre manière ? (p.e. pour soigner certaines maladies, pour la construction, etc…) *Si oui, décrivez en bas du tableau, la partie utilisée, préparation, raison d’utilisation, etc B12 : Répondez par oui (=accord) ou non (pas d’accord) sur les thèses/assertions suivantes : 1 Je consomme régulièrement des fruits sauvages 2 Je consomme régulièrement des légumes sauvages 3 Je consomme régulièrement des noix sauvages 4 Je consomme régulièrement des tubercules sauvages 5 Je consomme régulièrement des épices sauvages 6 J’offre parfois des fruits sauvages aux visiteurs 7 J’offre parfois des légumes sauvages aux visiteurs 8 J’offre parfois des noix sauvages aux visiteurs 9 J’offre parfois des tubercules sauvages aux visiteurs 10 J’offre parfois des épices sauvages aux visiteurs 11 Je mange des fruits sauvages pendant des occasions spéciales 12 Je prépare des légumes sauvages pendant des occasions spéciales 13 Je mange des noix sauvages pendant des occasions spéciales 14 Je prépare des tubercules sauvages pendant des occasions spéciales 15 Je prépare avec des épices sauvages pendant des occasions spéciales 16 Les fruits sauvages sont une contribution importante pendant des périodes difficiles 17 Les légumes sauvages sont une contribution importante pendant des périodes difficiles 18 Les noix sauvages sont une contribution importante pendant des périodes difficiles 19 Les tubercules sauvages sont une contribution importante pendant des périodes difficiles 20 Les épices sauvages sont une contribution importante pendant des périodes difficiles 21 Les hommes de mon ménage consomment des fruits sauvages 22 Les hommes de mon ménage consomment des légumes sauvages 23 Les hommes de mon ménage consomment des noix sauvages 24 Les hommes de mon ménage consomment des tubercules sauvages 25 Les hommes de mon ménage consomment des épices sauvages 26 Les enfants aiment manger les fruits sauvages 27 Les enfants aiment manger les légumes sauvages 28 Les enfants aiment manger les noix sauvages 29 Les enfants aiment manger les tubercules sauvages 30 Les enfants aiment manger les épices sauvages 31 J’apprends aux enfants à reconnaître les fruits sauvages 32 J’apprends aux enfants à préparer les légumes sauvages 33 J’apprends aux enfants à reconnaître les noix sauvages 34 J’apprends aux enfants à préparer les tubercules sauvages 35 J’apprends aux enfants à préparer avec des épices sauvages 36 Je recommande aux autres (familles, amies, etc.) de consommer des fruits sauvages 37 Je recommande aux autres (familles, amies, etc.) de consommer des légumes sauvages 38 Je recommande aux autres (familles, amies, etc.) de consommer des noix sauvages 39 Je recommande aux autres (familles, amies, etc.) de consommer des tubercules sauvages 40 Je recommande aux autres (familles, amies, etc.) de consommer des épices sauvages Annex 2: Questionnaire B13 1 La disponibilité des fruits sauvages est : 1 = plus grande qu’auparavant ; 2 = n’a pas changé ; 3 = est plus petite qu’auparavant 2 La disponibilité des légumes sauvages est : 1 = plus grande qu’auparavant ; 2 = n’a pas changé ; 3 = est plus petite qu’auparavant 3 La disponibilité des noix sauvages est 1 = plus grande qu’auparavant ; 2 = n’a pas changé ; 3 = est plus petite qu’auparavant 4 La disponibilité des tubercules sauvages est 1 = plus grande qu’auparavant ; 2 = n’a pas changé ; 3 = est plus petite qu’auparavant 5 La disponibilité des épices sauvages est 1 = plus grande qu’auparavant ; 2 = n’a pas changé ; 3 = est plus petite qu’auparavant B14 : Répondez avec -2= pas du tout d’accord, -1 = pas d’accord, 0 = ni contre, ni pour , 1 = d’accord, 2 = tout à fait d’accord ; sur les thèses suivantes 1 Les fruits sauvages sont important pour moi 2 Les légumes sauvages sont important pour moi 3 Les noix sauvages sont important pour moi 4 Les tubercules sauvages sont important pour moi 5 Les épices sauvages sont important pour moi 6 Les fruits sauvages sont nutritif 7 Les légumes sauvages sont nutritif 8 Les noix sauvages sont nutritif 9 Les tubercules sauvages sont nutritif 10 Les épices sauvages sont nutritif 11 Le goût des fruits sauvages me plaît 12 Le goût des légumes sauvages me plaît 13 Le goût des noix sauvages me plaît 14 Le goût des tubercules sauvages me plaît 15 Le goût des épices sauvages me plaît 16 Les fruits sauvages sont bon pour la santé 17 Les légumes sauvages sont bon pour la santé 18 Les noix sauvages sont bon pour la santé 19 Les tubercules sont bon pour la santé 20 Les épices sauvages sont bon pour la santé 21 Quand je mange des fruits sauvages, je me sens bien 22 Quand je mange des légumes sauvages, je me sens bien 23 Quand je mange des noix sauvages, je me sens bien 24 Quand je mange des tubercules sauvages, je me sens bien 25 Quand je mange des épices sauvages, je me sens bien 26 Je mange des fruits sauvages parce que ça fait partie de mon identité culturelle 27 Je mange des légumes sauvages parce que ça fait partie de mon identité culturelle 28 Je mange des noix sauvages parce que ça fait partie de mon identité culturelle 29 Je mange des tubercules sauvages parce que ça fait partie de mon identité culturelle 30 Je mange des épices sauvages parce que ça fait partie de mon identité culturelle 31 Ceux qui on les moyens d’acheter les fruits cultivées consomment aussi des fruits sauvages 32 Ceux qui on les moyens d’acheter les légumes cultivées consomment aussi des légumes sauvages 33 Ceux qui on les moyens d’acheter les noix cultivées consomment aussi des noix sauvages 34 Ceux qui on les moyens d’acheter les tubercules cultivées consomment aussi des tubercules sauvages 35 Ceux qui on les moyens d’acheter les épices cultivées consomment aussi des épices Sauvages 36 Les PAS sont les aliments des pauvres 37 Riches et pauvres, tous consomment les PAS 38 J’associe les PAS avec une bonne alimentation 39 Les PAS sont bien pour diversifier et améliorer l’alimentation Annex 2: Questionnaire B15 : Quelles sont les contraintes que vous rencontrez par rapport aux PAS ? 1 Si vous avez envie de manger des PAS, est-ce qu’ils sont tjrs disponible ? OUI / NON Si non, expliquer (quel produit, raison : du à la périodicité, du au disfonctionnement du marché,…) 2 La qualité des fruits sauvages est : 1 = mauvaise ; 2= médiocre ; 3 = passable ; 4 = bonne ; 5 = très bonne Quelles sont les critères de qualité que vous observez :………………………………………. 3 La qualité des légumes sauvages est : 1 = mauvaise ; 2= médiocre ; 3 = passable ; 4 = bonne ; 5 = très bonne Quelles sont les critères de qualité que vous observez :………………………………………. 4 La qualité des noix sauvages est : 1 = mauvaise ; 2= médiocre ; 3 = passable ; 4 = bonne ; 5 = très bonne Quelles sont les critères de qualité que vous observez :………………………………………. 5 La qualité des épices sauvages est : 1 = mauvaise ; 2= médiocre ; 3 = passable ; 4 = bonne ; 5 = très bonne Quelles sont les critères de qualité que vous observez :………………………………………. 6 La qualité des tubercules sauvages est : 1 = mauvaise ; 2= médiocre ; 3 = passable ; 4 = bonne ; 5 = très bonne Quelles sont les critères de qualité que vous observez :………………………………………. 7 Les prix des PAS au marché sont 1 = bas ; 2 = normale/abordable ; 3 = trop élevé 8 Est-ce que vous rencontrez des problèmes pendant la préparation ou transformation des PAS ? Si oui, lesquelles ?...... 9 Est-ce que vous rencontrez des problèmes pendant la conservation ou le stockage des PAS ? Si oui, lesquelles ?...... 10 Est-ce que vous trouvez que vous avez assez d’information par rapport à la consommation ou autre des PAS ? Expliquer 11 Si vous faites la vente, est-ce que rencontrez des problèmes par rapport à la commercialisation. Si oui, lesquelles ?...... 12 Est-ce que vous rencontrez encore d’autres contraintes par rapport aux PAS ? Si oui, lesquelles ?...... B16 : Si je vous offre 2 légumes (fruits ou noix), laquelle est-ce que vous choisirez 1Bilolo Epinards 7Bilolo Pondu 13Mangue Mabongo 19Kasu Arachides 2Pondu Fumbwa 8Fumbwa Muchicha 14Ananas Bombi 3Misili Pondu 9Muchicha Misili 15Bombi Papaye 4Epinards Meye 10Epinards Fumbwa 16Mangue Bombi 5Muchicha Bilolo 11Pondu Meye 17Mabongo Ananas 6Misili Epinards 12Meye Muchicha 18Papaye Mabongo B17 : Si demain nous voulons mener des recherches pour que ces plantes puissent être cultivées, avec lesquelles est-ce que nous devons commencer ? (citer 5 PAS et la raison) Sécurité alimentaire C1. a) Le mois passé, est-ce qu’il vous êtes arrivé de s’inquiéter une fois de ne pas avoir assez à manger pour votre ménage? Si oui, b) Combien de fois est-ce que cela s’est passé ? 0=jamais 1= presque jamais (1-2 fois dans les 4 semaines passées), 2= parfois (3-10 fois dans les 4 semaines passées), 3=souvent (plus de 10 fois dans les 4 semaines passées) 4=chaque jour C2. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage n’a pas pu manger ce qu’il préfère parce qu’il n’y avait pas assez de moyens? Si oui, b) Combien de fois est-ce que cela s’est passé ? Annex 2: Questionnaire C3.a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage a du manger une variété limitée de nourriture parce qu’il n’y avait pas assez de moyens? Si oui, b) Combien de fois est-ce que cela s’est passé ? C4. a)Le mois passé, est-ce qu’il vous êtes arrivé de collecter de la nourriture sauvage, de chasser? Si oui, b) Combien de fois est-ce que cela s’est passé ? C5. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage a du diminuer la quantité du repas par rapport à ce qu’ il sent avoir besoin, parce qu’il n’y avait pas assez de nourriture ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C6. a) Le mois passé, est-ce qu’il vous êtes arrivé d’emprunter de la nourriture? Si oui, b) Combien de fois est-ce que cela s’est passé ? C7. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage a du manger la nourriture qu’il n’aime pas du tout, parce qu’on n’avait pas de moyens pour obtenir d’autres aliments ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C8. a)Le mois passé, est-ce qu’il vous êtes arrivé d’acheter de la nourriture sur crédit ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C9. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage a du diminuer le nombre de repas par jour comparé avec le nombre de repas pris habituellement, parce qu’il n’y avait pas assez de nourriture ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C10. a) Le mois passé, est-ce qu’il vous êtes arrivé de dépendre de l’aide d’un ami ou de la famille? Si oui, b) Combien de fois est-ce que cela s’est passé ? C11. a)Le mois passé, est-ce il vous êtes arrivé de limiter la consommation des adultes afin d’avoir plus de nourriture pour les petits enfants ? Si oui, b) Combien de fois est-ce que cela c’est passé ? C12. a)Le mois passé, est-ce qu’il vous êtes arrivé de récolter les cultures non-mûr? Si oui, b) Combien de fois est-ce que cela s’est passé ? C13. a) Le mois passé, est-ce qu’il y a eu un moment où il n’y avait vraiment plus rien à manger dans votre ménage par manque de moyens pour obtenir de la nourriture ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C14. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage est allé dormir en famine parce qu’il n’y avait pas assez de nourriture? Si oui, b) Combien de fois est-ce que cela s’est passé ? C15. a) Le mois passé, est-ce que vous ou quelqu’un d’autre du ménage n’a rien eu à manger pendant toute une journée et nuit parce qu’il n’y avait pas assez de nourriture ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C16. a)Le mois passé, est-ce qu’il vous êtes arrivé de nourrir plutôt les membres du ménage qui travaillent au détriment des membres qui ne travaillent pas ? Si oui, b) Combien de fois est-ce que cela s’est passé ? C17. a) Le mois passé, est-ce qu’il vous êtes arrivé de consommer les semences que vous avez stocké/gardé pour la saison culturale prochaine? Si oui, b) Combien de fois est-ce que cela s’est passé ? C18. a)Le mois passé, est-ce qu’il vous êtes arrivé d’envoyer vos enfants chez les voisins pour manger? Si oui, b) Combien de fois est-ce que cela s’est passé ? C19. a)Le mois passé, est-ce qu’il vous êtes arrivé d’envoyer des membres du ménage pour mendier? Si oui, b) Combien de fois est-ce que cela s’est passé ? C20. a) Le mois passé, est-ce qu’il vous êtes arrivé de dire aux membres du ménage d’aller se débrouiller chacun pour soi de trouver à manger? Si oui, b) Combien de fois est-ce que cela s’est passé ? Nutrition D1. Combien de repas prenez-vous par jour ? ………….. Quand ? ………………………………………………… D2. Est-ce que vous prenez des casse-croûtes ? OUI / NON Si oui, quoi et quand ?…………………………………………………… D3. Poids de la femme : …………… kg D4. Etes-vous enceinte ? OUI / NON Si oui, dans quel semestre ? 1 / 2 / 3 D5. Est-ce que vous allaitez un enfant ? OUI / NON Si oui, date de naissance de l’enfant ? D6. Hier, est-ce que vous avez fait des efforts physiques 1 = très fort ; 2 = moyenne ; 3 = faible D7. Hier, c’était une journée normale ou journée de fête/marché/deuil/maladie? D8. Est-ce qu’il y a (maintenant ou dans le passé) des projets ici dans la commune qui donnent l’information sur l’alimentation ou qui vous apprennent comment préparer certaines aliments ? OUI / NON Décrire les projets/les infos reçus Annex 2: Questionnaire Food Frequency Combien de fois est-ce que vous avez mangé les aliments ci-dessous la semaine passée ? (pour les PAS, tubercules, fruits, légumes, feuilles et épices sauvages, notez le nom vernaculaire ET la langue) Langue des En quantité de En quantité de aliments plus de 15 g moins de 15 g sauvages 1. Céréales 1.1. Mais (fufu, grillé, bouilli,…) 1.2. Riz 1.3. Farine de blé (beignets, autres…) 1.4. Spaghetti 1.5. Biscuits 1.6. Pain 1.7. Autres céréales 2. Tubercules + plantain 2.1. Manioc (fufu, chikwangue,…) 2.2. Plantains (lituma, grillé, bouilli, …) 2.3. Autres que manioc (pommes de terre, igname, taro, patate douce,…) 2.4. Tubercules sauvages :……… 3. légumineuses et noix 3.1. Haricots et pois 3.2. Arachides 3.3.Soja 3.4. Autres :…………………………………. 3.5.Légumineuses sauvages………………… 3.6 Noix sauvages :………………………… 4. légumes feuilles 4.13.1 Feuilles de manioc (pondu)(pondu, sakasaka) 4.2 Feuilles d’haricot/patates 4.3 Amaranthes (muchicha)/ épinards 4.4.Oseille (ngaingai) 4.5. Autres feuillles sauvages : ……………… 5. légumes 5.1 Tomates frais 5.2 Gombo 5.3 Aubergine 5.4 Aubergine locale 5.5 Courge 5.6 Carottes 5.7 Choux (de chine, pommé) 5.8. Oignon 5.9. Concombre 5.10 Poivron 5.11Autres cultivés :…………………….:…… 5.12 Autres sauvages : ………………………. 6. Fruits 6.1 Banane 6.2 Safou 6.3 Avocat 6.4 Autres fruits cultivés (mangue, papaye, orange, mandarine, citron, goyave, ananas, melon, maracuja, fruits d’étoile) Annex 2: Questionnaire 6.5 Autres fruits sauvages : ……….. 7. épices (condiments) 7.1 Pâte de tomate 7.2 Maggi 7.3 Poivre 7.4 Sel usine 7.5 Piment 7.6 Ail 7.7 Poudre d’arachide/pâte d’arachide 7.8 noix muscade 7.9 feuille laurier 7.10 bicarbonate 7.11 goût 7.12 Autres cultivé:……………………. 7.13 Autres sauvages : …………………. 7.14 Sel indigène 8 Poisson 8.1 Poisson frais 8.2 Poisson séché/salé + frétin 8.3 Sardines/ Pilchards 8.4 Autres :…………………………………… 9. viande 9.1 Viande frais élévée (chèvre, mouton, bœuf, porc) 9.2 Viande frais de chasse (Antilope, lapin, serpent) 9.3 Viande fumée ( de brousse) 9.4 Corned beef ou autre en boîte 9.5 Autres :…………………… 10.Oeufs 11. Volaille 12. Corps gras 12.1 Huile de palme rouge 12.2 Autre huile locale (huile d’arachide,…) 12.3 Huile végétale (bidons) 12.4 Autres sauvages : ……………….. 13. Divers 13.1 500, lotoko 13.2 Vin de palme 13.3 Bierre 13.4 Sucré 13.3 Café 13.4 Thé 13.5 Sucre/ Miel 13.6 Champignons 13.7. Insectes (chenilles, fournis,..) 13.8 Lait en poudre 13.9 Bonbon 13.10 Fromage 13.11 Autres :……………… Annex 2: Questionnaire meye muchicha épinards fumbwa épinard muchicha epinards muchicha misili pondu bilolo epinards misili épinards pondu meye pondu fumbwa muchicha meye pondu pondu muchicha misili fumbwa meye bilolo bilolo fumbwa epinards muchicha pondu bilolo bilolo épinards meye epinards muchicha pondu bilolo epinards muchicha épinards fumbwa pondu bilolo meye muchicha meye bilolo misili fumbwa bilolo pondu pondu misili meye épinards fumbwa misili meye misili fumbwa bilolo muchicha misili misili bilolo epinards pondu misili bilolo meye bilolo pondu misili épinards meye muchicha meye misili misili fumbwa bilolo muchicha misili fumbwa épinards meye pondu fumbwa muchicha meye misili pondu fumbwa bilolo fumbwa muchicha fumbwa meye épinards muchicha pondu fumbwa mangue mabongo papaye papaye mangue bombi ananas mabongo mangue bombi mangue papaye mabongo ananas bombi bombi mabongo papaye mangue bombi ananas bombi mangue mabongo mabongo papaye ananas papaye ananas mangue Annex 2: Questionnaire