Food Assistance for Ebola-Affected and Food Insecure 
Populations of Beni and Lubero (FABELU) 
Baseline Survey for FABELU Project in 
Ituri Province
June 2020
Edson NIYONSABA SEBIGUNDA
CIS-ISSNT Director 
Célestin KIMANUKA RURIHO 
Expert and researcher at CIS-ISSNT
Promesse KASEREKA KAVULIRENE
Expert and researcher at CIS-ISSNT
i 
TABLE OF CONTENT
TABLE OF CONTENT................................................................................................................... i
LIST OF ACRONYMS ................................................................................................................. vi
ACKNOWLEDGEMENTS .......................................................................................................... vii
EXECUTIVE SUMMARY ...........................................................................................................viii
I. INTRODUCTION ................................................................................................................... 1
1.1 BRIEF PROJECT DESCRIPTION AND CONTEXT................................................. 1
1.2 PURPOSE AND EXPECTED USE OF THE BASELINE REPORT ....................... 2
1.3 OBJECTIVES OF THE BASELINE............................................................................. 2
1.4 LIMITATIONS OF BASELINE...................................................................................... 3
1.5 LITERATURE REVIEW................................................................................................. 3
1.6 SUMMARY OF METHODOLOGY............................................................................... 6
1.6.1 Preliminary work for the survey............................................................................ 6
1.6.2 Sampling...................................................................................................................... 6
1.6.3 Survey implementation............................................................................................ 7
1.6.3.1 Investigator training ............................................................................................. 7
1.6.3.2 Sample presentation............................................................................................ 8
1.6.4 Data analysis .............................................................................................................. 8
II. FINDINGS............................................................................................................................... 9
2.1 SOCIO-DEMOGRAPHIC CHARACTERISTICS ....................................................... 9
2.1.1 Distribution of households by sex and age of respondents ......................... 9
2.1.2 Level of education ................................................................................................... 10
2.1.3 Characteristics of pregnant household members ......................................... 10
2.1.4 Household size ........................................................................................................ 11
2.1.5 Household residential status and household gender ................................... 11
2.2 OCCUPATION, HOUSEHOLD INCOME AND DEBT ............................................ 12
2.2.1 Current main occupation ...................................................................................... 12
2.2.2 Monthly income ....................................................................................................... 13
2.2.3 Income per person per day................................................................................... 14
2.2.4 Household debt ....................................................................................................... 14
2.3 HOUSEHOLD FOOD SECURITY MEASURES ...................................................... 15
2.3.1 Meals shared by household members .............................................................. 15
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2.3.2 Food Consumption Score ..................................................................................... 16
2.3.2.1 FCS by Health Zone .......................................................................................... 16
2.3.2.2 FCS by household type ..................................................................................... 18
2.3.2.3 FCS according to household status ................................................................ 19
2.3.2.4 FCS according to whether or not the household received food aid in the 
past two months ................................................................................................................... 19
2.3.3 Reduced Coping Strategy Index (rCSI) ............................................................. 20
2.3.3.1 Elements for computation of the rCSI ............................................................. 20
2.3.3.2 Comments on findings....................................................................................... 21
2.3.3.3 Main coping strategies used ............................................................................ 22
2.3.4 Household Hunger Scale (HHS) .......................................................................... 23
2.3.5 Household Dietary Diversity Score (HDDS) ..................................................... 25
2.3.5.1 Meaning and measure....................................................................................... 25
2.3.5.2 Discussion of the results of the HDDS ........................................................... 26
2.4 ACCESS TO PUBLIC INFRASTRUCTURE AND MARKET STUDY ................. 33
2.4.1 Evolution of the price of basic necessities in Mambasa .............................. 33
2.4.2 Period of the year when households do not have enough food ................ 35
2.4.3 Method of obtaining food ...................................................................................... 36
2.4.4 Frequency of market attendance ........................................................................ 36
2.4.5 Means used for transportation ............................................................................ 37
2.4.6 Public transportation cost .................................................................................... 37
2.4.7 Assessment of the market access distance .................................................... 38
2.4.8 Market access barriers .......................................................................................... 38
2.4.9 Access to market indicators ................................................................................ 39
2.5 COMMUNITY PERCEPTIONS ON EBOLA ............................................................. 41
2.5.1 Knowledge of Ebola virus disease by health zone and gender of 
respondents ............................................................................................................................ 41
2.5.2 Level of knowledge of the causes, signs and treatment of Ebola Virus 
Disease ..................................................................................................................................... 41
2.5.3 Identifying the causes of EVD ............................................................................. 42
2.5.4 EVD considerations in community ..................................................................... 43
2.5.5 Regularity in the listening of EVD signs ........................................................... 43
2.5.6 Knowledge of Signs and Symptoms of Ebola ................................................. 44
2.5.7 Prevention of EVD ................................................................................................... 45
2.5.8 Assessment of access to Health Structure in the community ................... 45
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2.5.9 Assessment of the distance to be walked to reach a health facility ......... 46
2.5.10 Perception of people with Ebola in the community ................................... 46
2.5.11 Effects of Ebola on Economic activities ....................................................... 47
2.6 COMMUNITY PERCEPTION OF COVID-19 ........................................................... 48
2.6.1 Knowledge of COVID-19 by health zone and gender of respondents ...... 48
2.6.2 Level of knowledge of the causes, signs and treatment of COVID-19 ..... 49
2.6.3 COVID-19 considerations ...................................................................................... 49
2.6.4 Knowledge of Signs and Symptoms of COVID-19 ......................................... 50
2.6.5 Prevention of COVID-19 ......................................................................................... 51
2.6.6 Perception of people with COVID-19 in the community ............................... 51
2.6.7 Effects of COVID-19 on Economic activities ................................................... 52
III. LESSONS LEARNED ..................................................................................................... 53
IV. CONCLUSION AND MAIN RECOMMENDATIONS .................................................. 54
REFERENCES ............................................................................................................................. 57
APPENDIX .................................................................................................................................... 59
Appendix 1. Survey Staff ..................................................................................................... 59
Appendix 2. Survey Questionnaire ................................................................................... 60
Appendix 3. Details of results tables ............................................................................... 73
Appendix 4. Mapping of HH survey .................................................................................. 79
Tables list
Table 1.Sampling........................................................................................................................... 8
Table 2. Age of respondents........................................................................................................ 9
iv
Table 3.Profile of pregnant persons in the household ........................................................... 10
Table 4.Household size of respondents ................................................................................... 11
Table 5 Household monthly income ......................................................................................... 14
Table 6.Income per person per day .......................................................................................... 14
Table 7.Household level of debt in Mambasa and Mandima HZ ......................................... 14
Table 8.Distribution of households according to the number of Meals taken by children 
aged 6-59 months ........................................................................................................................ 15
Table 9.Distribution of households according to the number of Meals taken by members 
aged 5 years and over ................................................................................................................ 15
Table 10.Interpretation of FCS .................................................................................................. 16
Table 11. Descriptive statistics of Reduced Coping Strategy Index .................................... 21
Table 12.Distribution of household according to the level of the HHS ................................ 24
Table 13. HDDS distribution and comparison ......................................................................... 26
Table 14. Source of product groups consumed in households ........................................... 29
Table 15. Crossing FCS and HHS ............................................................................................ 30
Table 16. Crossing FCS and rCSI .......................................................................................... 30
Table 17. Crossing FCS and HDDS ....................................................................................... 31
Table 18. Price index for 14 essential products in Mambasa territory from January 2019 
to May 2020 (Price in Congolese Franc) ................................................................................. 34
Table 19. Proportion of households by month during which households do not have 
enough food to support families ................................................................................................ 35
Table 20. Means of transport used to get to the market ....................................................... 37
Table 21 Transportation cost to go to market ......................................................................... 38
Table 22.Time to reach the market ........................................................................................... 39
Table 23. Known ways of preventing EVD according to health zones ............................... 45
Table 24. Average and median distances to reach a health facility ................................... 46
Table 25. Community perception of Ebola patients ............................................................. 47
Table 26. Effects of EVD on economic activities .................................................................... 48
Table 27. Means of prevention of COVID-19 known by the population ........................... 51
Table 28. Community perception of COVID-19 patients ..................................................... 51
Table 29. Effects of COVID-19 on economic activities according to the inhabitants 
interviewed .................................................................................................................................... 52
Figures list 
Figure 1. Distribution of households by sex of respondents................................................... 9
Figure 2. Distribution of education level of respondents ....................................................... 10
Figure 3. Household gender status ........................................................................................... 11
Figure 4. Household status ........................................................................................................ 12
Figure 5. Respond current main occupation for living ........................................................... 13
Figure 6. Distribution of FCS by Health Zone ......................................................................... 18
Figure 7. Distribution of Food Consumption score by household type ............................... 18
Figure 8. Distribution of FCS according to household status ............................................... 19
Figure 9. Distribution of FCS according to whether or not the household received food 
aid ................................................................................................................................................... 20
Figure 10. Coping Strategy Index by household category .................................................... 22
v 
Figure 11. Main strategies used by household ....................................................................... 23
Figure 12. Cumulative average number of consumption of 8 different food groups 
according to different categories of households ..................................................................... 28
Figure 13. Comparison of FCSs from Health Zones of Project Ituri and North Kivu ........ 32
Figure 14. Evolution of price index of basics products (base 100 in January 2019) .......... 33
Figure 15. Methods of obtaining food used by households .................................................. 36
Figure 16. Frequency of market attendance ........................................................................... 37
Figure 17. Market access distance appreciation .................................................................... 38
Figure 18. Distribution of respondents who find it difficult to get to the market ................. 39
Figure 19. Types of difficulty encountered by locals to get to the market .......................... 40
Figure 20. Access to market information ................................................................................. 40
Figure 21. Proportion of respondents who have ever heard of Ebola Virus Disease ....... 41
Figure 22. Proportion of respondents who know the causes, signs and treatment of 
Ebola Virus Disease .................................................................................................................... 42
Figure 23. Knowledge of EVD causes...................................................................................... 42
Figure 24. EVD Considerations in community ........................................................................ 43
Figure 25. Regularity of listening to signs of EVD .................................................................. 44
Figure 26. Proportion of respondents based on known symptoms of EVD ....................... 44
Figure 27. Existence and ease of access to health facilities in the area ............................ 46
Figure 28. Proportion of respondents who have heard of COVID-19 ................................. 48
Figure 29. Proportion of respondents who know the causes, signs and treatment of 
COVID-19 ...................................................................................................................................... 49
Figure 30. Proportion of respondents according to different COVID-19 considerations .. 50
Figure 31. COVID-19 symptoms knowledge distribution ...................................................... 50
vi 
LIST OF ACRONYMS 
Abbreviation Signification
ADRA Adventist Development and Relief Agency 
ANOVA Analysis of Variance
CAPI Computer Assisted Personal Interviews
CDF Congolese Francs 
CNP Congolese National Police
DDS Dietary Diversity Score
DRC Democratic Republic of Congo
EVD Ebola Virus Disease
FABELU Food Assistance for Ebola-Affected and Food Insecure 
Populations of Beni and Lubero
FAD Fonds Africain de Développement 
FAO Food and Agriculture Organization
FCS Food Consumption Score 
FFP Food for Peace
HDDS Household Dietary Diversity Score
HH Household 
HZ Health Zone 
IRC International Rescue Committee
ISSNT-CIS Institut Supérieur de Statistique et de Nouvelles 
Technologies/Centre Informatique et Statistique
M&E Monitoring and Evaluation
MSF Médecins Sans Frontières
NIS National Institute of Statistics 
NRC Norwegian Refugee Council
OCHA Office for the Coordination of Humanitarian Affairs
ODK Open Data Kit
PADIR Projet d’Appui au Développement des Infrastructures Rurales 
PAM Programme Alimentaire Mondiale
PNUD Programme des Nations unies pour le développement
rCSI Reduced Coping Strategy Index
SPSS Statistical Package for Social Sciences
STATA Statistical Analysis
UNICEF Fonds des Nations unies pour l'enfance
UNWFP United Nations World Food Program
USAID United States Agency for International Development 
WHO World Health Organization
vii 
ACKNOWLEDGEMENTS
This report covers the baseline survey of the Food Assistance for Ebola-Affected and Food 
Insecure Populations of Beni and Lubero (FABELU) project in North Kivu and Mambasa 
in Ituri. The project is implemented by ADRA-RDC and funded by USAID and FFP. 
Data collected and analyzed in this baseline study relate to four axes: (i) household 
demographic and socioeconomic situation, (ii) food security, (iii) access to public 
infrastructure and market analysis and (iv) Ebola and COVID-19 perception.
The Household survey covered by this report was carried out by the Center for Informatics 
and Statistics (CIS) of “Institut Supérieur de Statistique et de Nouvelles Technologies” 
(ISSNT) on behalf of ADRA within the framework of the FABELU Project.
This study was conducted by ISSNT-CIS researchers under the coordination of Edson 
NIYONSABA SEBIGUNDA1, Célestin KIMANUKA RURIHO2 and Promesse KASEREKA 
KAVULIRENE3. 
The authors thank the entire ADRA team for supervising this work as well as all the other 
project partners for their diverse support.
To all investigators who worked selflessly in a risky field and to the reviewers of this report, 
may you find our gratitude here.
 
Finally, our gratitude goes to key informants, population of Mambasa and Mandima who 
gave their time to answer questions and to provide useful guidance.
Information provided and opinions expressed in this report do not necessarily reflect the 
view of ADRA and USAID/FFP
1As the director of the CIS-ISSNT, he coordinated the study: tool design, data collection, data analysis, manuscript 
editing. 
2 Specialist in the conduction of investigation, trained investigators, participated in data analysis and manuscript 
editing. 
3 Expert in digital data collection, configured the survey questionnaire on tablets, ensured the quality control of the 
server and took part in the data analyzes. He also ensured the translation of the questionnaire into the local language 
during the training. 
viii
EXECUTIVE SUMMARY
The present study carried out in June 2020 aimed at determining the basic indicators of 
the project “Food Assistance for Ebola-Affected and Food Insecure Populations of 
Beni and Lubero (FABELU)” in the province of Ituri. The survey of 420 inhabitants 
randomly selected from households in Mambasa and Mandima Health Zones allowed to:
• Determine the percentage of households whose Food Consumption Score (FCS) 
is poor, limited and acceptable;
• Detect the prevalence of households suffering from moderate or severe hunger 
(household hunger scale);
• Compute the Average Reduced Coping Strategy Index (rCSI) (according to sex, 
gender and type of household);
• Compute the average score for household food ration diversity.
• Perform market analysis;
• Identify community perceptions about Ebola and COVID19. 
We used cluster sampling with identical allocation between health zones. For in-depth 
exploration of the targeted themes, a standard questionnaire was administered using 
CAPI for individuals aged 18 years old and above. Households were randomly selected.
Among the randomly selected people, 46% were women, 65% of them did not go beyond 
primary school, 32% reached secondary school and only 3% reached university. The 
average age of the respondent is 38 years. The average number of people per household 
is 5 while the main source of income is agriculture (64%). The majority of households 
(74%) are residents of these regions, 14% being displaced while the remaining 12% are 
returnees.
Analysis of the data provided the following main results: 
1. Households are low income and contract debts to support their needs 
The study shows that 66% of the population live with less than US$ 1 per person per day. 
A typical household has a monthly income of US$ 21.10. In addition, 79% of households 
interviewed have debts and the typical household has a debt of US$ 10.30, which 
represents 49% of its monthly income.
ix
2. Food security indicators confirm severe vulnerability for a significant proportion 
of households
 
The overall average household Food Consumption Score (FCS) was calculated as 
“Borderline” with a score of 32.2, and 18% of households are in a situation of great 
vulnerability with a “poor” FCS (or less than 21). The proportions of households with a 
“poor” FCS are respectively 20% in Mandima Health zone (HZ) and 16% in Mambasa HZ. 
The quality (richness and diversity of food composition) and quantity of their food is 
insufficient. Just over half (51%) have “borderline” FCS and 31% have “acceptable” FCS.
The highest proportions of households in situations of severe vulnerability with a “poor” 
FCS are recorded in the health areas of Makele (43%), Mayuwano (43%), Banana (30%), 
Salama (30%) and Some (23%).
Internally displaced and returnee households are relatively more in situation of severe 
vulnerability than residents. Indeed, the proportion of households with a “poor” FCS is 
20% for internally displaced households, 20% for returnees and 17% for residents. 
Adult Female No Adult Male (FNM) households seem to be more severely vulnerable 
(23% of poor FCS) than Adult Male & Adult Female (M&F) households (13% of poor FCS) 
and Adult Male No Adult Female (MNF) households (8% poor).
Displaced households (20% of poor FCS) and returnees (20% of poor FCS) are relatively 
more in a situation of serious vulnerability than residents (17%).
The study shows that the reduced average coping strategy index (rCSI) for all the 
households visited is 19.1. This rCSI average is not significantly different between the two 
Health Zones and between status of households (IDP, Returnee and Resident). However, 
there is a significant difference according to the sex of the head of household, the health 
areas and the types of household. The most vulnerable households are those headed by 
women and Adult Female No Male Adult (FNM) Households. 
The proportions of households that have more difficulty obtaining food and therefore resort 
to high coping strategies are in Health Areas of Mayuwano (97%), Some (97%), Salama 
(97%), Banana (90%), Bukulani (80%), Lukaya (70%)4. 
The Coping strategies most used by households to obtain food in the two Health Zones 
are: 
4 The health areas of Mayuwano, Some and Lukaya are in the health zone of Mandima while the others (Salama, 
Banana, Bukulani) are in the health zone of Mambasa.
x 
• Purchase of less preferred and less expensive foods (92% of households), 
• Limit the size of portions at meals (72% of households),
• Reduce the number of meals taken a day (71% of households),
• Borrow food or rely on the help of a friend or a relative (67% of households), 
• Restrict the consumption of adults so that children can eat (54% of households). 
The household hunger scale (HHS) shows that 21% of households in the Mambasa Health 
Zone compared to 26% in the Mandima Health Zone show severe hunger. This is around 
58,765 inhabitants who suffer from severe hunger in the 2 Health Zones visited. In 
addition, internally displaced and returnee households experience more food deprivation 
than resident households (severe and moderate hunger).
Adult Female No Adult Male (FNM) households experience food deprivation and a higher 
proportion of severe hunger (31%) than Adult Male & Adult Female (F&M) households 
(23%) and Adult Male No Adult Female (MNF) households (21%).
The study also shows that some health areas are more affected by severe hunger. These 
are Banana (50%), some (47%), Lukaya (33%), Mayuwano (33%), Salama (30%) health 
areas5. 
Across all of the Health Zones in the study, household diets were not generally diverse. 
Households mainly consume basic foods (cereals and tubers), vegetables and oil. The 
mean dietary diversity score is 5.09. The Low Food Diversity Score is more observed in 
Adult Female No Adult Male (FNM) households (52%), in internally displaced households 
(54%), in returned households (48%) and logically in those falling into the Poor FCS 
category (83%).
At the Health Zone level, the diet is almost identical and dominated by the consumption 
of vegetables (6 days per week), staple crops (cereals and tubers) (5 days per week) and 
oil (6 days per week)
Households with a "Poor" Food Consumption Score eat only vegetables or leaves, staple 
crops and oil. The very low level of consumption of basic foods (cereals and tubers) among 
households with a poor FCS is a concern. In short, households with poor FCS are more 
affected by severe hunger (57%) with poor dietary diversity (83%) and are forced to resort 
to the most extreme coping strategies (96%).
5 Banana and Salama are in the Mambasa Health Zone while Lukaya and Mayuwano are in Mandima Health Zone. 
xi 
The main sources of basic food products (cereals and tubers) consumed are produced by 
the household itself and cash purchases at the market. The same also stands for
vegetables and fruits.
For all other food groups (meat, fish, poultry, legumes, milk and dairy products, oil, fat, 
sugar and other sweets), the main source is cash purchase at the market.
3. Access to the market is hindered by distance, poor road network and transport 
costs
We observe that 83% of the population firstly gets their supplies from the market, 68% of 
households invest in farming and maintaining home gardens and only 8% obtain food by 
other methods (donation from friends or family members and food as remuneration for 
work). 
There is a diversity of difficulties or barriers to market access. These main difficulties are 
linked: in fact, a poor road network would make the distance long and imply a significant 
average cost for the household. The average cost of transportation to the market is US $ 
4. We note that 86% walk to get to the market and spend an average of 1 hour walking to 
the market. 50% of the population report to find the distance to the market as being is long 
or very long.
4. Good knowledge and practices on Ebola virus disease and COVID-19 disease 
coexist with bad ones
The study found that Ebola virus disease (EVD) is known by many in the seven surveyed 
Health Zones. Overall, 95% of people know or have heard about Ebola virus disease.
A large proportion of respondents are aware of the signs or symptoms of EVD (79%). 64% 
know the causes of EVD but only 7% know that EVD can be treated. Results show that
about 45% of respondents know the most exposed persons. In addition, 64% of 
respondents said that they were vulnerable to EVD and 90% considered EVD to be a very 
dangerous disease in the community.
It was that, 42% of respondents said that they have been hearing regularly (1 to 3 months) 
that Ebola affected people.
To prevent EVD, respondents first cited hygiene practices (75%). Follow respectively: 
Avoid unprotected contact (49 %), safe funeral practices (39%), Avoid coming into contact 
xii 
with wild animals (36%), Vaccination (29%), Avoid regions and high risk activities (28%), 
Avoid unprotected sexual activity (26%) and others (2%).
About 90% of respondents declared having a health facility nearby. However, 17% do not 
have easy access to health facilities (long distance). The typical household is located 0.5 
km from the nearest health center.
Most people avoid contact with persons with Ebola (72%). Even after recovery, some 
continue to isolate them fearing to be contaminated (24%).
Ebola virus disease did not only affect health sector, it also affected the region's economy, 
as indicated 42% of respondents.
The study finds that 81% of people have heard of COVID-19 disease. However, few 
people know the causes (9%) and signs (29%) of the pandemic. Only 1% of respondents 
know the mode of treatment. The majority of inhabitants (73%) declared that COVID-19 is 
a dangerous disease and 40% recognized good hygiene practices as a prevention 
strategy.
According to statements by 55% of residents in Mambasa HZ and 35% in Mandima HZ, 
COVID-19 affects their work. Overall, agriculture and food security in the Territory of 
Mambasa have been affected negatively by COVID-19 as 27% of households reported.
1 
I. INTRODUCTION
1.1 BRIEF PROJECT DESCRIPTION AND CONTEXT 
The FABELU Project is implemented in Beni and Lubero territories, North Kivu and 
Mambasa territory in Ituri Province. The Upper North Kivu and Ituri provinces of the 
Democratic Republic of Congo (DRC) is currently experiencing renewed inter-ethnic 
violence and attacks by armed groups, leading to the displacement of thousands of 
persons6. High youth unemployment is a major issue associated with political violence, 
and unemployed youth often become active participants in armed conflict to escape 
vulnerability and marginalization, seeking peers, purpose, and economic security they 
perceive in armed groups7. Most youth in eastern DRC have lived their childhood, teenage 
hood and/or adult developmental years in conflicts beginning 1996 until nowadays. As a 
matter of fact, wars in eastern DRC have increased child labor and other exploitations in 
addition to the direct involvement of children as soldiers. In this context, the vulnerability 
of different youth segments in eastern DRC is largely shaped by characteristics that make 
individuals susceptible to exclusion, exploitation, abuse, violence, and/or recruitment into 
armed groups.8
Over the past three months, various armed groups have increased attacks and abuses in 
territories of Beni, Lubero and Mambasa. These areas have also suffered from the Ebola 
epidemic. The persistence of the Ebola epidemic continues to disrupt agro-pastoral 
activities, limiting people’s access to livelihoods in Beni, Lubero and Mambasa territories
in particular and in the , and in the eastern part of the DRC in general9. The latest report 
of 29th October 2019 indicates that, there were 3,151 confirmed cases of Ebola in Eastern 
DRC, with a high fatality rate (65%)10. Insecurity has reduced agricultural activities and 
efforts to combat the Ebola disease. This has led to a decrease in food security and, 
without humanitarian assistance, household food consumption will be impacted. In the first 
three months of 2019, the North Kivu province experienced between 31 and 66 % increase 
in hunger among displaced families and host communities11. In the same province, most 
displaced families, returnees or host families live with only one meal a per day and spend 
more than 75% of their income on food. The food consumption score reflecting the quantity 
and quality of the food—is poor (≤28) 12. 
6 FEWS NET, Democratic Republic of Congo Food Security Outlook Update, February to September 2019
7 Iiro Pankakoski, Youth livelihoods and the local conflict in North Kivu, 2017
8 USAID, overview of youth development perspectives in the eastern Democratic Republic of Congo, July 
9 FEWS NET, Democratic Republic of Congo Food Security Outlook, June to January 2020
10 WHO, Ebola Rapport de situation N°319, July 2019 11 NRC, DR Congo: Imminent hunger crisis threatens Ebola-stricken North Kivu. 12 OCHA, Emergency operational plan, for Ituri and North Kivu Province, January to June 2019.
2 
To address unmet needs, improve population’s conditions, and reduce vulnerability to 
food insecurity in Ebola-affected and food insecure areas, ADRA with the support from 
USAID/FFP is providing a 15-month Food Assistance for Ebola-Affected and Food 
Insecure Populations of Beni, Lubero (FABELU) in North Kivu and Mambasa in Ituri. 
These areas are chosen due to the high level of vulnerability to the food crisis aggravated 
by the current EVD epidemic that has negatively impacted the food security situation in 
these territories.
The overarching goal of the intervention is to contribute to improving the living conditions 
and reducing the risk of food insecurity of displaced persons, returnees and host families 
including young people in the health areas of Beni (Beni, Oicha, Kalunguta and 
Mutwanga), Lubero (Kayna, Lubero and Alimbongo) and Ituri (Mambasa and Mandima)
affected by the Ebola Disease Virus in the provinces of North Kivu and Ituri, Democratic 
Republic of Congo.
The FABELU project will alleviate suffering and maintain human dignity among vulnerable, 
displaced IDPs and host communities, with an emphasis on households experiencing 
crisis and food insecurity in Ebola-affected health zones of North Kivu and Ituri Provinces, 
Eastern DRC. Specific objectives are:
• To increase access to food and complementary resources to meet the immediate 
food needs of vulnerable populations through conditional food vouchers.
• To reduce vulnerability of crisis affected IDPs and host communities, especially 
youth, women, and children through community asset rehabilitation.
1.2 PURPOSE AND EXPECTED USE OF THE BASELINE REPORT 
The baseline will be useful to provide the USAID-funded FABELU project and partners 
with sufficient and accurate data/information to enable them set a benchmark on which 
outcomes and objectives of the project will be measured. The baseline will facilitate the 
monitoring and evaluation of the progress and effectiveness of actions implemented in 
this project in Ituri, East of the DRC.
1.3 OBJECTIVES OF THE BASELINE 
Specifically, the baseline survey seeks to establish benchmark values for the following 
outcome indicators;
1. Percentage of household with poor, borderline and acceptable food consumption 
score 
2. Prevalence of households with moderate or severe hunger (Household Hunger 
Scale)
3 
3. Mean reduced Coping Strategy Index (CSI –sex disaggregated by gendered 
household type)
4. Average household dietary diversity score (HDDS)
5. Total number of households with access to local markets
6. Percentage of beneficiaries who have access to basic public infrastructure
7. Awareness and Perception of Ebola
8. Awareness and Perception of COVID-19 outbreak
1.4 LIMITATIONS OF BASELINE 
Due to health restrictions imposed by the context of the COVID-19 pandemic and the fear 
of the population to regroup for focus-group sessions, the study did not use the qualitative 
approach. It is limited to the quantitative approach. However, since this is the baseline 
study, this does not affect the relevance of the information collected. The mid-term and 
final evaluation studies will take into account both approaches to provide in-depth
explanations of the indicators found in relation to the targets.
1.5 LITERATURE REVIEW 
In most regions of DRC, the majority of households (88%) are residents, 9% being 
displaced while the remaining 3% are returnees. 87.5% of households have debts and the 
typical household has a debt of US$ 14.71 which represents 83% of its monthly income 
(US$ 17.65). Almost all of the households (93%) live with less than US$ 0.5 per person 
per day. The overall average household Food Consumption Score (FCS) is 35.3, and 14% 
of households are in a situation of severe vulnerability with a “poor” FCS (or less than 21). 
The quality and quantity of their food is inadequate. The average reduced coping strategy 
index (rCSI) for the set of households is 14.6 (Marivoet et al., 2019)13. 
Poor or borderline food consumption compared to the national average is located in 
Équateur (10% poor consumption and 37% borderline consumption), followed by Kasaï 
Oriental (7% poor consumption and 37% borderline), South Kivu (12% poor consumption 
and 31% at the limit), and North Kivu (13% poor consumption and 25% at the limit). Poor 
households with food above the national average live in North Kivu (13%), South Kivu 
(12%), Katanga (12%), Équateur and Kasaï Occidental (10%) (WFP, 2014)14. 
Between 40 and 60 percent of the households are forced to adopt negative coping 
mechanisms to cope with the situation, while nearly 15 percent went to the extreme, selling 
13 Marivoet, W., Becquey, E. & Van Campenhout, B. How well does the Food Consumption Score capture diet 
quantity, quality and adequacy across regions in the Democratic Republic of the Congo (DRC)?. Food 
Sec. 11, 1029–1049 (2019). https://doi.org/10.1007/s12571-019-00958-3 14 Programme alimentaire mondiale (PAM), 2014 Analyse approfondie de la sécurité alimentaire et de la vulnérabilité 
(CFSVA) http://www.wfp.org/food-security ou en contactant wfp.vaminfo@wfp.org
4 
all their assets. In Tanganyika, the food consumption score (FCS) varied from 39 to 60 
percent before the shock, the coping strategy index (CSI) was 20 and the overall GAM 
rate was 12 percent. After the conflict, the FCS deteriorated to between 54 and over 90 
percent, the CSI from 22 to 40 and the Global acute Malnutrition rate to 16 percent. In the 
Kasaï, the FCS reached nearly 84 percent of households, the CSI was at 16.2 and the 
GAM rate varied between 11 and 14 percent, with a mortality rate for children under five 
ranging from 1.13 to 2.77% (FAO, 2017)15. 
At the national level, 51% of household food consumption was bought; 42% came from 
own production, 3% from fishing, hunting, gathering and the remaining 4% was borrowed, 
received as gifts or in the form of food aid. More than half (52%) of the food consumed by 
poor households came from their own production. Market purchases are the second most 
important source of food for poor households (40%), followed by fishing, hunting and 
gathering (4%) households in relatively wealthy provinces depend mainly on purchases 
made at the market as a source of food: Sud-Kivu (68%), Nord-Kivu (66%), Bas-Congo 
(59%), Kasaï Occidental (53%). Bandundu is the least dependent province on market 
purchases (24%), followed by Katanga (33%). (WFP, 2014). 
In South Kivu only 5% of the population has an acceptable food consumption score, 26% 
have borderline FCS and 69% have poor FCS (<28), thus classifying the households 
surveyed in a situation of severe vulnerability in terms of food security. The coping strategy 
most used by households are the consumption of less expensive or less preferred foods 
(96%), the reduction of the quantity of meals (95%), the reduction of the consumption of 
adults for the benefit of children ( 95%) and borrowing food or seeking help from friends, 
neighbors and family (94%) (ACTED, 2018)16. 
Only 17% of the population in rural areas have access to water from an improved source 
(United Nations, 2013)17; infrastructure for marketing and conservation of agricultural 
products is in an advanced state of degradation. Peasants' technical support services are 
practically absent. This situation has created a paradox in rural areas: agricultural 
production in fields that cannot find buyers and shortages of food in cities. The food deficit 
is estimated at more than 30% (FAD, 2011)18. 
Adding to that, Active conflict and insecurity make Ebola epidemic one of the most 
complex ever faced, with the disease anchored in areas with decades of endemic mistrust 
15 FAO. 2017. The Democratic Republic of the Congo. Response Plan 2017–2018, Rome. 14 pp. 16 ACTED, Juin, 2018. Rapport d’évaluation des besoins en sécurité alimentaire et nfi axe lubishako1 – lubishako2, 
territoire de fizi, sud-kivu - https://reliefweb.int/sites/reliefweb.int/files/resources/acted_msa_axe_lubishako1-
lubishako2.pdf
17 United Nations, 2013. République Démocratique du Congo, Plan d’action humanitaire 
https://reliefweb.int/sites/reliefweb.int/files/resources/2013DRCHAPFR.pdf
18Fonds Africain de Développement (FAD). Aout, 2011.
Projet d’Appui au Développement des Infrastructures Rurales (PADIR). République Démocratiqe du Congo. 
5 
and violence particularly in Eastern DRC. This epidemic continues unabated, households 
that were already living with food insecurity are now faced with reduced purchasing and 
economic power as people’s access to their livelihoods are limited (WHO, 2019)19. 
Even without restriction, the Ebola virus has negative short-term and medium-term effects 
on agro-pastoral activities in the eastern part of the DRC. Even before Ebola virus disease 
(EVD), people were already suffering from high chronic food insecurity in this region. In 
fact, 67% of the Ituri population has a high level of food insecurity and 26% of the 
population in the areas affected by EVD in North Kivu. While hotspot areas such as Beni 
can import foodstuffs and supplies from neighboring areas to compensate for decreased 
agro-pastoral activity, people living in the other areas rely on unrestricted access to 
markets in a survival economy where freedom of movement is essential to access food 
and survive (Jerving et al,, 2019)20. 
It has to be noticed that in a study conducted by Fauche et al., in 201521; 2% of population 
have never heard about EDV, 46.8% men and 39% women have a good knowledge of 
the EDV. Then 66.3% of the population would choose to call the health department if they 
were in front of a person with symptoms of EDV, and 98.8% perceive that the EVD is 
dangerous along with 67.9% are willing to help the Congolese authorities to educate the 
population to prevent it. The main source of information is television (88.4%). 
Regarding COVID19, a survey done by Target SARL22, revealed that only 39% of those 
questioned considered that COVID-19 is a serious disease against 58% who do not find 
it serious and 5% who do not comment. Almost half of the people surveyed (46%) said 
that there were no cases of the sick in their immediate environment and 21% thought that 
the number of confirmed cases in their province was low. The same population (20%) 
agreed that COVID-19 is more prevalent abroad and that it is a disease whose impact is 
exaggerated by the media and politicians (12%). As for the second part, focusing on 
whether or not measures taken by the Republic President are satisfied, the trend here is 
rather upward: 70% of those questioned say they are satisfied with the measures taken 
by the Republic President to stop the pandemic against 16% who are not and 14% without 
opinion. With the exception of South Kivu (41%), the satisfaction scores on the actions 
19 World Health Organization (WHO), Ebola Rapport de situation N°319, July 2019 20 Jerving, Sara. "Q&A: How the Ebola Declaration Could Threaten Food Security in DRC." Devex. July 18, 2019. 
Accessed 26 July 
21 Fauche, Adelin & Mukebayi, Kadimanche & Bakenje, Marlin & Pilipili, Koyange & Bongo, G.N. & Konga, Museu & 
Songo, Baukaka & Ngombe, Nadège & Mbemba, Théophile & Ngbolua, Koto-Te-Nyiwa. (2015). Knowledge, Attitude 
and Perception of the Population Related To Ebola Virus Disease in Kinshasa City, Democratic Republic Of The 
Congo. Journal Of Advancement In Medical And Life Sciences. 
https://www.researchgate.net/publication/279853040_Knowledge_Attitude_and_Perception_of_the_Population_Rel
ated_To_Ebola_Virus_Disease_in_Kinshasa_City_Democratic_Republic_Of_The_Congo
22 Target SARL, 2020. les congolais et la pandémie de COVID19. https://zoom-eco.net/a-la-une/rdc-39-de-congolais￾conscients-de-la-gravite-de-COVID19-etude-de-target-sarl/
6 
taken by the provincial governments are higher than 50% with a very high score in Haut￾Katanga (80%). 
And according to Kinshasa Digital (2020)23; 73% are worried or very worried; 17% can no 
longer work; 39% would call the toll free number in case of symptoms; 76% see an 
increase in the prices of essential products. 35% have difficulty finding masks, 29% of coal 
and flour whether it is corn flower or cassava which are the most consumed in DRC, 28% 
of Chloroquine, 26% of meat and vegetables. 
1.6 SUMMARY OF METHODOLOGY 
1.6.1 Preliminary work for the survey 
Preliminary work which came before data collection consisted respectively in:
• Development of survey tools: adapted questionnaire and training manual,
• Configuration of the questionnaire on tablet under Kobo Collect,
• Recruitment and training for 2 days of investigators and supervisors (see list in 
Annex 1), and
• The development of an activity schedule and a team deployment plan.
1.6.2 Sampling
The identification of the sample will follow the procedure below: 
23 Kinshasa Digital, 2020. Craintes et réactions face au COVID-19 en RDC, Sondage auprès de la population. 
https://www.atibt.org/wp-content/uploads/2020/04/RDC-Sondage-COVID19-Craintes-et-réactions-20200414-02.pdf
1. Select the territory : Mambasa
2. Select Health Zones : Mambasa and Mandima
3. Select the Heath Areas ; affected by EBOLA
4. Select the Villages: Affected by EBOLA
5. Select the HH and the people for interview 
7 
The calculated sample size is 402, rounded to 420 HHs (this was computed at 5% error 
margin, 95% confidence interval, 50% response distribution, Population size of 7,000 and 
a 10% contingency for non-response/attrition). 
The technique applied is that of “one-stage cluster sampling with identical allocation 
between health zones”. The 420 households are distributed in 14 clusters. Candidates for 
the interview were men, women, girls and boys in households. The preference was given 
to the head of household or his/her spouse. Another adult (over 18 years of age) was
interviewed in the absence of the household head. All data were collected using CAPI with 
the ODK Collect application.
1.6.3 Survey implementation 
1.6.3.1 Investigator training 
The theoretical and practical training of investigators for quantitative data collection took 2 
days and focused on:
• The objectives of the baseline survey; 
• Household survey methodology: random walk in dense villages and scattered 
villages; 
• Basic rules, basic techniques necessary in contact with the population, role and tasks 
of investigators; 
• Explanation and translation of the questionnaire into the main survey language; 
• Explanation of the security context and instructions, data quality control mechanism 
and the deployment plan.
8 
1.6.3.2 Sample presentation
The table 1 presents the Baseline sample by Health Zone and heath areas. 
Table 1.Sampling
Territory Health Zone Cluster / Health areas #HH
Mambasa
Mambasa 
Population in 
2019 :100,565
Banana 30
Binase 30
Bukulani 30
Makoko2 30
Mambasa 30
Salama 30
Tobola 30
Mandima
Population in 
2019 : 144,909
Biakako mayi 30
Biakato mine 30
Katanga 30
Lukaya 30
Makeke 30
Mayuwano 30
Some 30
Total 420 HH
1.6.4 Data analysis 
Data collected by CAPI using ODK Collect application was compiled in Excel format. Then, 
exported and analyzed with SPSS 23 and STATA 15.0 
For statistical tests, we consider a result as statistically significant when the probability is 
less or equal to 0.05 (p≤ 0.05). To test the dependence of two characters represented in 
a contingency table, we used the Chi-square independence test. While to compare the 
means of several populations, we used the Analysis of Variance (ANOVA) test.
9 
II. FINDINGS 
2.1 SOCIO-DEMOGRAPHIC CHARACTERISTICS 
2.1.1 Distribution of households by sex and age of respondents
As Figure 1 illustrates, both sexes are validly represented among the respondents. 
Overall, 53.6% of men were surveyed compared to 46.4% of female. 
Figure 1. Distribution of households by sex of respondents
The average age of respondents is 38 years old with a standard deviation (SD) of 13.9. 
Their age varies between 17 and 85 years (Table 2). 
Table 2. Age of respondents 
Health Zone Obs. Mean Std. Dev. Min Max
Mambasa 18-59 194 35.9 10.6 17 58
Mandima 18-59 182 35.0 10.7 17 59
Aggregate 18-59 376 35.5 10.7 17 59
Mambasa 60+ 16 65.1 4.7 60 75
Mandima 60+ 28 67.3 6.7 60 85
Aggregate 60+ 44 66.5 6.1 60 85
Mambasa 210 38.2 12.9 17 75
Mandima 210 39.3 15.0 17 85
Aggregate 420 38.7 13.9 17 85
55.2%
51.9%
53.6%
44.8%
48.1%
46.4%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Mambasa (n=210)
Mandima (n=210)
Aggregate (n=420)
PERCENTAGE (%) OF RESPONDENTS
Male Female
10 
2.1.2 Level of education 
Figure 2 illustrates that, 65.3% of respondents have not gone beyond primary school, 
19.8% of them did not attend any school level. Almost 32% are in secondary school and 
only 3% attended university.
Figure 2. Distribution of education level of respondents
2.1.3 Characteristics of pregnant household members
Among all the members of households surveyed, 84 cases of pregnancy were identified. 
Overall, their average age is 27, the youngest being 15 and the oldest 46 years old. Among 
the 84 cases, 16 women are household heads of, 47 are wives of the head of household, 
15 are children of the head of household and 6 are sisters of the head of the household.
Table 3.Profile of pregnant persons in the household
Health Zone Obs. % Mean Std. Dev. Min Max
Head of household 16 19% 33.0 5.9 18 42
Wife of the head of household 47 56% 27.1 7.2 17 46
Daughter of the head of household 15 18% 23.0 8.3 15 41
Sister of the head of household 6 7% 21.7 3.7 17 25
Aggregate 84 100% 27.1 7.7 15 46
12.9%
26.7%
19.8%
0.5%
0.5%
0.5%
45.2%
44.8%
45.0%
35.7%
28.1%
31.9%
5.7%
0.0% 2.9%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa (n=210) Mandima (n=210) Aggregate (n=420)
PERCENTAGE (%) OF RESPONDENTS
No level No formal education Primary High school Tertiary
11 
2.1.4 Household size 
The results in table 3 show that the average household size is 5 people with a standard 
deviation of 2.6. Household sizes differ significantly24 between the two health zones at the 
5% error threshold. 
Table 4.Household size of respondents 
Health Zone Obs. Mean Std. Dev. Min Max
Mambasa 210 5.7 2.8 2 19
Mandima 210 5.1 2.4 1 16
Aggregate 420 5.4 2.6 1 19
Difference≠0 0.6 0.4 - -
2.1.5 Household residential status and household gender 
Figure 3 reveals that Adult Male & Female Adult (F&M) households represent 79%. In our 
random draw, we did not observe Child Only No Adult (CNA) households.
Figure 3. Household gender status
As shown in figure 4, the majority of households (73.6%) are made up of residents. Then 
comes the internally displaced (14.5%) and finally the returnees with 11.9%. The 
distribution of households by current residence status differs significantly between the 
two Health Zones25. We see that there are more returnees and internally displaced 
people in Mandima compared to Mambasa.
24 pv = 0.0128 25 p<0,0001
16.2% 14.3% 15.2%
77.6% 80.5% 79.0%
6.2% 5.2% 5.7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa (n=210) Mandima (n=210) Aggregate (n=420)
PERCENTAGE (%) OF HH
Adult Female No Adult Male (FNM) Adult Male & Adult Female (F&M)
Adult Male No Adult Female (MNF) Child Only No Adult (CAN)
12 
Figure 4. Household status 
2.2 OCCUPATION, HOUSEHOLD INCOME AND DEBT 
2.2.1 Current main occupation 
The main occupation of respondents is agriculture (63.8%), which is necessary for the 
survival of the majority of households in the two health zones Then there is the agricultural 
workforce (7.9%), civil servants (7.9%). We cannot neglect agricultural labor (5%) and 
small scale business (5%), handicrafts (5.5%), trading (5.5%) and daily labor (5.5%) as 
also main occupations. All other occupations (beekeeping, grazing, association support, 
relative support and begging) represent 4.3%.
13.3% 15.7% 14.5%
84.8%
62.4%
73.6%
1.9%
21.9%
11.9%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa (n=210) Mandima (n=210) Aggregate (n=420)
PERCENTAGE (%) OF HH
Internally displaced persons (IDP) Resident Returnee
13 
Figure 5. Respond current main occupation for living 
2.2.2 Monthly income 
The respondent's average monthly income is 67,663.1CDF, or US$ 35.60 (SD=48.50) 26. 
Since there is a large dispersion, we consider the median income. Thus, a typical 
household has a median monthly income of 40,000 CDF (US$ 21.10). This income is 
very low. There is also a significant27 difference of 22,450CDF, or US$ 11.80, between 
26 Exchange rate of US$ 1 = 1900 CDF 27 Pr(|T| > |t|) = 0.0124
63.3% 63.8% 63.6%
0.5% 0.0% 0.2%
7.1% 3.8% 5.5%
4.3%
6.7% 5.5%
1.0% 1.0% 1.0%
9.5%
6.2% 7.9%
5.2%
5.7%
5.5%
5.7% 10.0% 7.9%
1.9% 1.4% 1.7% 0.0% 1.0% 0.5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa (n=210) Mandima (n=210) Aggregate (n=420)
PERCENTAGE (%) OF HH
Agriculture
Beekeeping
Handicraft
Trading (private small business)
Grazing
Civil employee
Dally labor
Agriculture labor
Begging
Support from charity associations
Relative support (inside or outside DRC)
Other
14 
the average incomes of two health zones covered by the study. With US$ 23.70 per 
month, the Mambasa health zone has a median income higher than that of Mandima (US$
18.40 per month). 
Table 5 Household monthly income 
Health Zone N Average 
Monthly 
Income in CDF
Average 
Monthly 
Income in US$
standard 
deviation
Median income in 
US$
Mambasa 210 78888.1 41.50 59.80 23.70
Mandima 210 56438.1 29.70 32.70 18.40
Aggregate 420 67663.1 35.6 48.5 21.1
2.2.3 Income per person per day 
It can be observed in the table 6 below that the majority of inhabitants encountered (66%) 
live far below the poverty line of US$ 1 per person per day. Two out of three people are in 
this situation, Mandima health zone having a lower income than Mandima.
Table 6.Income per person per day 
Income range 
in CDF
Income 
range in $
n
Mambasa %
n
Mandima %
n
aggregate
% of total 
0 – 950 0 – 0.50 64 30% 86 41% 150 36%
951 -1900 0.51-1.0 66 31% 58 28% 124 30%
1901 and over 1.01 et plus 80 38% 66 31% 146 35%
Total 210 100% 210 100% 420 100%
 
2.2.4 Household debt 
The study shows that out of the 420 households surveyed, only 90, (or 21.4%) declare to 
be debt free. The average debt is 56,676.9 CDF or US$ 29.8 (SD=64.7). Considering the 
median debt, the debt of the typical household is estimated at 19,500 CDF, or US$ 10.3. 
The income of the typical household being US$ 21.1, the debt represents a proportion 
48.8% of income. Households in Mandima are more in difficulty than those in Mambasa.
Table 7.Household level of debt in Mambasa and Mandima HZ
Health Zone N Average debt in 
CDF
Average debt in 
US$
Standard 
deviation
Median debt in 
US$
Mambasa 210 52709.8 27.7 68.5 6.8
Mandima 210 60644.1 31.9 60.7 11.3
Aggregate 420 56676.9 29.8 64.7 10.3
15 
2.3 HOUSEHOLD FOOD SECURITY MEASURES 
2.3.1 Meals shared by household members 
a. Meals taken by children aged 6 to 59 months 
We find that 28% of children aged between 6 to 59 months have at most one meal the 
day before the survey. Among them, there are 8% who usually take at most one meal per 
day.
Table 8.Distribution of households according to the number of Meals taken by children aged 6-
59 months
Meals taken yesterday by household 
members aged 6 to 59 months
Number of meals USUALLY taken by household 
members aged 6-59 months
Number Frequency % Number Frequency %
0 37 11% 0 2 1%
1 60 17% 1 23 7%
2 181 53% 2 190 55%
3 60 17% 3 119 35%
4 and over 5 1% 4 and over 9 3%
Total 343 100% Total 343 100%
b. Meals taken by household members aged 5 years and above 
As illustrated in table 9, it can be seen that 35% of households provided zero or one meal 
for their members aged 5 years and over, the day before the survey. The majority of 
respondents, 58%, reported usually eating two meals a day.
Table 9.Distribution of households according to the number of Meals taken by members aged 5 
years and over
Meals taken yesterday by household 
members aged 5 years and over 
Number of meals USUALLY taken by household 
members aged 5 years and over
Number Frequency % Number Frequency %
0 18 4% 0 4 1%
1 130 31% 1 43 10%
2 213 51% 2 242 58%
3 56 13% 3 127 30%
4 and over 3 1% 4 and over 4 1%
Total 420 100% Total 420 100%
16 
2.3.2 Food Consumption Score 
The Food Consumption Score (FCS) is a composite score based on dietary diversity, 
frequency and relative nutritional importance of different food groups. It is a basic indicator 
recommended by the VAM (Vulnerability Analysis and Mapping) in food security analyses.
The FCS is calculated based on the past 7-day food consumption recall for the household 
and classified into three categories: poor consumption (FCS = 1.0 to 21); borderline (FCS 
= 21.5 to 35); and acceptable consumption (FCS = >35.0)28. The FCS is a weighted sum 
of food groups. The score for each food group is calculated by multiplying the number of 
days the commodity was consumed and its relative weight. Eight food groups are 
considered.
The following thresholds of FCS are used to categorize households into three food 
consumption groups – Poor, Borderline and Acceptable: 
Table 10.Interpretation of FCS
FCS
Food 
Consumption 
Score
Description
Poor 1-21
An expected consumption of staple 7 days, vegetables 
5-6 days, sugar 3-4 days, oil/fat 1 day a week, while 
animal proteins are totally absent : Inadequate 
quantity and quality
Borderline 21.5 -35
An expected consumption of staple 7 days, vegetables 
6-7 days, sugar 3-4 days, oil/fat 3 days, 
meat/fish/egg/pulses 1-2 days a week, while dairy 
products are totally absent: Inadequate quality
Acceptable > 35
As defined for the borderline group with more number 
of days a week eating meat, fish, egg, oil, and 
complemented by other foods such as pulses, fruits, 
milk: Adequate food
. 
2.3.2.1 FCS by Health Zone 
The overall average household FCS is Borderline, at 32.2 (SD = 14.3)29. A typical 
household has an FCS of 29.030. In all of the households visited, it can be seen that 18% 
are in a situation of severe vulnerability with a "poor" FCS (or less than 21). The quality 
and quantity of their food is inadequate. We also find that more than half of them (51%) 
have borderline FCS and 3 out of 10 households (31%) have acceptable FCS. These 
28 USAID Food for Peace, Indicators for Emergency Program Performance Indicator Reference Sheets, February 
2019.
29 We note that there is a strong dispersion between households (coefficient of variation = 44% >25%). We prefer to 
use the median score.
30 This is the median FCS. 
17 
results are comparable to the findings of Marivoet et al. (2019) 31 where the national 
average consumption score was evaluated at 35.3. The same study found that 14% of 
households were in a situation of severe vulnerability compared to 18% identified in 
Mambasa in this study. 
The average household FCS is not different between the two Health zones and according 
to the sex of the Head of household. However, we note that the average FCS is lower32
in internally displaced households (27.7) compared to returnees (28.3) and residents 
(33.8) as we can read in Appendix 3.1.
In addition, Adult Female No Adult Male (FNM) households have the lowest average FCS 
(28.0) compared to Adult Male & Adult Female (F&M) households (31.1) and Adult Male 
No Adult Female (MNF) households (32.3) 33 (details in appendix 3.1)
The highest poor FCS are recorded in the health areas of Makele (43%), Mayuwano 
(43%), Banana (30%), Salama (30%) and Some (23%). For more details, see the Table 
in appendix 3.3.
 
The figure 6 shows that the distribution of households according to the classes of the food 
consumption score is not significantly different from one Health Zone (HZ) to another34. 
The proportions of households with a “poor” FCS are respectively 20% in Mandima HZ
and 16% in Mambasa HZ. The two health zones have relatively the same proportion of 
households in a situation of severe vulnerability. In each of two health zones, the 
proportion of households with “poor” and “borderline” FCS is estimated at 69%.
31 Marivoet, W., Becquey, E. & Van Campenhout, B. How well does the Food Consumption Score capture diet 
quantity, quality and adequacy across regions in the Democratic Republic of the Congo (DRC)?. Food 
Sec. 11, 1029–1049 (2019). https://doi.org/10.1007/s12571-019-00958-3 32 The difference in FCS average is significantly different depending on the status of the household (p=0,001). 33 The difference in FCS average is significant between households according to their status (p=0,033) 34 p=0,41
18 
Figure 6. Distribution of FCS by Health Zone 
2.3.2.2 FCS by household type 
In terms of vulnerability, there is no significant difference between Adult Female No Adult
Male households35. However, the proportions of “poor” FCS are 20% in Adult Female No 
Adult Male households, 18% in Adult Female & Adult Male households and 13% for Adult 
Male No Adult Female36.
Figure 7. Distribution of Food Consumption score by household type 
35 The statistical test shows that the difference is not significant (p=0.128) 36 We have presented the distribution for Adult Male & No Adult Female households for information, given their small 
size. In our sample, we did not find Child Only No Adult (CAN) households.
16% 20% 18%
53% 49% 51%
31% 31% 31%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n=420)
PERCENTAGE (%) OF HH
FCS Poor FCS Borderline FCS Acceptable
20% 18% 13%
61%
48% 63%
19%
34% 25%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Adult Female & No Adult Male
(n=64)
Adult Male & Adult Female
(n=332)
Adult Male No Adult Female
(n=24)
PERCENTAGE (%) OF HH
FCS Poor FCS Borderline FCS Acceptable
19 
2.3.2.3 FCS according to household status 
Households of IDPs and returnees are relatively more in situations of severe vulnerability 
(20% “poor” FCS) than residents (17% “poor” FCS)37. The proportion of households with 
“borderline” FCS are also higher in the households of IDPs and returnees as shown in the 
following graph.
Figure 8. Distribution of FCS according to household status 
2.3.2.4 FCS according to whether or not the household received food aid in 
the past two months
Households that received food aid in the last two months before the survey date have 
FCSs not significantly different from those of households that did not receive food aid38. 
The most likely assumption would be that the aid received is insignificant.
37 The statistical test shows that the difference in distribution between internally displaced households and returnees is 
not significant (p = 0.102).
38 The statistical test shows that the difference in the distribution of households with or without food aid is not 
significant between the FCS classes (p = 0.971).
20% 17% 20%
66%
46%
62%
15%
36%
18%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Internally displaced
persons [IDP] (n=61)
Resident (n=309) Returnee (n=50)
PERCENTAGE (%) OF HH
FCS Poor FCS Borderline FCS Acceptable
20 
Figure 9. Distribution of FCS according to whether or not the household received food aid
2.3.3 Reduced Coping Strategy Index (rCSI)
2.3.3.1 Elements for computation of the rCSI
The Reduced Coping Strategy Index (rCSI) is often used as a proxy indicator of household 
food insecurity. Households were asked about how often they used a set of five short￾term food based coping strategies in situations in which they did not have enough food, 
or money to buy food, during the one-week period prior to interview. The information is 
combined into the rCSI which is a score assigned to a household that represents the 
frequency and severity of coping strategies employed. First, each of the five strategies is 
assigned a standard weight based on its severity. These weights are: Relying on less 
preferred and less expensive foods (=1.0); Limiting portion size at meal times (=1.0); 
Reducing the number of meals eaten in a day (=1.0); Borrow food or rely on help from 
relatives or friends (=2.0); Restricting consumption by adults for small children to eat 
(=3.0). Household rCSI scores are then determined by multiplying the number of days in 
the past week each strategy was employed by its corresponding severity weight, and then 
summing together the totals39. 
Based on the country’s context, the total rCSI score is the basis to determine and classify 
the level of coping: into three categories: No or low coping (rCSI= 0-3), medium (rCSI = 
4-9) and high coping (rCSI ≥10).
39 Malick Ndiaye ; Indicateurs de la sécurité alimentaire, PAM, Dakar, June 2014.
22% 17%
38% 33%
62% 50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Did not receive food aid (n=402) Received food aid (n=18)
PERCENTAGE (%) OF HH
FCS Poor FCS Acceptable FCS Borderline
21 
2.3.3.2 Comments on findings 
The Coping Strategy index reflects the difficulties households face in feeding themselves.
 The results in Table 11 and Appendix 3.2 show that the rCSI for all the households 
visited is 19.1. The mean of rCSI found is higher than that of 14.6 identified by Marivoet 
and al. (2019) at national level. This average rCSI does not differ significantly between 
the two Health Zones and according to the status of the household. Note that there is 
a significant difference in rCSI between the Health Areas, according to the sex of the 
Head of household and according to the type of household;
Table 11. Descriptive statistics of Reduced Coping Strategy Index
Unity 
Adult Female 
& Adult Male 
(FNM)
Adult Female No 
Adult Male (FNM)
Adult Male No 
Adult Female 
(MNF)
Aggregate 
rCSI Mean 18.1 24.5 19.3 19.1
Std. Dev. 13.7 12.9 13.3 13.7
CI at 95% [16.6-19.5] [21.2-27.7] [14.3-25.1] [17.8-20.5]
rCSI Median 16 23.5 19.5 17
n 332 64 24 420
 As can be seen in the figure 10, overall, 70% of households use high coping strategies. 
Taking into account the survival strategies adopted, households headed by women 
are more food insecure than those headed by men. In addition, Adult Female No Adult 
Male (FNM) households are more food insecure than Adult Female & Adult Male 
(FNM) households and Adult Male No Adult Female (MNF) households; 
 The proportions of households that have more difficulty obtaining food and therefore 
resort to high coping strategies are the highest in the Health Areas of Mayuwano 
(97%), Some (97%), Salama (97%), Banana (90%), Bukulani (80%), Lukaya (70%) 
For more details, see the Table in Appendix 3.2. 
22 
Figure 10. Coping Strategy Index by household category
2.3.3.3 Main coping strategies used 
As can be seen in the figure 11, the survival strategies most used by households in case 
of difficulty obtaining food in the two Health Zones are: relay on less preferred and less 
expensive foods (92%), Limit portion size to meals (72%), reduce number of meals eaten 
in a day (71%), borrow food or rely on the help from a friend or relative (67%), Restrict 
consumption of adults in other for small children to eat (54%). This graph also presents 
the details by health zone and confirms that the strategies used by households are not 
significantly different between them. These results are very close to those obtained in the 
71%
69%
81%
65%
91%
66%
79%
87%
67%
68%
70%
18%
21%
11%
23%
6%
23%
8%
11%
21%
20%
19%
11%
10%
8%
12%
3%
12%
13%
2%
12%
12%
11%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Mambasa
Mandima
Household head Female
Household head Male
Adult Female No Adult Male (FNM)
Adult Male & Adult Female (F&M)
Adult Male No Adult Female (MNF)
Internally displaced persons (IDP)
Resident
Returnee
Aggregate
PERCENTAGE (%) OF HH
High Coping Medium No or low Coping
23 
study conducted in South Kivu by ACTED (2018) 40, where the most used strategy (96%) 
is to rely on less preferred and less expensive foods.
Figure 11. Main strategies used by household
2.3.4 Household Hunger Scale (HHS)
The HHS is an index of food deprivation in households. It focuses on the quantity of 
food, food access and does not measure the quality of the diet.
The following HHS cut-offs are used to categorize households into three hunger groups, 
none or light, moderate and severe:
40 ACTED, Juin, 2018. Rapport d’evaluation des besoins en securite alimentaire et nfi axe lubishako1 – lubishako2, 
territoire de fizi, sud-kivu - https://reliefweb.int/sites/reliefweb.int/files/resources/acted_msa_axe_lubishako1-
lubishako2.pdf
93%
76%
75%
66%
56%
91%
69%
68%
69%
52%
92%
72%
71%
67%
54%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rely on less preferred and less
expensive foods?
Limit portion size at mealtimes?
Reduce number of meals eaten in a day?
Borrow food, or rely on help from a
friend or relative?
Restrict consumption by adults in order
for small children to eat?
PERCENTAGE (%) OF HH
Aggregate (n=420) Mandima (n=210) Mambasa (n=210)
24 
0-1 score: None or light hunger
2-3 scores: Moderate hunger
4-6 scores: Severe hunger 
 The results in Table 12 show that in all the health zones concerned by this study, 
73% of households are affected by severe or moderate hunger and only 27% 
households are characterized by none or light hunger. 
 Specifically, we note that in the Mambasa Health Zone, 71% of households manifest 
severe or moderate hunger and only 28% do not manifest hunger. For the Mandima 
Health Zone, more than 73% of households are characterized by severe or moderate 
hunger and only 27% do not manifest hunger41. 
 The study also shows that health areas are disproportionally affected by severe 
hunger. These are Banana (50%), Some (47%), Lukaya (33%), Mayuwano (33%), 
Salama (30%) health areas.
 In addition, internally displaced and returnee households experience more food 
deprivation than resident households (severe and moderate hunger).
 Note, however, that the difference observed in the distribution of households according 
to the level of hunger is not significant when we consider the sex of the head of 
household.
 Adult Female No Adult Male Households experience food deprivation and a higher 
proportion of severe hunger (31%) than Adult Female & Adult Male households (23%) 
and Adult Male No Ault Female households (21%).
Table 12.Distribution of household according to the level of the HHS
Category n None or light 
hunger
Moderate 
hunger
Severe 
hunger
P-value
Health zone 
Mambasa 210 28% 51% 21% 0,5100
Mandima 210 27% 47% 26%
Health areas 
Biakako Mayi 30 30% 50% 20% < 0,0001
Biakato Mine 30 37% 47% 17%
Katanga 30 43% 50% 7%
Lukaya 30 23% 43% 33%
Makeke 30 40% 33% 27%
Mayuwano 30 10% 57% 33%
Some 30 3% 50% 47%
Banana 30 0% 50% 50%
41 The distributions of households according to the level of severity of hunger do not differ significantly between the 
two Health Zones of the study (p = 0.510)
25 
Category n None or light 
hunger
Moderate 
hunger
Severe 
hunger
P-value
Binase 30 43% 37% 20%
Bukulani 30 7% 67% 27%
Makoko2 30 47% 43% 10%
Mambasa 30 40% 50% 10%
Salama 30 17% 53% 30%
Tobola 30 40% 57% 3%
Household head Gender
Female 139 20% 52% 28% 0,0590
Male 281 31% 48% 22%
Household type
Adult Female No Adult 
Male (FNM)
64 13% 56% 31% 0,0420
Adult Male & Adult 
Female (F&M)
332 30% 47% 23%
Adult Male No Adult 
Female (MNF)
24 21% 58% 21%
Status of the household 
in the current location
Internally Displaced 
Persons (IDP)
61 11% 70% 18% < 0,0001
Resident 309 32% 45% 23%
Returnee 50 14% 50% 36%
Aggregate 420 27% 49% 24%
We note that 21% of households in the Mambasa HZ and 26% of households in the 
Mandima HZ are severely hungry. It is therefore around 58,765 inhabitants in the 2 Health 
Zones surveyed.
2.3.5 Household Dietary Diversity Score (HDDS) 
2.3.5.1 Meaning and measure 
The Diet Diversity Score measures how many food groups (out of 8) are consumed during 
a seven-day reporting period. Households that within seven-day period consumed foods
from four or fewer food groups out of eight are classified as having low dietary diversity.
Dietary diversity scores are calculated by counting the number of food groups consumed 
in the household during a 7-day reference period. For this study, we have considered 8 
food groups, namely: (1). Main staples (cereals and tubers); (2) Vegetables; (3) Fruits; (4) 
Proteins (meats, fish); (5) Legumes; (6) Dairy products; (7) Oil and fats; (8) Sugar.
26 
The sum obtained as DDS is classified into 3 groups: 1. Weak DDS: <4; 2. Medium DDS: 
5-6; 3. Acceptable DSS: >6.
2.3.5.2 Discussion of the results of the HDDS 
Overall, the proportion of households with a low or average Dietary Diversity Score 
represents 82%. Household diets are not generally diversified. Households mainly 
consume basic foods (cereals and tubers), vegetables and oil. The mean dietary diversity 
score is 5.09 (95% CI: 4.95-5.22).
As can be seen in the table 13, the low DDS is more observed in Adult Female No Adult 
Male households (52%), in internally displaced households (54%), returned households 
(48%) and logically those with poor FCS (83%). The DDS is not significantly different 
between the two health zones surveyed.
Table 13. HDDS distribution and comparison 
Categories n Weak 
DDS
Medium 
DDS
Acceptable 
DDS
P-value
Health zone
Mambasa 210 34% 46% 20% 0,1750
Mandima 210 41% 45% 14%
Household head Gender
Female 139 45% 39% 16% 0,0570
Male 281 33% 49% 18%
Household type
Adult Female No Adult Male (FNM) 64 52% 42% 6% < 0,0001
Adult Male & Adult Female (F&M) 332 37% 44% 19%
Adult Male No Adult Female (MNF) 24 4% 79% 17%
Status of the household in the 
current location
Internally Displaced Persons (IDP) 61 54% 41% 5% < 0,0001
Resident 309 32% 46% 22%
Returnee 50 48% 50% 2%
FCS
FCS Acceptable 130 2% 55% 43% < 0,0001
FCS borderline 214 43% 50% 7%
FCS Poor 76 83% 17% 0%
Aggregate 420 37% 45% 17%
At the Health Zone level, the diet is almost identical and dominated by the consumption 
of vegetables (6 days per week), staple foods: cereals and tubers (5 days per week) and 
oil (6 days per week).
27 
Households with a "Poor" Food Consumption Score consume only leaves, staple foods 
and oil. The very low level of consumption of basic foods (cereals and tubers) among 
households with a poor FCS deserves attention (2 days per week on average). The 
consumption of animal proteins and sugar is almost zero. Consumption of different food 
groups is not significantly different between households depending on whether or not it is 
headed by a woman, or between IDPs, returnees and Residents. 
Adult Male No Adult Female Households consume less vegetables and green leaves than 
the other two types of household considered according to the gender of its members (See 
appendix 3.6. for details on the average number of days of consumption of each type of 
food by category). 
4.8
4.8
5.0
4.7
4.7
4.9
4.0
6.2
4.9
2.2
5.1
4.8
4.7
4.8
5.8
5.8
5.7
5.9
6.0
5.8
5.0
5.6
5.9
6.0
5.7
5.8
5.6
5.8
2.1
1.6
1.6
1.9
1.4
1.9
2.6
3.0
1.6
0.5
0.9
2.1
1.5
1.8
1.2
1.2
1.1
1.2
0.8
1.3
1.3
2.6
0.7
0.1
0.6
1.3
1.3
1.2
1.8
1.9
1.6
2.0
1.5
1.9
2.2
3.5
1.3
0.7
1.7
2.0
1.1
1.9
6.3
6.2
6.2
6.3
6.1
6.4
5.5
6.5
6.4
5.5
6.2
6.3
6.2
6.3
1.2
0.8
1.1
1.0
0.5
1.1
1.5
1.9
0.7
0.2
0.6
1.2
0.3
1.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
HZ Mambasa
HZ Mandima
Household Head Female
Household Head Male
Adult Female No Adult Male (FNM)
Adult Male & Adult Female (F&M)
Adult Male No Adult Female (MNF)
FCS Acceptable
FCS Borderline
FCS Poor
Internally displaced persons (IDP)
Resident
Returnee
Aggregate
Average number of consumption days
 Main Staples (Maize, Cassava) Vegetables Fruits/fruit juices
Meat, Fish, Poultry Pulses, legumes and nuts Milk and milk products
Oils and fats Sweets (Sugar/honey)
28 
Figure 12. Cumulative average number of consumption of 8 different food groups according to 
different categories of households
Source of food groups consumed in households
• The main sources of basic food products (cereals and tubers) consumed in the household 
are household produce and cash purchases at the market. The same is true of vegetables 
and fruits. 
• For all other food groups (meats, fish, poultry, legumes, milk and dairy products, oil, fat, 
sugar and other sweets), the main source is cash purchase at the market.
• One of the main sources of fruit is picking. 
29 
Table 14. Source of product groups consumed in households
Source of food Cereals and 
tubers Vegetables Fruits Meat, Fish, 
Poultry Legumes Milk and milk 
products Oils and fats Sugar, honey 
and sweets 
n % n % n % n % n % n % n % n %
Household 
produced 193 48% 251 60% 82 38% 14 6% 95 33% 3 8% 114 28% 18 13%
Purchased in 
cash 144 35% 107 26% 40 19% 170 77% 143 49% 30 83% 228 55% 91 67%
Bought on 
credit 17 4% 7 2% 2 1% 18 8% 17 6% 1 3% 15 4% 9 7%
Receipt as 
payment / 
Work for food
10 2% 7 2% 1 0% 2 1% 4 1% 0 0% 11 3% 0 0%
Hunting / 
gathering / 
fishing
13 3% 26 6% 82 38% 11 5% 5 2% 0 0% 8 2% 6 4%
Food aid 
(State, NGO) 0 0% 0 0% 0 0% 0 0% 1 0% 0 0% 0 0% 0 0%
Loan / gifts, 
Donations 
(friends / 
relatives)
24 6% 19 5% 7 3% 5 2% 20 7% 1 3% 29 7% 10 7%
Begging 2 0% 2 0% 0 0% 0 0% 3 1% 1 3% 2 0% 1 1%
Exchange / 
Barter 3 1% 0 0% 0 0% 1 0% 4 1% 0 0% 4 1% 0 0%
Total 406 100
% 419 100
% 215 100% 221 100% 292 100
% 36 100
% 411 100
% 135 100%
30 
Crossing food security indicators
As we can observe in table 15, 16 and 17, there is a logical gradation of the different 
food security indicators. We crossed the FCS levels with the different levels of the three 
food security indicators, namely the HHS, the rCSI and the DDS to assess the consistency 
of the results obtained.
• Household Hunger Scale (HHS) by FCS
The study shows that households with poor FCS are more affected by severe hunger 
(57%). Households that have an acceptable FCS logically have less food deprivation 
(60%). The proportion of households characterized by moderate hunger is higher (62%) 
for those with borderline FCS. In their previous study, Jerving et al. (2019)42 already 
showed that the large part of the population (67%) of Ituri already had a high level of food 
insecurity long time before the arrival of EVD. The same proportion was 26% for the 
population of North Kivu located in the areas affected by EVD.
Table 15. Crossing FCS and HHS
FCS N HHS: Severe hunger HHS: Moderate 
hunger
HHS: None or light 
hunger
FCS Acceptable 130 5% 35% 60%
FCS Borderline 214 23% 62% 14%
FCS Poor 76 57% 37% 7%
Aggregate 420 24% 49% 27%
• Reduced Coping Strategy Index (rCSI) by FCS 
As can be seen in table 16, households with a poor FCS are forced to resort to the most 
extreme coping strategies illustrated by a high rCSI. In fact, we find that 96% of 
households with poor FCS use high adaptation strategies against 39% of those with an 
acceptable FCS.
Table 16. Crossing FCS and rCSI
FCS N CSI: High Coping CSI: Medium CSI: No or low Coping
FCS Acceptable 130 39% 35% 25%
FCS Borderline 214 80% 15% 5%
FCS Poor 76 96% 3% 1%
Aggregate 420 70% 19% 10%
42 Jerving, Sara. "Q&A: How the Ebola Declaration Could Threaten Food Security in DRC." Devex. July 18, 2019. 
Accessed 26 July 
31 
• Household Dietary Diversity Score (HDDS)
Table 17 shows that a higher proportion (83%) of households with a poor FCS have low 
SDS (composed mainly of cereals, tubers and green vegetables) compared to those with 
an acceptable FCS. The majority (43%) of the latter (borderline DDS) have a much more 
varied diet consisting of 8 different types of food.
Table 17. Crossing FCS and HDDS
FCS N DDS: Weak DDS : Medium DDS: Acceptable
FCS Acceptable 130 2% 55% 43%
FCS Borderline 214 43% 50% 7%
FCS Poor 76 83% 17% 0%
Aggregate 420 37% 45% 17%
32 
Comparison of FCS from the base study conducted in Ituri to that of North-Kivu 
Having used the same methodology, we compare in the figure 13 the FCS in the different health zones of the FABELU 
project in North Kivu with those concerned by this study. We can see that the two HZ of Mambasa and Mandima have 
accumulations of poor and limited FCS of 69%. The combination of poor and borderline FCS greater than 60% is observed 
in the Kalunguta (75%), Kayna (65%) and Alimbongo (63%) HZ. 
Figure 13. Comparison of FCSs from Health Zones of Project Ituri and North Kivu
Determination of summary indicators for the FABELU project (See Appendix 3.5)
 Food Consumption Score (FCS): For all the Health Zones visited in the study of the FABELU project (in North Kivu 
and Ituri), households with a Poor FCS represent 16%, those who have a Borderline FCS represent 46% and finally
those who have an Acceptable FCS are 38%.
 Reduced Coping Strategy Index (rCSI):, we observe 70% of households with high coping, 21% with medium and 9% 
who have low coping. 
 Household Hunger: The summary of household hunger indicators shows that 19% are severely hungry, 51% are 
moderately hungry and 30% have no or low hunger.
16% 20% 28%
16% 12% 8% 15% 14% 17%
53% 49% 35%
31%
63%
57% 45%
33%
42%
31% 31% 37%
53%
25%
36% 40%
53%
42%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa Mandima Alimbongo Beni Kalunguta Kayina Lubero Mutwanga Oicha
Ituri North Kivu
PERCENTAGE (%) OF HH
FCS Poor FCS Borderline FCS Acceptable
33 
2.4 ACCESS TO PUBLIC INFRASTRUCTURE AND MARKET STUDY 
2.4.1 Evolution of the price of basic necessities in Mambasa
The data presented in appendix 3.4 are means of monthly prices collected in Mambasa 
markets. Prices are collected between 11 a.m. and 2 p.m. For each product, 5 samples 
are weighed.
The Price Index presented in the figure 14 is calculated on the 14 products. We observe 
an increase in prices in the month of August and November 2019 for several products, 
followed by a stagnation of January to April.
Figure 14. Evolution of price index of basics products (base 100 in January 2019)
100.00100.00100.00100.00
101.43
104.17
100.00
111.73
105.12
101.79
110.79
105.95
101.19
104.11
101.43100.84101.40
94.00
96.00
98.00
100.00
102.00
104.00
106.00
108.00
110.00
112.00
114.00
J-19 F-19 M-19 A-19 M-19 JN-19 JL-19 A-19 S-19 O-19 N-19 D-19 J-20 F-20 M-20 A-20 M-20
Price Index
Month
34 
Table 18. Price index for 14 essential products in Mambasa territory from January 2019 to May 2020 (Price in Congolese 
Franc)
Items J-19 F-19 M-19 A-19 M-19 JN￾19 JL-19 A-19 S-19 O-19 N-19 D-19 J-20 F-20 M-20 A-20 M-20
1. Cassava flour (kg) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 125,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0
2. Peeled dry corn (kg) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 125,0 100,0 100,0 100,0 100,0 100,0 120,0 100,0 100,0
3. Cassava leaves 
(Sombe) (bunch) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 200,0 100,0 100,0 100,0 100,0 100,0 100,0
4. Sweet banana (diet) 100 100,0 100,0 100,0 100,0 100,0 100,0 150,0 100,0 100,0 100,0 100,0 116,7 100,0 100,0 100,0 100,0
5. Natural Fruit Juice 
(bottle of 33cl) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 140,0 100,0 100,0 100,0 100,0 100,0 100,0
6. Boneless beef meat 
(kg) 100 100,0 100,0 100,0 100,0 100,0 100,0 114,3 100,0 100,0 100,0 100,0 100,0 112,5 100,0 100,0 100,0
7. Fresh Tilapia fish (kg) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 111,1 100,0 100,0 100,0 100,0 100,0 100,0
8. Local live chicken 
(piece) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 113,3 100,0 100,0 100,0 100,0 100,0 100,0 111,8 100,0
9. Multicolored beans 
(kg) 100 100,0 100,0 100,0 100,0 125,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 120,0 100,0 100,0 100,0
10. Fresh tomatoes (kg) 100 100,0 100,0 100,0 120,0 100,0 100,0 100,0 100,0 100,0 100,0 133,3 100,0 100,0 100,0 100,0 100,0
11. Coconut (piece) 100 100,0 100,0 100,0 100,0 100,0 100,0 200,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0
12. Fresh milk (liter) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 150,0 100,0 100,0 100,0 100,0 100,0
13. Palm oils (bottle of 
72cl) 100 100,0 100,0 100,0 100,0 133,3 100,0 100,0 100,0 100,0 100,0 100,0 100,0 125,0 100,0 100,0 100,0
14. Sugar (kg) 100 100,0 100,0 100,0 100,0 100,0 100,0 100,0 133,3 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0
Mean Index 100,0 100,0 100,0 100,0 101,4 104,2 100,0 111,7 105,1 101,8 110,8 106,0 101,2 104,1 101,4 100,8 100,0
Source: Calculated from the table in Appendix 3.
35 
2.4.2 Period of the year when households do not have enough food
The results grouped in Table 19 show that in all of the two Health Zones concerned by this study, a significant proportion of 
households declared that they did not have enough food in the month of February to April. The situation is much more 
difficult for households in March and April
Table 19. Proportion of households by month during which households do not have enough food to support families
Health areas n JN-19 JL-19 A-19 S-19 O-19 N-19 D-19 J-20 F-20 M-20 A-20 M-20
Biakako Mayi 23 13% 9% 13% 17% 26% 26% 26% 30% 52% 61% 52% 22%
Biakato Mine 21 10% 10% 14% 10% 24% 24% 38% 33% 48% 76% 62% 29%
Katanga 20 0% 5% 5% 15% 20% 40% 40% 30% 45% 60% 35% 15%
Lukaya 23 22% 26% 22% 26% 26% 39% 35% 30% 83% 74% 65% 57%
Makeke 21 5% 10% 10% 19% 24% 24% 29% 43% 52% 48% 67% 57%
Mayuwano 27 44% 37% 37% 33% 44% 52% 56% 44% 59% 63% 70% 67%
Some 26 50% 50% 54% 54% 46% 54% 54% 62% 58% 65% 77% 69%
HZ Mandima 161 22% 22% 24% 26% 31% 38% 40% 40% 57% 64% 62% 47%
Banana 27 41% 37% 52% 44% 56% 63% 59% 48% 48% 41% 41% 41%
Binase 18 6% 6% 11% 22% 11% 17% 33% 33% 50% 39% 50% 50%
Bukulani 25 24% 28% 28% 24% 20% 20% 36% 48% 64% 60% 72% 48%
Makoko2 22 14% 14% 27% 23% 18% 18% 23% 23% 18% 45% 55% 55%
Mambasa 21 5% 0% 10% 10% 5% 10% 10% 33% 48% 48% 48% 10%
Salama 26 23% 46% 27% 54% 38% 46% 50% 58% 46% 38% 31% 15%
Tobola 14 14% 7% 14% 29% 43% 50% 36% 29% 21% 29% 29% 0%
HZ Mambasa 153 20% 22% 26% 31% 28% 33% 37% 41% 44% 44% 47% 33%
Aggregate 314 21% 22% 25% 28% 30% 35% 39% 40% 51% 54% 55% 40%
36 
2.4.3 Method of obtaining food
Looking at Figure 15, it is clear that the majority of the population obtains its provisions 
from the market (83%) and 68% rely on the production of fields and gardens. Only 8% 
obtain food by other methods (donation from friends or family member, food as 
remuneration for work).
.
Figure 15. Methods of obtaining food used by households
2.4.4 Frequency of market attendance
Majority of households go to the market weekly (51%), monthly (29%) and daily (20%). 
This disparity in terms of market attendance is largely due to the distance to reach the 
market, the cost of transport and the condition of the road network.
 
78%
89%
83%
69% 67% 68%
8% 9% 8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
PERCENTAGE (%) OF HH
Buy from Market Garden/farm Other
37 
Figure 16. Frequency of market attendance
2.4.5 Means used for transportation 
Table 20 shows that the majority (86%) walk to the market. Only 12% use private 
transportation (bicycle, motorcycle, car, etc.) and 10% use public transportation. The 
proportion of people walking is high in the Mandima area (98%) compared to 75% in 
Mambasa.
Table 20. Means of transport used to get to the market
Items Mambasa 
(n=210)
Mandima 
(n=210)
Aggregate (420)
Frequency % Frequency % Frequency %
Walking 157 75% 206 98% 363 86%
Bike, motorcycle, car (private 
transport) 40 19% 11 5% 51 12%
Bike, motorcycle, car (public or 
public transport) 41 20% 2 1% 43 10%
2.4.6 Public transportation cost 
The table 21 shows that the average cost of public transport to get to the market is US$ 
3.78 in the Mambasa health zone, and US$ 1.71 in the Mandima health zone. A typical 
person pays US$ 3.16 in the health zone of Mambasa and US$ 1.71 in that of Mandima.
42%
60%
51%
21% 19% 20%
36%
21%
29%
0%
10%
20%
30%
40%
50%
60%
70%
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
PERCENTAGE (%) OF HH
Axis Title
Weekly Daily Monthly
38 
Table 21 Transportation cost to go to market
Health Zone Mean cost in
$
SD Median cost
in $
Min in $ Max in $
Mambasa 3,78 2,16 3,16 0,26 7,89
Mandima 1,71 1,3 1,71 0,79 2,63
Aggregate 3,68 2,16 3,16 0,26 7,89
2.4.7 Assessment of the market access distance 
From figure 17, 50% of study participants believe that the distance to reach the nearest 
market is long or very long. Only 25% consider it average and 25% consider this distance 
short or very short.
Figure 17. Market access distance appreciation 
2.4.8 Market access barriers 
Access to market in Mambasa and Mandima territories is difficult. 58% of respondents say 
they have difficulty reaching the market. These difficulties are more accentuated in
Mambasa HZ (66%) compared to that of Mandima HZ (50%). 
6%
18% 12%
11%
15%
13%
20%
30%
25%
21%
26%
24%
41%
11%
26%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa (n=210) Mandima(n=210) Aggregate ( n= 420)
PERCENTAGE (%) OF RESPONDENTS
Axis Title
Very short Not Long Average Long Very long
39 
Figure 18. Distribution of respondents who find it difficult to get to the market
2.4.9 Access to market indicators 
2.4.9.1 Time to get to the market 
The average time to reach the nearest market is 80 minutes in Mambasa health zone and 
36 minutes in the Mandima. Typically, an individual travels 45 minutes in Mambasa HZ
and 30 minutes in the Mandima HZ. 
Table 22.Time to reach the market 
Health Zone Average time in 
minutes
SD Median time in 
minutes
Mambasa 80 88 45
Mandima 36 30 30
Aggregate 58 70 30
2.4.9.2 Difficulties in accessing the market 
The most important challenge respondents face in access to the market relates to the 
distance to be covered (43%). Then comes the difficulty of having the money to reach the 
market (42%). There is also the difficulty of the poor road network (26%). These main 
difficulties are linked: indeed, a poor road network would make long distance and imply a 
significant average cost for the household. According to the respondents met in the two 
health zones, the most important routes are:
1. Malutu-Mambasa on the Mangina-Mambasa axis and
2. Mambasa-Nduye-Dingbo.
66%
50%
58%
0%
10%
20%
30%
40%
50%
60%
70%
MAMBASA MANDIMA Aggregate
PERCENTAGE (%) OF RESPONDENTS
40 
Figure 19. Types of difficulty encountered by locals to get to the market
2.4.9.3 Access to market information 
The figure 20 show that 88% of respondents get information from friends or family 
members for any market information. A small proportion use the radio for all market 
information.
Figure 20. Access to market information 
The other means of access to the market information cited by the inhabitants met are 
mainly received during the visit to the market, or by occasional sources.
39%
54%
50%
2% 3%
13%
32% 34%
4%
0%
26%
43% 42%
3% 2%
0%
10%
20%
30%
40%
50%
60%
Poor Road
Network
Distance Money Insecurity Other
PERCENTAGE (%) OF 
RESPONDENTS
MAMBASA MANDIMA Aggregate
87%
13%
0% 1%
10%
88%
14%
0% 1%
14%
88%
13%
0% 1%
12%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Friends & Family Radio Television Community
information board
Other
PERCENTAGE (%) OF RESPONDENTS
MAMBASA MANDIMA Aggregate
41 
2.5 COMMUNITY PERCEPTIONS ON EBOLA 
2.5.1 Knowledge of Ebola virus disease by health zone and gender of 
respondents
When asked if the respondent has ever heard of Ebola Virus Disease (EVD), the results 
grouped in figure 21 show that 95% of respondents know or have heard of Ebola virus
disease. This corresponds, respectively, to 97% and 93% in Mambasa and Mandima 
health zones. Compared to the results of Fauche et al. (2015)43 in their study conducted 
in the city of Kinshasa where only 2% of respondents were aware of EVD, the population 
of Mambasa in Ituri is better informed of the existence of EVD than they were 5 years 
ago. 
Figure 21. Proportion of respondents who have ever heard of Ebola Virus Disease
2.5.2 Level of knowledge of the causes, signs and treatment of Ebola Virus 
Disease
We find that the community has knowledge about the causes of Ebola virus disease (64%) 
and 79% of people can identify the signs. However, the treatment method remains ignored 
by the large part of the population (only 7% are aware) regardless of the Health Zone.
43 Fauche, Adelin & Mukebayi, Kadimanche & Bakenje, Marlin & Pilipili, Koyange & Bongo, G.N. & Konga, Museu & 
Songo, Baukaka & Ngombe, Nadège & Mbemba, Théophile & Ngbolua, Koto-Te-Nyiwa. (2015). Knowledge, Attitude 
and Perception of the Population Related To Ebola Virus Disease in Kinshasa City, Democratic Republic Of The 
Congo. Journal Of Advancement In Medical And Life Sciences. 
https://www.researchgate.net/publication/279853040_Knowledge_Attitude_and_Perception_of_the_Population_Rel
ated_To_Ebola_Virus_Disease_in_Kinshasa_City_Democratic_Republic_Of_The_Congo
97% 93% 97% 93% 95%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mambasa Mandima Female Male
Health Zone Respondent's gender Aggregate
PERCENTAGE (%) OF RESPONDENTS
42 
Figure 22. Proportion of respondents who know the causes, signs and treatment of Ebola Virus 
Disease 
2.5.3 Identifying the causes of EVD 
Among the causes of EVD, the best known by the community are unprotected contact 
with the blood, body fluids or tissues of an infected person with symptoms of EVD (52%), 
direct contact (44%). 
Figure 23. Knowledge of EVD causes
67%
80%
8%
61%
79%
6%
64%
79%
7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Are you aware of the
causes of Ebola? : Yes
Are you aware of some of
the signs/symptoms of
people who have Ebola
Sickness? : Yes
Are you aware of the
treatment of the Ebola
PERCENTAGE (%) OF RESPONDENTS
Sickness? : Yes
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
3%
35%
36%
39%
41%
58%
1%
34%
35%
36%
46%
47%
2%
35%
35%
37%
44%
52%
0% 10% 20% 30% 40% 50% 60% 70%
Others
Unprotected sexual contact with a person
recovering from EVD up to 12 months after…
Contact unprotected with contaminated
surfaces, materials (such as bedding) or…
Contact with contaminated objects.
Direct contact (e.g. handling or consumption)
with infected animals (live or dead) or their…
Unprotected contact with blood, body fluids or
tissue from an infected person with symptoms…
PERCENTAGE (%) OF RESPONDENTS
Aggregate (n= 420) MANDIMA (n=210) MAMBASA (n=210)
43 
2.5.4 EVD considerations in community 
The study shows that 90% of respondents say that EVD is a dangerous disease, 64% 
think they are vulnerable to EVD and almost 45% know the type of people who can 
contract EVD. These considerations on EVD are similar between the two Health Zones.
Figure 24. EVD Considerations in community 
2.5.5 Regularity in the listening of EVD signs 
As it can be seen in the figure 25, 42% of respondents say that they are often informed 
about the signs of EVD, 43% are rarely informed. We note that the proportion of those 
who never heard of the signs of EVD remains significant (10%). To this proportion must 
also be added those who do not feel concerned.
50%
65%
90%
40%
62%
90%
45%
64%
90%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Do you know the type of
people that can get Ebola? :
Yes
Do you think you are
vulnerable? : Yes
Do you consider Covid-19 a
dangerous illness/sickness? :
Yes
PERCENTAGE (%) OF RESPONDENTS
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
44 
Figure 25. Regularity of listening to signs of EVD 
2.5.6 Knowledge of Signs and Symptoms of Ebola
Among the symptoms of EVD, the community spontaneously cites fever (65%), vomiting 
(64%), hemorrhage (64%), diarrhea (61%) and headache with (56%). It is in Mandima HZ 
that a larger proportion (70%) cite hemorrhage compared to 58% recorded in Mambasa 
HZ. The other signs of EVD are cited in similar proportions in the two Health Zones.
Figure 26. Proportion of respondents based on known symptoms of EVD
3% 7% 5%
13% 7% 10%
42% 44% 43%
41% 42% 42%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
PERCENTAGE (%) OF RESPONDENTS
Non affected Never Rarely (6months or more) Often (1 month – 3months)
66% 63% 63%
58% 59%
9%
64% 64%
60%
70%
53%
2%
65% 64% 61% 64%
56%
6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Fever Vomiting Diarrhea Hemorrhage Headache Others
PERCENTAGE (%) OF 
RESPONDENTS
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
45 
2.5.7 Prevention of EVD 
In terms of prevention, we find that the exercise of good hygiene practices is the best 
known practice by the community (75%), followed by the practice of avoiding unprotected 
direct contact (49%), the proportion remains weak for other measures which seem to be 
overlooked by a large proportion of the community.
Table 23. Known ways of preventing EVD according to health zones
Known means of 
prevention
Mambasa (n=210) Mandima (n=210) Aggregate (420)
Frequency % Frequency % Frequency %
Practice good hygiene 
practices 157 75% 156 74% 313 75%
Avoid unprotected direct 
contact 104 50% 100 48% 204 49%
Follow safe funeral 
practices 74 35% 88 42% 162 39%
Avoid coming into contact 
with wild animals 69 33% 81 39% 150 36%
Vaccination 61 29% 62 30% 123 29%
Avoid high risk areas and 
activities 54 26% 63 30% 117 28%
Avoid unprotected sexual 
activity 59 28% 52 25% 111 26%
Other means of prevention 7 3% 2 1% 9 2%
2.5.8 Assessment of access to Health Structure in the community
According to figure 27, 90% of the households visited declare that there is a health facility 
(FOSA) close to their living environment. We find that 83% of respondents say that their 
household has easy access to a local health facility. The proportions of households with 
easy access to the health structure of their environment are not significantly different 
between the two health zones.
46 
Figure 27. Existence and ease of access to health facilities in the area
2.5.9 Assessment of the distance to be walked to reach a health facility
The study reveals that the average distance to reach a health facility is 1.5 km in Mambasa 
health zone and 0.6 km in Mandima. Given the large dispersion, we consider the median 
distances. Thus, a typical person travels 1 km in Mambasa HZ and 0.5 km in Mandima 
HZ to reach a health facility.
Table 24. Average and median distances to reach a health facility 
 
Health Zone Average distance in km SD Median distance in
km 
Mambasa 1,5 2,1 1
Mandima 0,6 0,5 0,5
Aggregate 1,1 1,6 0,5
2.5.10 Perception of people with Ebola in the community
From Table 25, it appears that 72% of respondents say they are not ready to make contact 
with a person with EVD. This percentage is reduced when moving from a person with a 
person who has been cured of EVD. We go from 72% to 24%. We also observe that the 
attitude "no contact" with Ebola patients considered by 67% of respondents goes to 18% 
for "no contact" with people who are cured of Ebola.
84%
86%
96%
80%
90%
83%
70%
75%
80%
85%
90%
95%
100%
Existance of medical facility located near Easy access to health facility
PERCENTAGE (%) OF RESPONDENTS
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
47 
Table 25. Community perception of Ebola patients
Mambasa (n=210) Mandima (n=210) Ensemble (420)
Frequency % Frequency % Frequency %
How do you see/relate to people who have Ebola sickness?
Not affected 7 3% 14 7% 21 5%
No contact 146 70% 158 75% 304 72%
With distrust / 
discrimination 25 12% 21 10% 46 11%
Good relations 15 7% 12 6% 27 6%
Other 17 8% 5 2% 22 5%
How does the community see and relate to people who have Ebola? 
Not affected 7 3% 14 7% 21 5%
No contact 140 67% 143 68% 283 67%
With distrust / 
discrimination 32 15% 39 19% 71 17%
Good relations 13 6% 8 4% 21 5%
Very good relations 1 0% 0 0% 1 0%
Other 17 8% 6 3% 23 5%
How do you see/relate to people who have recovered from Ebola?
Not affected 7 3% 14 7% 21 5%
No contact 48 23% 51 24% 99 24%
With distrust / 
discrimination 36 17% 41 20% 77 18%
Good relations 89 42% 69 33% 158 38%
Very good relations 12 6% 27 13% 39 9%
Other 18 9% 8 4% 26 6%
How does the community see and relate to people who have recovered from Ebola?
Not affected 7 3% 14 7% 21 5%
No contact 38 18% 39 19% 77 18%
With distrust / 
discrimination 31 15% 43 20% 74 18%
Good relations 108 51% 90 43% 198 47%
Very good relations 10 5% 16 8% 26 6%
Other 16 8% 8 4% 24 6%
2.5.11 Effects of Ebola on Economic activities
We observe that 42% of people questioned say that EVD has indeed affected their work 
and 39% declare that EVD has had an effect on agriculture and food security in their 
community. 
48 
Table 26. Effects of EVD on economic activities
MAMBASA 
(n=210)
MANDIMA 
(n=210)
Aggregate (n= 420)
Frequency % Frequency % Frequency % 
Say Ebola has affected the work 
of people in the community 79 38% 98 47% 177 42%
Declare that Ebola has affected 
agriculture and food security in 
your community
71 34% 94 45% 165 39%
2.6 COMMUNITY PERCEPTION OF COVID-19
2.6.1 Knowledge of COVID-19 by health zone and gender of respondents
According to the results shown in the graph below (figure 28), we note that 8 out of 10 
respondents know or have already heard of COVID-19 (81%). This proportion is 75% in 
Mandima HZ and 88% in that of Mambasa. The study shows that the proportion of people 
who have heard or know about EVD do not differ significantly by gender of the respondent.
There has been an improvement in public awareness compared to the results of the 
survey carried out by Target SARL44. 
Figure 28. Proportion of respondents who have heard of COVID-19
44 Target SARL, 2020. Les congolais et la pandémie de COVID-19. https://zoom-eco.net/a-la-une/rdc-39-de￾congolais-conscients-de-la-gravite-de-COVID-19-etude-de-target-sarl/
88%
75%
82% 81% 81%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MAMBASA MANDIMA Female Male
Health Zone Gender Aggregate
PERCENTAGE (%) OF RESPONDENTS
49 
2.6.2 Level of knowledge of the causes, signs and treatment of COVID-19
From figure 29, we note that there is still a deficit in terms of knowledge of the causes of 
the COVID-19 virus with a rate of 9%, while in terms of knowledge of the signs the rate is 
29%. Knowledge of the treatment method remains ignored by the community.
Figure 29. Proportion of respondents who know the causes, signs and treatment of 
COVID-19
2.6.3 COVID-19 considerations 
We find that 7 out of 10 respondents declare that COVID-19 is a dangerous disease 
(73%). In addition, 4 out of 10 people recognize that they economically vulnerable because 
of the COVID-19 pandemic (42%) and 2 out of 10 people say they know the type of people 
who could contract COVID-19 disease (20%).
14%
36%
2% 4%
21%
0%
9%
29%
1%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Aware of the causes of Covid￾19
Aware of some of the
signs/symptoms
Aware of the treatment of the
Covid-19 Sickness
PERCENTAGE (%) OF 
RESPONDENTS
MAMBASA (n= 210) MANDIMA (n= 210) Aggregate (n= 420)
50 
Figure 30. Proportion of respondents according to different COVID-19 considerations
2.6.4 Knowledge of Signs and Symptoms of COVID-19
The study shows that overall, less than ¼ of those interviewed are aware of the signs or 
symptoms of COVID-19. However, we note that the residents of Mambasa HZ have 
relatively more information on the COVID-19 pandemic than those of Mandima HZ.
Figure 31. COVID-19 symptoms knowledge distribution 
27%
47%
82%
12%
37%
63%
20%
42%
73%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Do you know the type of
people that can get Covid-19? :
Yes
Do you think you are
vulnerable? : Yes
Do you consider Covid-19 a
dangerous illness/sickness? :
Yes
PERCENTAGE (%) OF RESPONDENTS
MAMBASA (n= 210) MANDIMA (n= 210) Aggregate (n= 420)
25% 27%
22%
8% 6%
20%
10%
12%
2%
14% 15%
11%
6% 5%
14%
8%
11%
0%
19% 21%
17%
7% 6%
17%
9%
12%
1%
0%
5%
10%
15%
20%
25%
30%
PERCENTAGE (%) OF 
RESPONDENTS
MAMBASA (n=210) MANDIMA (n=210) Aggregate (n= 420)
51 
2.6.5 Prevention of COVID-19
With regard to the prevention of COVID-19, it should be said that the practice of good 
hygiene practices remains the best known by the community although known by less than 
half of the population (40%). In addition to this measure, almost 3 out of 10 people believe 
that unprotected direct contact should be avoided (28%). Details by HZ are shown in the 
table below.
 
Table 27. Means of prevention of COVID-19 known by the population
Known means of prevention Mambasa (n=210) Mandima 
(n=210)
Aggregate 
(n=420)
Frequency % Frequency % Frequency %
Practice good hygiene practices 97 46% 72 34% 169 40%
Avoid unprotected direct contact 68 32% 48 23% 116 28%
Avoid high risk areas and activities 40 19% 20 10% 60 14%
Follow safe funeral practices 24 11% 15 7% 39 9%
Avoid unprotected sexual activity 18 9% 12 6% 30 7%
Avoid coming into contact with wild 
animals
12 6% 9 4% 21 5%
Vaccination 7 3% 6 3% 13 3%
2.6.6 Perception of people with COVID-19 in the community
As for people reached contact will not exist for 62% while this proportion will increase to 
49% for people recovered from COVID-19. This implies that, compared to EVD, the 
community will avoid contact more with someone who has been healed or released from 
COVID-19 than from Ebola (46% for COVID-19 versus 24% for EVD). 
Table 28. Community perception of COVID-19 patients
Mambasa (n=210) Mandima (n=210) Aggregate (n= 420)
Frequency % Frequency % Frequency %
How do you see/relate to people who have COVID-19 sickness?
Not affected 26 12% 52 25% 78 19%
No contact 129 61% 130 62% 259 62%
With distrust / 
discrimination
9 4% 2 1% 11 3%
Good relations 2 1% 0 0% 2 0%
Other 44 21% 26 12% 70 17%
How does the community see and relate to people who have COVID-19
Not affected 26 12% 52 25% 78 19%
No contact 128 61% 128 61% 256 61%
With distrust / 
discrimination
11 5% 4 2% 15 4%
Good relations 3 1% 0 0% 3 1%
Other 42 20% 26 12% 68 16%
How do you see/relate to people who have recovered from COVID-19?
52 
Not affected 26 12% 52 25% 78 19%
No contact 89 42% 106 50% 195 46%
With distrust / 
discrimination
26 12% 19 9% 45 11%
Good relations 21 10% 3 1% 24 6%
Very good relations 4 2% 2 1% 6 1%
Other 44 21% 28 13% 72 17%
How does the community see and relate to people who have recovered from COVID-19?
Not affected 26 12% 52 25% 78 19%
No contact 83 40% 91 43% 174 41%
With distrust / 
discrimination
24 11% 15 7% 39 9%
Good relations 30 14% 21 10% 51 12%
Very good relations 5 2% 2 1% 7 2%
Other 42 20% 29 14% 71 17%
2.6.7 Effects of COVID-19 on Economic activities
 
It emerges from the table 29 that COVID-19 has impacted work in the community 
according to the declaration of 55% of the inhabitants in Mambasa HZ and 35% in 
Mandima HZ.
Regarding its effect on agricultural activities and food security, it should be noted that 
almost 3 out of 10 people (27%) affirm that COVID-19 had an effect on agriculture and 
food security in Mambasa territory.
Table 29. Effects of COVID-19 on economic activities according to the inhabitants 
interviewed
Mambasa (n=210) Mandima (n=210) Aggregate (n =420)
Economic effects Frequency % Frequency % Frequency %
Do you think COVID-19
has affected the work of 
people in the community
116 55% 74 35% 190 45%
Has COVID-19 affected 
agriculture and food 
security in your 
community?
65 31% 47 22% 112 27%
53 
III. LESSONS LEARNED 
The main lessons learned from this baseline study are:
- The insecurity experienced by the inhabitants makes them feel abandoned and 
leads to a kind of mistrust towards humanitarian workers and even towards 
government services (organizations). Action on concrete activities with immediate 
impact, particularly the rehabilitation of agricultural service roads, national road 
networks and other basic infrastructure, could change the perceptions of the 
population and help the administrative and political authorities to improve public 
policy in favor of population.
- The population is informed of the Ebola virus epidemic and currently of the COVID￾19 pandemic (COVID-19). 
54 
IV. CONCLUSION AND MAIN 
RECOMMENDATIONS 
The present descriptive and cross-sectional study shows the following results:
The main targeted population for this study, 73.6% are residents, 14.5% of internally 
displaced persons and 11.9% of returnees has agriculture as the main source of income 
(63.6%) and 66% of them live with less than $ 1 per person per day (below the poverty 
threshold).
To meet household needs, 89% of households resort to debt. In the case of difficulties of 
obtaining food, 7 out of 10 households resort to high coping strategies, the most used of 
which are: Count on less preferred and less expensive foods (92%), Limit portion size to 
meals (72 %), Reduce the number of meals taken in a day (71%), Borrow food or count 
on the help of a friend or relative (67%), Restrict the consumption of adults so that little 
ones can eat (54%).
The overall average Food Consumption Score of households is 32.2, and 18% of 
households are in a situation of severe vulnerability with a “poor” FCS (or less than 21). 
The quality and quantity of their food is inadequate. Households in situations of severe
vulnerability (poor FCS) and limits are in similar proportions in the two health zones 
surveyed. 
The study also shows that some health areas are more affected by severe hunger than 
others. These are Banana, Some, Lukaya, Mayuwano and Salama health areas
The computation of the household hunger index (HHI) shows 21% of households in 
Mambasa HZ and 26% of households in Mandima HZ. It is therefore around 58,765 
inhabitants in the 2 Health Zones visited during the survey.
The proportions of households that have more difficulty obtaining food and therefore resort 
to high coping strategies are highest in the Health Areas of Mayuwano, Some, Salama, 
Banana, Bukulani, Lukaya.
Another aspect of food security is that across the health zones surveyed, household diets 
are not generally diverse. Households frequently consume basic foods (cereals and 
tubers), vegetables and oil.
The Low Food Diversity Score is more observed in Adult Female No Adult Male 
households, in internally displaced households, returned households and logically among 
those with poor FCS.
55 
Households with a "Poor" Food Consumption Score (which represent 18%) eat only 
vegetables or leaves, staple foods and oil. The very low level of consumption of basic 
foods (cereals and tubers) and animal proteins among households with a poor FCS should 
appeal to decision-makers at all levels.
Locals encounter various difficulties in getting to the market. The main difficulties are 
linked to a poor road conditions in the areas, which makes the distance longer and 
therefore involves a significant average cost for the household.
The knowledge about Ebola virus disease is good, although some residents are unaware 
of the causes, signs, treatments and prevention of this serious disease (There is a mixture 
with bad and good practices regarding this serious disease). Good approaches and good 
practices, prejudice and discrimination against persons with EVD, even after recovery 
continues to be observed in the community.
From an economic point of view, EVD has not only impacted the health and human sector, 
but also the economy of the region especially on the dysfunction of economic activities 
and particularly agricultural activities, which nevertheless constitute the main source of 
income for household. It is therefore important to emphasize that EVD has affected food 
security in the two Health Zones surveyed.
Regarding COVID-19, although the majority of the population (81%) has already heard of 
this pandemic, very few know the causes, symptoms and means of prevention. Over one 
quarter of the population said that COVID-19 has affected agriculture and food security in 
their communities in a certain way.
Recommendations
In view of these conclusions, urgent actions could be envisaged:
• Provide food assistance to groups that are vulnerable to food insecurity by 
prioritizing the health areas most affected by food insecurity (Makele, Mayuwano, 
Banana, Salama and Some). 
• Identify the most vulnerable households who could benefit from the assistance. 
(among IDPs), returnees and even among residents). 
• Organize the reinforcement of capacities of households to participate in production 
(for example agricultural activities, livestock rearing, small scale businesses, 
crafting and other artistic works) by facilitating access to cultivable land, seeds, 
livestock, production tools, technical supervision, etc. 
• Provide guidance on assistance to vulnerable households according to their 
specific needs or the groups to be assisted. These will be either general food 
56 
distributions, cash system or voucher to combine with the possibility of helping 
locals to carry out their usual livelihood activities. 
• Rehabilitate basic infrastructure such as roads to facilitate access to markets and
products in order to increase the income level of the inhabitants,
• Organize a more complete survey on access to basic infrastructure (schools, 
market, health facilities, etc.).
• Continue to raise public awareness on the barrier, hygiene and prevention 
measures for Ebola virus disease and the COVID-19 pandemic (COVID-19).
• Organize restitution workshops and conferences at the level of each Health Zone 
so that the population take ownership of the results of the study (survey).
57 
REFERENCES 
1. ACTED, Juin, 2018. Rapport d’evaluation des besoins en securite alimentaire et 
nfi axe lubishako1 – lubishako2, territoire de fizi, sud-kivu -
https://reliefweb.int/sites/reliefweb.int/files/resources/acted_msa_axe_lubishako1-
lubishako2.pdf
2. ACTED, Rapport d’Evaluation multisectorielle, Province du Nord-Ubangi, 
Décembre 2019
3. WFP, Enquête sur la sécurité alimentaire des ménages au Nord Kivu - Décembre 
2016 
4. Famine Early Warning Systems Network. "DRC Food Security Outlook June 2019 
to January 2020 Famine Early Warning Systems Network." Accessed July 26, 
2019. http://fews.net/sites/default/files/documents/reports/DRC_ FSO_June 
2019_ EN.pdf. 
5. FAO. 2017. The Democratic Republic of the Congo. Response Plan 2017–2018, 
Rome. 14 pp. 
6. Fauche, Adelin & Mukebayi, Kadimanche & Bakenje, Marlin & Pilipili, Koyange & 
Bongo, G.N. & Konga, Museu & Songo, Baukaka & Ngombe, Nadège & Mbemba, 
Théophile & Ngbolua, Koto-Te-Nyiwa. (2015). Knowledge, Attitude and 
Perception of the Population Related To Ebola Virus Disease in Kinshasa City, 
Democratic Republic Of The Congo. Journal Of Advancement In Medical And Life 
Sciences. 
https://www.researchgate.net/publication/279853040_Knowledge_Attitude_and_P
erception_of_the_Population_Related_To_Ebola_Virus_Disease_in_Kinshasa_Ci
ty_Democratic_Republic_Of_The_Congo
7. Fonds Africain de Développement (FAD). Août, 2011.
Projet d’Appui au Développement des Infrastructures Rurales (PADIR). 
République Démocratique du Congo. 
8. Gina Kennedy, Terri Ballard et Marie-Claude Dop, Guide pour mesurer la 
diversité alimentaire au niveau du ménage et de l’Individu, FAO, 2013.
9. Jerving, Sara. "Q&A: How the Ebola Declaration Could Threaten Food Security in 
DRC." Devex. July 18, 2019. Accessed 26 July 
10.Kinshasa Digital, 2020. Craintes et réactions face au COVID-19 en RDC, 
Sondage auprès de la population. https://www.atibt.org/wp￾content/uploads/2020/04/RDC-Sondage-COVID-19-Craintes-et-réactions￾20200414-02.pdf
11.Liro Pankakoski, Youth livelihoods and the local conflict in North Kivu, 2017
12.Malick Ndiaye; Indicateurs de la sécurité alimentaire, PAM, Dakar, juin 2014.
13.Marivoet, W., Becquey, E. & Van Campenhout, B. How well does the Food 
Consumption Score capture diet quantity, quality and adequacy across regions in 
58 
the Democratic Republic of the Congo (DRC)?. Food Sec. 11, 1029–1049 (2019). 
https://doi.org/10.1007/s12571-019-00958-3 
14.Ministère Provincial de l’Agriculture, Pêche et Elevage – Développement Rural 
Présentation de résultats d’analyses Ville de Goma, Maison de Jeunes, Mercredi 
19 Avril 2019 
15.NRC, DR Congo: Imminent hunger crisis threatens Ebola-stricken North Kivu. 
16.OCHA, Emergency operational plan, for Ituri and North Kivu Province, January to 
June 2019. 
17.Programme alimentaire mondiale (PAM), 2014 Analyse approfondie de la sécurité́
alimentaire et de la vulnérabilité́ (CFSVA) http://www.wfp.org/food-security
18.Target SARL, 2020. les congolais et la pandémie de COVID19. https://zoom￾eco.net/a-la-une/rdc-39-de-congolais-conscients-de-la-gravite-de-COVID19-
etude-de-target-sarl/
19.Terri Ballard, Jennifer Coates, Anne Swindale, Megan Deitchler, Indice 
domestique de la faim : Définition de l’indicateur et guide de mesure, USAID, 
Août 2011.
20.United Nations, 2013. République Démocratique du Congo, Plan d’action 
humanitaire 
https://reliefweb.int/sites/reliefweb.int/files/resources/2013DRCHAPFR.pdf
21.USAID Food for Peace, Indicators for Emergency Program Performance Indicator 
Reference Sheets, February 2019. 
22.USAID, Overview of youth development perspectives in the eastern Democratic 
Republic of Congo, July -2017. 
23.WFP, Evaluation approfondie de la sécurité alimentaire, Mars 2017
24.WFP, Technical guidance for WFP’S Consolidated Approach for Reporting 
Indicators of Food Security
25.World Health Organization (WHO), Ebola Rapport de situation N°319, July 2019
59 
APPENDIX 
Appendix 1. Survey Staff 
Coordination
N° NAMES Sex Function
1 Edson NIYONSABA SEBIGUNDA M Coordinator
2 Célestin KIMANUKA RURIHO M Assistant Coordinator 
and supervisor 
3 Promesse KASEREKA KAVULIRENE M Assistant Coordinator
and supervisor 
 
60 
Appendix 2. Survey Questionnaire 
Food Assistance for Ebola-Affected and Food Insecure 
Populations of Beni, Leburo and Ituri (FABELU) 
Baseline Survey
June 2020
INFORMED CONSENT Hello. My name is ___________________ and I work for ADRA DRC. We are conducting a survey about the 
FABELU project, Food Assistance for Ebola-Affected and Food Insecure Populations of Beni, Leburo and Ituri. The 
information we collect will be used for planning, implementation and evaluation of the project. You have been 
selected to be interviewed for this survey and we would very much appreciate your participation. The survey usually 
takes about 20 to 25 minutes. Your participation is voluntary, and you may end the survey at any time or decide not 
to answer a particular question. Your answers will be kept confidential. Do you agree to participate in the survey?
0 = No If No, STOP here. /__/
1 = Yes If Yes, proceed with the interview. (Ask for possible reasons_________________________________)
IDENTIFICATION
Questionnaire ID : _________________________________
Territory: _________________________________ /__ /
Health Zone: ________________________________ /__/__/
City / Village: __________________________________ 
GPS Coordinate of Household Latitude __________, Longitude ____________ 
Alt________________ Accuracy____________
INTERVIEW
Name of Enumerator: 
_________________________
Phone No. of Enumerator:
_________________________
DATE of Interview (day/month/year) /__/__/ /__/__/ /__/__/
Household phone number Phone No. 1: ______________________________
Phone No. 2: ______________________________
A. HOUSEHOLD DEMOGRAPHY
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
1 Name of respondent 
2 Age of respondents (in years)
 /__/__/
3 Sex of respondent  1. Male
 2. Female
3a. Level of Education  1. Primary
 2. High School
 3. Tertiary
 4. No Formal Education
 5. Other Specify
4 Household (HH) Size
61 
5 Name of Household member Sex Age
Status 
(Pregnant or not )
5a
5b
5c
5d
5e
5f
6 Household (HH) Gender  1. Adult Male No Adult Female (MNF)
 2. Adult Female No Adult Male (FNM)
 3. Adult Male & Adult Female (F&M)
 4. Child Only No Adult (CAN)
7 What is the status of your household in your 
current location?
 1. IDP
 2. Returnee
 3. Resident
8 If you are an IDP, where did you come from? Territory: ____________
Health Zone: ______________
Village: ________________
9 If you are a returnee, where did you displace? Territory: ____________
Health Zone: ______________
Village: ________________
B. SOURCE OF INCOME
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
10 What is your current main occupation for living?  Livestock 
 Agricultural labor
 Grazing 
 Beekeeping 
 Fishing 
 Trading (private small business)
 Handicraft 
 Civil employee
 Daily labor
 Relative support (inside or outside DRC)
 Support from charity associations
 Begging
 Other, ______________
11 What is your current monthly income level (from 
all sources)?
/_________________/ (Francs / month)
12 What is the level of debt in your household? ___________________Francs total 
62 
C. FOOD SECURITY
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1. Yes
 2. No
Has your household received any food support 
in the last two months? 
13
If yes, what is the name of the agency that 
supported your household?
14
 1. Yes
 2. No
Has your household received any livelihood 
support in the last two months? 
15
If yes, what is the name of the agency that 
supported your household?
16
 If no, go to Q19 1. Yes
 2. No
Now I would like to ask you about your 
household’s food supply during different 
months of the year.
In the past 12 months, were there months in 
which you did not have enough food to meet 
your family’s needs?
17
Do not read the 
list of the months 
but tick the months 
in which there 
were not enough 
food
If yes, which were the months in which you Jun 19 /__/ Dec 20 /__/
did not have enough food to meet your 
family’s needs?
18
July 20 19 /__/ Jan 20 /__/
Aug 19 /__/ Feb 20 /__/
Sept 19 /__/ March 20 /__/
Oct 19 /__/ April 20 /__/
Nov 19 /__/ May 20 /__/
D. HOUSEHOLD HUNGER SCALE
Sn QUESTIONS AND 
FILTERS Frequency Weight Value
19 In the past month, was there ever 
no food to eat of any kind in your 
house because of lack of resources 
to get food? If yes, how often?
Never 0
Rarely
Sometimes 1 
Often 2
20 In the past month, did you or any 
household member go to sleep at 
night hungry because there was no 
enough food? If yes, how often?
Never 0
Rarely
Sometimes 1
Often 2
21 In the past month, did you or any 
household member go a whole day 
and night without eating anything 
at all because there was not 
enough food? If yes, how often?
Never 0
Rarely
1 Sometimes
Often 2
HHS Score
63 
E. REDUCED COPING STRATEGY INDEX:
22
In the past 7 days, if there have been times when you 
did not have enough food or money to buy food, how 
often has your household had to:
Frequency
(# days) Severity Weight
Weighted Score = 
Frequency X 
weight Relative Frequency Score
a Rely on less preferred and less expensive foods? 1
b Borrow food, or rely on help from a friend or relative? 2
c Limit portion size at mealtimes? 1
d Restrict consumption by adults in order for small children 
to eat?
3
f Reduce number of meals eaten in a day? 1
TOTAL HOUSEHOLD SCORE—Reduced CSI Sum down the totals for 
each individual strategy
F. FOOD CONSUMPTION SCORE (FCS) AND HOUSEHOLD DIETARY DIVERSITY 
SCORE (HDDS):
23a - How many members are there in your household and were home in the last 7 days? 
Focus on food eaten 
INSIDE the house
Did your household 
eat the following 
item in the last 24 
hours
0 = No
1 = Yes
Number of days 
eaten in previous
7 days?
Weight weighted score 
= frequency x 
weight
What was the main 
source of the food in 
the last 7 days?
0 =Not eaten
1 = 1 day
2 = 2 days
3 = 3 days
4 = 4 days
1=Produced by the 
household 
2 
=Hunting/gathering/fishing
3 =Bought using cash
4 =Bought on credit
5 =Borrowed/gifts (friends/ 
relatives)
6 =Begging
7=Swap
8 =Food assistance
9 =Received as payment
99=Not applicable
5 = 5 days
6 = 6 days
7 = 7 days
1 Main Staples 
(Maize, 
Cassava)
2
2 Vegetables 1
3 Fruits/fruit 
juices
1
4 Meat, Fish, 
Poultry 
4
5 Pulses, legumes 
and nuts 
3
64 
23b. 24 hour and 7 day reminder
1. Yesterday, how many meals were taken by household members aged 6-59 
months
2. Yesterday, how many meals were taken by household members aged 5 years 
or older
3. Usually, how many daily meals do household members aged 6-59 months
4. Usually, how many daily meals do household members aged 5 years or older 
G. ACCESS TO PUBLIC INFRASTRUCTURE
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1. Buy from Market 
 2. Garden/farm?
 3. Other (specify)
24 Where do you get your food items? 
How many markets do you have within 
your locality?
25
(Distance in 
Km)
What is the distance to the nearest 
market from you? (Km)
26
 1. Daily
 2. Weekly
 3. Monthly
27 How often do you go to the market? 
 1. Foot
 2. Bicycle
 3. Public 
 4 Other…. Specify
What is the means of transport to and 
from the market
28
28a If other, kindly state?.....
(Congolese 
Francs)
If public bus or other how much do you 
have to pay before getting to the Market 
(Congolese Francs)
28b
(time in 
Minutes)
How long does it take you to get to the 
nearest market? (time in minutes)
29
 1. Very long
 2. Long 
 3. Average
Will you say the duration above is too 
long in getting to the market? 
30
6 Milk and milk 
products 
4
7 Oils and fats 0.5
8 Sweets
(Sugar/honey) 
0.5
Total Household Food 
Consumption score
Sum down the total for each 
HH FCS
65 
 4 Not Long …. 
 5 Very short 
 1. Yes
 2. No
Do you have any challenges going to 
and from your local market? 
31
 1. Poor Road Network
 2. Distance 
 3. Money 
 4 Other…. Specify
31a If yes what are this Challenges
What do you think can be done 
improve your access to the local 
market?............
31b 
H. MARKET INFORMATION
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1. Friends & Family 
 2. Radio
 3. Television
 4. Community information 
board 
 5 Other…. Specify
Where do you get your market information 
from? 
32
32a If other, state……………………….
 1. Daily
 2. Weekly
 3. Every two weeks 
 4. Monthly 
 5. Other…. Specify
33 How often do you receive this information 
 1. Very useful
 2. Useful
 3. Somehow useful 
 4 Not Useful 
33a Is the information mostly useful 
33b If yes/no, state reason………………
I. MARKET PRICES.
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
Which food items do you normally buy from the market? 
(Kindly List)
34
 1. Yes
 2. No
Do you normally get the type of food/items you want from 
the market? 
35
If No, which other options do you explore to get these 
items?
35a
66 
 1. Yes
 2. No
35b Do the food commodity prices change during the year?
( Unit Price 
in Congolese 
Francs)
How much do you pay for following food items?? 
(Congolese Francs) 
36
Notes Maximum Prices (Congolese 
Francs)
Minimum Prices (Congolese 
Food Items Francs)
Price Month Price Month
1. Cassava
2. Maize
3. Vegetables
4. Fruits
5. Fruit juices
6. Meat
7. Fish
8. Poultry 
9. Pulses
10. Legumes
11. Nuts
12. Milk
13. Other Milk 
products
14. Oils and fats
15. Sweets 
(Sugar/honey)
16. Other(specify)
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
1.Very Expensive 
2.Expensive 
3. Somehow Not 
4. Expensive
5. Very Cheap
36a. Will you say food items are expensive in these markets?
 1. Yes
 2. No
Are you able to afford enough quantity of the food 
commodities for your household? 
36b
36c If no, how does the household cope?
 1. Yes
 2. No
Do you have other vendors selling the same type of 
food/items you want? 
37
 1. Price is similar 
(uniform within market)
 2.Prices Vary greatly
38 Are their prices almost the same or vary largely?
If prices vary greatly, kindly state possible 
reasons?..........
38a.
J. KNOWLEDGE ON THE EXISTENCE OF EBOLA
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
39 Do you know or have heard of Ebola?  1. Yes
67 
 2. No
If yes, Describe Ebola in your own 
words………………..
39a.
39b. How did Ebola get to your community?
 1= Less than 1 Month
 2=1-3Months
 3=4 -6 Months
4=More than 6 Months
40 How long have you heard/known of Ebola? 
1= Very Common 
2=Common
3= Somehow
4= Not common
5= I don’t know
41 Is Ebola a common sickness? 
 1. Yes
 2. No
Do you know the type of people that can get 
Ebola?
42
42a If yes, List…………….
 1. Yes
 2. No
42b. Do you think you are vulnerable?
 1. Yes
 2. No
Do you consider Ebola a dangerous 
illness/sickness? 
43
43a 5a.If yes/no, state reason…………….
K. KNOWLEDGE ON CAUSES OF EBOLA
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1. Yes
 2. No
44 Are you aware of the causes of Ebola? 
1.Unprotected contact with blood, body fluids 
or tissue from an infected person with 
symptoms of EVD.
2.Unprotected sexual contact with a person 
recovering from EVD up to 12 months after 
infection with an Ebola virus.
3.Contact with contaminated objects.
4.Contact unprotected with contaminated 
surfaces, materials (such as bedding) or 
medical equipment (such as needles) 
contaminated with the Ebola virus.
5.Direct contact (e.g. handling or consumption) 
with infected animals (live or dead) or their 
body fluids,
Others
44a 1a. If yes, kindly list them………………
1= Never 
 2=Rarely (6months or more)
 3= Often (1 month – 
3months)
How often do you hear that people are sick 
with Ebola? 
45
 1. Yes
 2. No
Do you have a relative or know anyone that 
had / has Ebola? 
46
46a If yes, state relation within affected person
68 
L. KNOWLEDGE OF SIGNS AND SYMPTOMS OF EBOLA
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
 1=Yes
 2=No Are you aware of some of the signs/symptoms of people 
who have Ebola Sickness? 
47
1=Fever 
2=Vomiting 
3=Diarrhea 
4=Hemorrhage 
5=Headache 
6=Other(Specify) 
47a. If yes, State all the signs you are aware of ….. 
 1=Yes
 2=No Do you have any idea how you can protect yourself from 
getting sick with Ebola? 
48
49 If yes, state the various ways of preventing Ebola.
(all aspects of 
Ebola)
 1=Yes
 2=No Have you received any training from anyone/organization 
on Ebola? 
50
If yes, State the name of Organizations who gave this 
training?.........
50a
 1=Yes
 2=No
50b If yes (Q50 above), has the training been useful? 
M. KNOWLEDGE ON POSSIBLE TREATMENTS/CURE
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
 1=Yes
 2=No
51 Are you aware of the treatment of the Ebola Sickness? 
If yes, State all the ways of treating Ebola that you know 
of…………...
51a
51b. Do you think the following control measures work?
 1=Yes
 2=No 1. Quick identification and isolation of cases.
 1=Yes
 2=No 2. Control measures in hospital settings. 
 1=Yes
 2=No 3.Identification and follow-up of contacts 
 1=Yes
 2=No 4.Safe burials (Yes / No) 
 1=Yes
 2=No
52 Is there a medical facility located near you?
53 What is the distance of the nearest facility to you (km)
 1=Yes
 2=No
54 Do you have easy access to this facility when you fall sick? 
54a If no, state the reasons………………….
How do you see/relate to people who have Ebola 
sickness?......
55
69 
How does the community see and relate to people who have 
Ebola………………………
56
How do you see/relate to people who have recovered from 
Ebola?
57
How does the community see and relate to people who have 
recovered from Ebola?………………… 
58
N. EFFECTS OF EBOLA ON ECONOMIC ACTIVITIES
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
59 How has Ebola affected the people of the community?
 1=Yes
 2=No Do you think Ebola has affected the work of people within the 
community
60
60a Explain the reason in Q60 above
 1=Yes
 2=No Has Ebola affected agriculture and food security in your 
community? 
61
61a Explain the reason in Q61 above?
O. KNOWLEDGE AWARENESS OF COVID19
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
 1. Yes
 2. No
62 Do you know or have heard of COVID19?
62a. If yes, Describe COVID-19 in your own words………………..
62b. How did COVID-19 get to your community?
 1= Less than 1 
Month
 2=1-3Months
 3=4 -6 Months
4=More than 6 
Months
63 How long have you heard/known of COVID19? 
1= Very Common 
2=Common
3= Somehow
4= Not common 
5= I don’t know
64 Is COVID-19 a common sickness? 
 1. Yes
 2. No
65 Do you know the type of people that can get COVID19?
65a If yes, List…………….
 1. Yes
 2. No
65b. Do you think you are vulnerable?
 1. Yes
 2. No
66 Do you consider COVID-19 a dangerous illness/sickness? 
66a 5a.If yes/no, state reason…………….
70 
P. KNOWLEDGE ON CAUSES OF COVID19
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1. Yes
 2. No
Are you aware of the causes of COVID19? 67
67a If yes, kindly list them………………
1= Never 
 2=Rarely (6months or more)
 3= Often (1 month – 3months)
How often do you hear that people are sick 
with COVID19? 68
 1. Yes
 2. No
Do you have a relative or know anyone that 
had / has COVID19? 69
69a If yes, state relation within affected person
Q. KNOWLEDGE OF SIGNS AND SYMPTOMS OF COVID19
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1=Yes
 2=No
Are you aware of some of the signs/symptoms of 
people who have COVID-19 Sickness? 70
 1=Fever 
 2=Dry caugh 
 3=Headache 
 4=Difficulty breathing 
 5=Loss of teste 
 6=Loss of smell 
 7=Flu
 8= Exaggerated weakness
 9=Others …………
If yes, State all the signs you are aware of ….. 70a.
 1=Yes
 2=No
Do you have any idea how you can protect yourself 
from getting sick with COVID19? 71
 1=Practice good hygiene 
practices
 2= Avoid unprotected 
direct contact
 3= Avoid high-risk 
regions and activities
 4= Avoid unprotected 
sexual activity
 5= Follow safe funeral 
practices
 6= Avoid coming into 
contact with wild animals
 7= Vaccination
 8= Other
72 If yes, state the various ways of preventing COVID19. 
(all aspects of 
COVID19) 
 1=Yes
 2=No
Have you received any training from 
anyone/organization on COVID19? 73
If yes, State the name of Organizations who gave this 
training?......... 73a
 1=Yes
 2=No 73b If yes (Q73 above), has the training been useful? 
71 
R. KNOWLEDGE ON POSSIBLE TREATMENTS/CURE
Sn QUESTIONS AND FILTERS CODING RESPONSES Notes
 1=Yes
 2=No Are you aware of the treatment of the COVID-19 
Sickness? 74
If yes, State all the ways of treating COVID-19 
that you know of…………... 74a
Do you think the following control measures 
74b. work? 
 1=Yes
 2=No 1. Quick identification and isolation of cases. 
 1=Yes
 2=No
2.Identification and follow-up of contacts
 1=Yes
 2=No 3. Control measures in hospital settings. 
 1=Yes
 2=No Is there a medical facility located near you? 75
What is the distance of the nearest facility to you 
(km) 76
 1=Yes
 2=No Do you have easy access to this facility when you 
fall sick? 77
77a If no, state the reasons………………….
 1= No contact
 2=With distrust / discrimination
 3=Good relationships
 4=Very good relationships
 5=Other Specify ................
How do you see/relate to people who have 
COVID-19 sickness?...... 78
 1= No contact
 2=With distrust / discrimination
 3=Good relationships
 4=Very good relationships
 5=Other Specify ................
How does the community see and relate to people 
who have COVID19……………………… 79
 1= No contact
 2=With distrust / discrimination
 3=Good relationships
 4=Very good relationships
 5=Other Specify ................
How do you see/relate to people who have 
recovered from COVID19? 80
 1= No contact
 2=With distrust / discrimination
 3=Good relationships
 4=Very good relationships
 5=Other Specify ................
How does the community see and relate to people 
who have recovered from 
COVID19?………………… 81
72 
S. EFFECTS OF COVID-19 ON ECONOMIC ACTIVITIES
Notes CODING 
RESPONSES Sn QUESTIONS AND FILTERS
How has COVID-19 affected the people of the community? 82
 1=Yes
 2=No Do you think COVID-19 has affected the work of people 
83 within the community
83a Explain the reason in Q83 above
 1=Yes
 2=No
Has COVID-19 affected agriculture and food security in 
your community? 84
84a Explain the reason in Q84 above?
THANK YOU FOR YOUR TIME AND CONTRIBUTION TO THIS SURVEY…
73 
Appendix 3. Details of results tables 
Appendix 3.1 Average food consumption score by category
Category N Mean SD VC Median p-value
Health Zone
Mambasa 210 32,6 14,0 43% 29,5 0,654 Mandima 210 31,9 14,7 46% 28,5
Household type (HH)
Adult Female No Adult Male
(FNM)
64 28,0 10,3 37% 26,8
Adult Male & Adult Female 0,033
(F&M)
332 33,1 14,9 45% 29,5
Adult Male No Adult 
Female (MNF)
24 32,3 12,8 40% 29,5
Household status in its 
current location
Internally Displaced 
Persons (IDP)
61 27,7 10,8 39% 25,5
0,001
Resident 309 33,8 15,3 45% 30,0
Returnee 50 28,3 9,4 33% 27,0
Gender of head of 
household
Feminine 139 31,6 14,8 47% 28,5 0,511 Male 281 32,6 14,1 43% 29,5
Aggregate 420 32,2 14,3 44% 29,0
Appendix 3.2. The reduced Coping Strategy Index by category
Category n Mean SD Median P-value High 
Coping
Medium 
Coping
No or low 
Coping
Health Zone
Mambasa 210 19,4 13,7 18 0,746 71% 18% 11%
Mandima 210 18,9 13,7 17 69% 21% 10%
Health areas
Biakako Mayi, 30 17,2 13,6 15,5 <0,0001 53% 40% 7%
Biakato Mine, 30 15,4 13,7 13,0 60% 23% 17%
Katanga, 30 10,6 8,6 8,0 47% 33% 20%
Lukaya, 30 20,5 13,8 18,0 70% 27% 3%
Makeke 30 14,7 12,3 13,5 60% 17% 23%
Mayuwano, 30 25,7 11,7 24,5 97% 0% 3%
Some, 30 28,3 13,6 25,5 97% 3% 0%
Banana 30 27,7 14,2 22,5 90% 10% 0%
Binase 30 13,8 11,9 11,5 57% 20% 23%
Bukulani 30 22,4 13,2 20,5 80% 20% 0%
74 
Category n Mean SD Median P-value High 
Coping
Medium 
Coping
No or low 
Coping
Makoko2 30 16,4 12,4 15,5 60% 27% 13%
Mambasa 30 13,8 11,6 11,0 60% 20% 20%
Salama 30 30,1 12,1 29,5 97% 3% 0%
Tobola 30 11,3 7,7 11,0 57% 27% 16%
Gender of head of 
household
Feminine 139 22,2 13,4 20 0,001 81% 12% 8%
Male 281 17,6 13,6 15 65% 23% 12%
Household type (HH)
Adult Female 
No Adult Male
(FNM)
64 24,5 12,9 23,5 0,003 91% 6% 3%
Adult Male & 
Adult Female
(F&M)
332 18,1 13,7 16 66% 23% 12%
Adult Male No 
Adult Female
(MNF)
24 19,3 13,3 19,5 79% 8% 13%
Household status in its current location
Internally 
Displaced 
Persons (IDP)
61 22,9 12,6 23 0,057 87% 11% 2%
Resident 309 18,7 14,2 16 67% 21% 12%
Returnee 50 17,4 11,3 16,5 68% 20% 12%
Aggregate 420 19,1 13,7 17 70% 19% 11%
75 
Appendix 3.3. FCS by Heath areas 
Health areas n FCS
Acceptable
FCS
Borderline
FCS
Poor
Biakako Mayi, 30 40% 50% 10%
Biakato Mine, 30 50% 33% 17%
Katanga, 30 40% 57% 3%
Lukaya, 30 33% 63% 3%
Makeke 30 33% 23% 43%
Mayuwano, 30 7% 50% 43%
AS Some, 30 13% 63% 23%
Banana 30 20% 50% 30%
Binase 30 60% 33% 7%
Bukulani 30 13% 77% 10%
Makoko2 30 40% 47% 13%
Mambasa 30 23% 63% 13%
Salama 30 27% 43% 30%
Tobola 30 33% 60% 7%
Aggregate 420 31% 51% 18%
76 
Appendix 3.4. Price of 14 basic necessities in the territory of Mambasa from January 2019 to May 2020 in CDF
 J-19 F-19 M-19 A-19 M-19 JN19 JL-19 A-19 S-19 O-19 N-19 D-19 J-20 F-20 M-20 A-20 M-20 Average 
price
1. Cassava flour (kg) 400 400 400 400 400 400 400 400 400 500 500 500 500 500 500 500 500 447
2. Peeled dry corn (kg) 800 800 800 800 800 800 800 800 1000 1000 1000 1000 1000 1000 1200 1200 1200 941
3. Cassava leaves 
(Sombe) (bunch) 250 250 250 250 250 250 250 250 250 250 500 500 500 500 500 500 500 353
4. Sweet banana (diet) 4000 4000 4000 4000 4000 4000 4000 6000 6000 6000 6000 6000 7000 7000 7000 7000 8000 5529
5. Natural Fruit Juice 
(bottle of 33cl) 500 500 500 500 500 500 500 500 500 500 700 700 700 700 700 700 700 582
6. Boneless beef meat 
(kg) 7000 7000 7000 7000 7000 7000 7000 8000 8000 8000 8000 8000 8000 9000 9000 9000 9000 7824
7. Fresh Tilapia fish 
(kg) 9000 9000 9000 9000 9000 9000 9000 9000 9000 9000 10000 10000 10000 10000 10000 10000 10000 9412
8. Local live chicken 
(piece) 15000 15000 15000 15000 15000 15000 15000 15000 17000 17000 17000 17000 17000 17000 17000 19000 20000 16353
9. Multicolored beans 
(kg) 2000 2000 2000 2000 2000 2500 2500 2500 2500 2500 2500 2500 2500 3000 3000 3000 3000 2471
10. Fresh tomatoes (kg) 2500 2500 2500 2500 3000 3000 3000 3000 3000 3000 3000 4000 4000 4000 4000 4000 4000 3235
11. Coconut (piece) 500 500 500 500 500 500 500 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 794
12. Fresh milk (liter) 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 3000 3000 3000 3000 3000 3000 2353
13. Palm oils (bottle of 
72cl) 600 600 600 600 600 800 800 800 800 800 800 800 800 1000 1000 1000 1000 788
14. Sugar (kg) 1500 1500 1500 1500 1500 1500 1500 1500 2000 2000 2000 2000 2000 2000 2000 2000 2000 1765
Source : Economy service in Mambasa territory
77 
Appendix 3.5.Comparison of food security indicators by health zone of FABELU project
Indicators Modalities Mambasa 
(n=210)
Mandima 
(210)
Alimbongo 
(n=60)
Beni 
(n=90)
Kalunguta 
(n=60)
Kayna 
(n=90)
Lubero 
(n=60)
Mutwanga 
(n=60)
Oicha 
(n=60)
Aggregate 
Project 
FABELU 
(n=900)
FCS
Poor 16% 20% 28% 16% 12% 8% 15% 7% 17% 16%
Borderline 53% 49% 35% 31% 63% 57% 45% 20% 42% 46%
Acceptable 31% 31% 37% 53% 25% 36% 40% 73% 42% 38%
rCSI
High 
coping 71% 69% 82% 71% 95% 66% 57% 37% 78% 70%
Moderate 
coping 18% 20% 17% 26% 5% 28% 28% 42% 7% 21%
No or low 
coping 10% 10% 2% 3% 0% 7% 15% 22% 15% 9%
HHS
Serve 
hunger 21% 26% 13% 18% 20% 8% 12% 10% 33% 19%
Moderate 
hunger 51% 47% 53% 51% 73% 53% 47% 43% 45% 51%
Low or no 
hunger 28% 27% 33% 31% 7% 39% 42% 47% 22% 30%
78 
Appendix 3.6 Average number of days of consumption of each type of food by category
Category
Cereals 
and 
tubers
Vegetables
Fruit/ 
Fruit 
juices
Meat, 
Fish and 
Poultry
Legumes and 
nuts 
Other 
Milk 
products
Oils and 
fats
Sweets 
(Sugar/honey)
HS Mambasa 4.8 5.8 2.1 1.2 1.8 0.2 6.3 1.2
HZ Mandima 4.8 5.8 1.6 1.2 1.9 0.2 6.2 0.8
Female Head of 
Household 5.0 5.7 1.6 1.1 1.6 0.4 6.2 1.1
Male Head of 
Household 4.7 5.9 1.9 1.2 2.0 0.2 6.3 1.0
Adult Female No 
Adult Male (FNM) 4.7 6.0 1.4 0.8 1.5 0.0 6.1 0.5
Adult Male & Adult 
Female (F&M) 4.9 5.8 1.9 1.3 1.9 0.3 6.4 1.1
Adult Male No Adult 
Female (MNF) 4.0 5.0 2.6 1.3 2.2 0.3 5.5 1.5
FCS Acceptable 6.2 5.6 3.0 2.6 3.5 0.8 6.5 1.9
FCS Borderline 4.9 5.9 1.6 0.7 1.3 0.0 6.4 0.7
FCS Poor 2.2 6.0 0.5 0.1 0.7 0.0 5.5 0.2
Internally Displaced 
Persons (IDP) 5.1 5.7 0.9 0.6 1.7 0.0 6.2 0.6
Resident 4.8 5.8 2.1 1.3 2.0 0.3 6.3 1.2
Returnee 4.7 5.6 1.5 1.3 1.1 0.0 6.2 0.3
Aggregate 4.8 5.8 1.8 1.2 1.9 0.2 6.3 1.0
79 
Appendix 4. Mapping of HH survey 
80 
FABELU PROJECT BASELINE STUDY
***********************************************************
The study was conducted 
by CIS/ISSNT from DRC 
INSTITUT SUPERIEUR DE 
STATISTIQUE ET DE NOUVELLES 
TECHNOLOGIES ISSNT
 Centre Informatique et Statistique 
« C.I.S »
REPUBLIQUE 
DEMOCRATIQUE 
DU CONGO (RDC)