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TECHNICAL REPORT ON ASSESSING NUTRITION STATUS, MORBIDITY AND MORTALITY OF CHILDREN, DAMASAK TOWN, MOBBAR LGA, BORNO STATE, NIGERIA SMART survey in Damasak Central and
Zanna Umarti wards of Mobbar LGA
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SMART Methodology- Orientation
What is SMART?
SMART (Standardized Monitoring and Assessment for Relief and Transition) is an inter-agency initiative launched in 2002 by a network of organizations and humanitarian practitioners. SMART is a standardized, simplified field survey methodology that produces a snapshot of the current situation on the ground. Developed in 2006 by a panel of experts in epidemiology, nutrition, food security, early warning systems and demography, SMART was originally devised to assess acute malnutrition and mortality in emergencies. It is now used in all settings, including development and displaced populations.
Surveys using SMART produce representative, accurate and precise estimates of global acute malnutrition (GAM), chronic malnutrition (stunting), underweight and retrospective mortality. These four indicators gathered through the SMART methodology provide the best available validated data that can be used for effective decision making and resource allocation.
Why SMART methodology? SMART advocates a multi-partner, systematized approach to provide critical, reliable information for decision-making, and to establish shared systems and resources for host government partners and humanitarian organizations. SMART is an improved survey method that balances simplicity (for assessment of acute malnutrition) and technical soundness. The SMART method ensures consistent and reliable survey data to be collected and analyzed using single standardized methodology. The plausibility test he lps to verify data quality and flag problems. The Global Nutrition Cluster also approves the methodology and encourages its dissemination. The SMART methodology is complimented by user-friendly software known as ENA (Emergency Nutrition Assessment) which helps to simplify all stages of survey starting from sample size calculation to automated report generation.
SMART:
Incorporates core elements of several survey methodologies and is continuously updated with current research and best practices.
ENA software provides a standardized reporting format that simplifies data entry and analysis.
Facilitates the survey process with flexible sample & cluster sizes, and standardizes survey protocols with the use of replacement clusters, household selection techniques and best field practices (e.g. for absent children or empty households).
Today, SMART is recognized as the standard methodology by national Ministries of Health of various countries, donors, and implementing partners such as international NGOs and UN agencies that wish to undertake nutrition and mortality surveys in all settings (emergency, development, displaced populations). SMART is also incorporated into many national nutrition protocols.
What is the expected outcome from a SMART survey?
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S Zanna Umorti Wards of Mobbar LGA)s in Borno state. In terms of nutrition status, prevalence
Contents List of Tables 5
List of Figures 6
List of Abbreviations and acronyms 7
Acknowledgments 8
Executive Summary: 9
1. Introduction 11
1.1. Borno State 11
1.2. Mobbar Local Government Area 12
1.2.1 Damasak Town 12
1.3. Humanitarian Assistance in Borno 13
1.4. Nutrition and Health Context 14
1.5. Objectives of the Survey 15
1.5.1. General Objectives 15
1.5.2. Specific Objectives 15
2. Methodology 16
2.1. Study Design 16
2.2. Target population 16
2.3. Sampling methodology 16
2.4. Sample Size Calculation 16
2.4.1. Sample size estimation of Acute Malnutrition 17
2.4.2. Sample size calculation for Mortality: 17
2.5. Final Sampling Strategy 18
2.6. Cluster Selection 18
2.7. Household Selection Techniques 18
2.8. Survey Teams 19
2.9. Survey equipment 19
2.10. Key Variables 19
2.11. Daily field procedure 19
2.12. Data collection and Supervision 20
2.13. Training of Enumerators 20
2.14. Data Collection Schedule 20
2.15. Data analysis and interpretation 20
2.16. Reserve Clusters: 21
2.17. Ethical clearance 21
4. Results of Child Nutrition 22
4.1 Survey Achievements 22
4.2 Anthropometric results (based on WHO standards 2006) 22
Prevalence of Acute Malnutrition by Weight for Height Z-score 23
Prevalence of Acute Malnutrition by Mid-Upper Arm Circumference 24
4.2.3 Comparison of Acute Malnutrition by WHZ and MUAC 25
4.3 Prevalence of Underweight 26
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4.4 Prevalence of Chronic Malnutrition 27
4.5 Morbidity and Immunization: 28
Immunization: 28
Vitamin A Supplementation 29
Use of mosquito net: 29
Coverage of deworming tablets: 29
Morbidity and treatment 29
5. Mortality rate: 30
6. Maternal Malnutrition 31
6.1 Maternal Malnutrition with MUAC 31
6.2 Maternal Malnutrition (BMI) 32
7. Infant and Young feeding practices 32
7.1 Early initiation of breastfeeding for children 0-23 months 32
7.2 Exclusive Breast-feeding 33
7.3 Initiation of Complementary Feeding and Continued Breastfeeding 33
7.4 Frequency and Diversity in Complementary Feeding 33
8. Water, Sanitation and Hygiene: 34
8.1 Source of drinking water 34
8.2 Water quantity consumed for drinking and cooking: 34
8.3 Time spent in fetching water 35
8.4 Practice and methods for water purification 35
8.5 Handwashing practices 35
8.6 Defecation practices 36
9. Food Security and Livelihood 36
9.1 Main source of income 36
9.2 Agriculture, livestock and humanitarian support 37
9.3 Main source of Food 38
9.4 Main shocked faced by the community: 38
9.5 Food consumption score 39
9.6 Reduced coping strategy Index (rCSI) 39
10. Discussion 40
11. Conclusion 43
11.1 Limitations of the survey: 44
12. Recommendations 45
12.1 Preventive strategies for awareness and BCC strategies 45
12.1.1 Short term awareness strategy: 45
12.1.2 Long term awareness and capacity building strategies 45
12.2 Treatment for Acute Malnutrition 45
12.2.1 Short term strategy for the treatment of acute malnutrition 45
12.2.2 Long term strategy for management of Acute Malnutrition 47
12.3 Research for simplified CMAM 48
12.3.1 Short term strategies on research for simplified CMAM 48
12.4 Surveillance strategies 49
12.4.1 Short term strategies for surveillance: 49
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12.4.2 Long term strategies for Surveillance: 50
13. Annexure 51
13.1 Plausibility check for: NG_SMART_Damasak_final data.as 51
13.2 List of Indicators 52
13.3 Questionnaire 57
13.3.1 Anthropometry Questionnaire 57
13.3.2 FCS and rCSI Questionnaire 58
13.3.3 FSL Questionnaire 58
13.3.4 WASH Questionnaire 60
13.3.5 IYCF Questionnaire 61
13.3.6 Demography and Mortality Questionnaire 64
13.4 List of Clusters 65
13.5 Training Schedule 67
List of Tables Table 1 Sample size estimation of Acute Malnutrition ............................................................................... 17
Table 2 Sample size calculation for Mortality assessment in SMART survey on Damasak Town ............... 17
Table 3 showing survey targets and achievements in the SMART survey .................................................. 22
Table 4 Distribution of age and sex of children surveyed .......................................................................... 22
Table 5 Prevalence of acute malnutrition based on WFH z-scores and by gender ..................................... 23
Table 6 showing the Prevalence of acute malnutrition by age, based on WFH z-scores. .......................... 24
Table 7 Distribution of acute malnutrition and edema based on WHZ ......... Error! Bookmark not defined.
Table 8 Prevalence of acute malnutrition based on MUAC cut offs (and/or edema) and by gender .......... 24
Table 9 Prevalence of acute malnutrition by age, based on MUAC cut offs and/or edema ....................... 25
Table 10 showing the prevalence of GAM and SAM. ................................................................................ 25
Table 11 Prevalence of underweight based on WFA z-scores by gender. .................................................. 26
Table 12 Prevalence of underweight by age, based on WFA z-scores ....................................................... 27
Table 13 Prevalence of stunting based on HFA z-scores and by gender .................................................... 27
Table 14 Prevalence of stunting by age based on HAZ .............................................................................. 28
Table 15 Mean z-scores, Design Effects and excluded subjects ................................................................. 28
Table 16 Death rates of Damasak town ...................................................................................................... 30
Table 17 showing the prevalence of malnutrition among women with MUAC ......................................... 31
Table 18 showing prevalence of underweight as per BMI in Damasak town ............................................. 32
Table 19 complementary feeding status as per age groups in Damasak town ............................................ 34
Table 20 showing average water intake per capita per day by community members of Damasak town . 35
Table 21 shows relationship between GFD with FCS and rCSI ................................................................... 42
Table 22 showing the FCS and rCSI categories of the respondents with different income sources. ......... 42
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List of Figures Figure 1 showing map of Nigeria with Borno state .................................................................................... 11
Figure 2 showing map of Damasak town with locations ............................................................................ 12
Figure 3 showing the seasonal calendar of North Nigeria (Source: FEWS NET 2019) ................................ 15
Figure 4 showing data distribution as compare to WHO graph ................................................................. 23
Figure 5 showing overlap of MUAC and WHZ in Damasak town ................................................................ 26
Figure 6 showing illness reported among children under 5 years in Damasak town ................................. 30
Figure 7 showing the feeding practices for children >9 months in Damasak town.................................... 33
Figure 8 showing source of water for Damasak town community ............................................................. 34
Figure 9 showing the water purification methods implemented by community in Damasak town .......... 35
Figure 10 showing proportion of handwashing events in the survey population ...................................... 36
Figure 11 showing the sources of income of population in Damasak town ............................................... 36
Figure 12 showing the humanitarian assistant provided in past 3 months to the Damasak town
community .................................................................................................................................................. 37
Figure 13 showing proportion of people have cultivated in past season and livestock ............................. 37
Figure 14 distribution of sources of food for families in Damasak in past 7 days ...................................... 38
Figure 15 showing the main shocked faced by the community of Damasak ............................................. 38
Figure 16 showing the distribution of food consumption score for Damasak town .................................. 39
Figure 17 showing the coping strategy index implemented by Damasak community to mitigate food
needs ........................................................................................................................................................... 39
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List of Abbreviations and acronyms AAH Action against Hunger BMI Body Mass Index BSFP Blanket Supplementary Feeding Program BSU Basic Sampling Unit CDC Center for Disease Control C.I Confidence Interval CMAM Community Management of Acute Malnutrition CMR Crude Mortality Rate DEFF Design Effect DPS Digit Preference Score ECHO European Civil Protection and Humanitarian Aid Operations ENA Emergency Nutrition Assessment FAO Food and Agriculture Organization FCT Federal Capital Territory GAM Global Acute Malnutrition GFD General Food Distribution GUW Global Underweight HAZ Height-for-age z-score HH Household IDP Internally Displaced People IYCF Infant and Young Child Feeding IYCF-E Infant and Young Child Feeding in Emergencies
KM Kilometer LGA Local Government Area MAM Moderate Acute Malnutrition MUW Moderate Underweight MUAC Mid-Upper Arm Circumference NCA Nutrition Causal Analysis NSAG Non-State Armed Group ODK Open Data Kit OTP Outpatient Therapeutic Program PPS Probability Proportion to Size PSU Primary Sampling Unit RC Reserve Cluster RRM Rapid Response Mechanism SAM Severe Acute Malnutrition SC Stabilization Center S.D Standard Deviation SFP Supplementary Feeding Program SMART Standardized Monitoring and Assessment of Relief and Transitions TAG Technical Alliance Group TFP Therapeutic Feeding Program U5MR Under-five Mortality Rate UNICEF United Nations Children's Fund WASH Water, Sanitation, and Hygiene WAZ Weight-for-Age z-score WFP World Food Programme W/H tables Weight-for-height tables WHO World Health Organization WHZ Weigh-for-Height z-score
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Acknowledgments
I would like to express my deepest gratitude to LGA and state authorities for providing their support to carry out SMART Survey successfully in Damasak town, Borno state of Nigeria. I extend my sincere gratitude to Ms. Warimu Mburathi (Head of Department, AAH Nigeria), and Mr. Aychiluhim Mitiku (Head of Department, AAH Nigeria) for their unceasing guidance, technical inputs and support round the clock to carry out SMART Survey. Also, an entire ECHO, finance and logistic team for their unquenchable enthusiasm to support this survey. I sincerely thank Mr. Simon Karanja (Nutrition Cluster Coordinator- UNICEF), Mr. Adamu (UNICEF) and Mr. Sanjay Kumar Das (Nutrition Specialist, UNICEF Nigeria) for their unwavering support during planning and implementation phase of the survey. I am also thankful of Damasak field Team in AAH for providing necessary support to conduct this survey and SPCHDA for their great cooperation throughout the process. I would thank my colleagues at Action Against Hunger, Nigeria team for their support. Ms. Endurance Nwahiri and Mr. Daniel Lalai helped us managing SMART logistics at the base level. Also, we thank Dr. Lanre for providing technical support for overall management for this survey. I also thank three enthusiastic and energetic AAH M&E staff, Ms. Ndifreke James, Mr. Anda Zakaria Saleh and Ms. Fatima Abba Kolobe along with two AAH ECHO program staff, Mr. Simon Maitala and Dr. Raphael Aworinde for their support and efforts throughout to make this survey successful. Also, we thank the whole Logistic, M&E and ECHO team of AAH Maiduguri base for their support and cooperation. Also, I want to appreciate the hard- work and commitment of supervisors and team members who carried out data collection with utmost sincerity. They braved the difficult topography and volatile security situation to reach sampled households. Their teamwork, commitment and dedication to reach sampled households and collect data are the success of this survey. Last but not the least, we are really indebted to camp coordinators, Bulama`s, Lawan`s and families who wholeheartedly welcomed, cooperated with us to join the survey and allowed their children to be weighed and measured. Also, I thank the drivers for taking care of the teams on the road and by going much beyond to aid them on the field. The successful completion of this survey is the result of hard work and joint efforts of everyone mentioned above. Thank you everyone!
Dr. Narendra Patil
SMART Survey Manager
Action Against Hunger, Nigeria
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Executive Summary: Background: The survey area was Damasak town, Borno State of Nigeria, where about 51,000 inhabitants were living. However, due to the ongoing conflict, population figures are highly fluid for the survey area as there is a continuous movement of people in and out of trenches. The result of the previous NFSS survey conducted in Northern Borno in November 2019 indicated that the prevalence of Global Acute Malnutrition (GAM) was 9.4%, of which 1.5% were severely malnourished. Objectives: The main objective of the survey was to determine the nutritional status of children aged 6 to 59 months and women between the age group of 18-49 years in Damasak town. This survey also aimed to capture the mortality status of the community. Also, indicators related to Infant and Young Child Feeding (IYCF) practices (0-23 months), morbidity status (0-59 months), immunization coverage (12 to 59 months), food security situation and Water, Sanitation and Hygiene (WASH) conditions were assessed in this survey. Methodology: The cross-sectional nutrition survey based on the SMART methodology was employed. The data collection conducted from 5th – 15th March 2020. PPS (Probability Proportionate to Size) method was used to identify clusters from the sampling frame i.e. the list of wards. Sampling method: This survey was conducted using two stage cluster random sampling methodology. Sample size: A total of 45 clusters and 588 households (422 children under 5 years) were selected for this survey as a sample for the nutrition component of the survey. Data collection: Data was collected using Smartphones and paper-based questionnaires in the field. This data was collected using the ODK interface and uploaded on the Kobo Server. Data analysis: Data were mainly analyzed using Emergency Nutrition Assessment (ENA) software for SMART (July 2015 version) and Epi- Info application for windows version 3.5.4. In addition, SPSS version 20 was also used especially for advanced statistical analysis. Results: A total of 429 children aged 6-59 months participated in anthropometry survey. The overall prevalence of Global Acute Malnutrition (GAM) and Severe Acute Malnutrition (SAM) based on weight-for-height z-score was 6.5%( 4.5%–9.5% 95% C.I.) and 0.9% (0.4%-2.4%: 95% C.I.) respectively. The prevalence of GAM and SAM with MUAC was 1.6% (0.8%-3.3% 95% C.I) and 0.5% (0.1%-1.9%: 95% C.I) respectively. Also, no case of bilateral pitting edema was identified in the survey. The prevalence of underweight for Damasak town was 14.7% (11.2%-19.1% 95% CI) and severe underweight is 2.1% (1.0%-4.2% 95% CI). The data also suggests that the prevalence of GUW and SUW is higher among boys than girls. Chronic malnutrition is a public health problem in Damasak affecting a large proportion of children. Prevalence of stunting and severe stunting was 26.9 %( 21.3% -33.3%, 95% C.I.), and 3.7% (2.1% - 6.5%; 95% CI) respectively. The prevalence of moderate malnutrition among mothers was 11.7% with MUAC, whereas the prevalence of malnutrition with MUAC among non-mothers was 2.9% and 15.9% for severe and moderate malnutrition respectively. In terms of morbidity, about 38.6%, of children have suffered from cough and fever respectively two weeks prior to data collection whereas 32.7% of children suffered from diarrhea. Among the children who suffered illness, 40.6% of caretakers preferred mobile clinic as a choice for the treatment for their children. Among the children from 9-59 months age group, 85.8% received the measles (with or without card) and 86.1% received vitamin A supplementation. Also, deworming tablet was received by 67.0% of the sampled children. Among the young infants, 22.4% was put on the breast of the mother within the first hour of birth and 77.6% of infants received colostrum. The data also shows that 38.2% (25.4% - 52.3%) of children under 6 months were exclusively breastfed. In terms of complementary feeding, 60% of children received food at 6-8 months of age. At the age of 6-23 months, 58.6% of children received meals as per minimum meal frequency, whereas among 6-23 months 33.1% received the minimum adequate diets.
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Borehole is the most preferred source of water as 98.5% of respondents use borehole as the main water supply source. Also, 74.2% respondents needed less than 60 minutes to fill the water required. On an average, 23.4 liters of water used by per person per day, which is above the SPHERE standards. Majority of the respondents (72.7%) doesn`t do any treatment for drinking water. Also, 22.2% uses soap with water in this community whereas no family washes hands at all 5 critical events also only 31.2% population washes hands on three critical times i.e. before cooking or eating and after defecation. The majority of the respondents used the hole (48.0%) for defecation. The main source of income is casual laborers as about 25.5% of the population have the same profession. About 86.4% population consumed acceptable food for the past seven days prior to the survey, whereas 3.4% had poor food consumption scores. The data also shows that about 33.7% has been using high coping strategy to survive the food scarcity, whereas 39.4% used low coping strategy. Conclusion: This survey falls under the “excellent” range as per the Plausibility test of SMART survey classification as the data received 6% penalty points. The prevalence GAM in the study area was 6.5%, indicating the survey population is under ‘moderate’ condition based on the WHO classification. Also, the retrospective mortality rates for the general population and under 5 population were below the alarming category as per the CDC standards. Moreover, the colostrum feeding and minimal meal frequency show a promising picture, but still, only two in ten children received milk in the first hour of delivery, whereas slightly higher than 30% of children were exclusively breastfed. The data shows that the majority of the population has consumed acceptable food. However, still about one-third of the survey population also used high coping strategies to survive the food scarcity. This paradox needs to understand further in order to analyze the situation better. The analysis of this data shows that there is a weak yet negative relationship between food consumption and coping. This means even though data shows that food consumption is acceptable, but it also includes extensive coping that maybe adding a sense of un-satisfaction among the community. This could be mainly due to sky rocketed prices of the food of choice as some part of the community perceive it as the mainshock (16.0%). Therefore, a detailed study to understand the gap between need and supply related to food aid requires to be considered. Also, in terms of the source of water, boreholes top the chart however no use of purification techniques also raised the concern since there is a possibility of water contamination during handling at the source. These phenomena along with many other causes may have also caused 32.7% of children suffer from diarrhea in past 15 days prior to data collection. Recommendations: Considering the prevalence of acute malnutrition are moderate as per the WHO thresholds, there is a need for a holistic approach to mitigate malnutrition. The short term strategies could include extensive behavioral change communication to enhance MYCN practices. Along with the awareness and management of SAM, innovations validated through research to simplify the CMAM strategies along with conducting the studies to understand the dynamics of malnutrition using methodologies like Nutrition Causal Analysis and Anthropological studies will help to minimize the malnutrition prevalence in the town. This survey area has a unique advantage of conducting a proper surveillance program mainly due to small geography and population but also due to the fact that majority of the population is resident of the town. Also, existing programs have unintentionally laid out the foundation of the surveillance by conducting half yearly household enumerations as well as monthly screening through field volunteers as well as mother groups. Therefore establishing the surveillance is quite feasible as the human resource capacity is already established. Although surveillance strategy is multidimensional hence requires support from various stakeholders in order to create child wise database of program interventions through M&E, creation and timely collection of surveillance data through program activities and constant analysis to generate feedback mechanism to ensure the quality of the program. This strategy need to design with the aim of identifying hotspots of malnutrition in order to create a sentinel surveillance program to ensure the adequate utilization of the resources as well as to move towards sustainable solutions.
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1. Introduction
An early childhood period i.e. 1000 days of life is essential to be provided with adequate nutrition to
ensure the growth, health and development of children to their full potential1. As per the World Health
Organization (WHO), malnutrition is a serious problem that can be associated with a substantial increase
in the risk of mortality and morbidity2. The World Bank report also suggests that malnutrition slows the
economic growth and perpetuates poverty3, where mortality and morbidity associated with malnutrition
represent a direct loss in human capital and productivity for the economy.
Nigeria Republic also known as a “Giant of Africa” thanks to the large population proportion across the
continent. Nigeria shares the boarder with Chad, Cameroon, Niger and Benin across all axis along with
Atlantic Ocean (Gulf of Guinea) from South-west border. This country has 36 states and a Federal Capital
Territory (FCT), which is National capital ‘Abuja’. The population of the country is inhabited by
approximately 200 million as per the 2019 estimates4 that made Nigeria as a ninth most populous country
in the world.
Action Against Hunger (AAH) has an active presence in Nigeria since past one decade. From 2014 to 2015,
AAH has doubled the volume of its operations in response to the crisis to mitigate the humanitarian needs
of 2.1 million people with health and nutrition programs; clean water and sanitation to reduce
malnutrition and disease; emergency cash transfers to help displaced people purchase food or meet other
urgent needs and long-term food security initiatives. In 2016, it has scaled up programs in Nigeria even
further, yet again doubling the volume of operations to meet rising needs, despite an extremely
challenging environment.
1.1. Borno State The Borno state is one of the largest State in the
Northeast region of Nigeria in terms of
geography. The capital of the state is Maiduguri.
This state is comprised of 27 local Government
Areas (LGAs) which has been part of three
senatorial districts of the state. This state is a
homeland of Kanuri people in Nigeria. However,
since past decade, the insurgency from Non-
Security Armed Groups (NSAG) have disrupted
the life in this state and the majority of
population greatly affected. Government and
Humanitarian agencies including INGOs and UN
have tried their best to minimized the
catastrophe of the crisis however so far they have
fail to resolve the issues faced by the community.
1 http://www.ncbi.nlm.nih.gov/books/NBK148967/
2 http://www.who.int/quantifying_ehimpacts/publications/eb12/en/ 3 Repositioning Nutrition as Central to Development: A Strategy for Large-Scale Action, The World Bank, 2006
4 "World Population Prospects: The 2017 Revision". ESA.UN.org (custom data acquired via website). United Nations Department of Economic
and Social Affairs, Population Division. Retrieved 10 September 2017
Borno
Figure 1 showing map of Nigeria with Borno state
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According to the humanitarian response plan of 2020, the total population of Borno state is 4.1 million of
which 1.8 million are classified as the host community, 1.5 million as internally displaced people (IDP), 0.5
million people as returnees, and 0.8 million people as inaccessible.5 The report further revealed that in
2020, 0.8 million children under 5 suffering from malnutrition, 310,000 people living in open without
shelter and about half a million are living in shelters among the host communities are in need of
humanitarian assistance in northeast Nigeria as a result of a crisis that is crossed tenth year. The crisis,
characterized by massive and widespread abuse against civilians including killings, rape and other sexual
violence, abduction, child recruitment, burning of homes, pillaging, forced displacement, arbitrary
detention, and the use of explosive hazards, including in deliberate attacks on civilian targets.
Prior to the current crisis, the majority of the population were engaged in agriculture. The major crops
cultivated include onion, maize, millet, cowpea, while livestock kept include cattle, sheep, goat etc.
After the initiation of the crisis, the whole state was suffering from acute food scarcity as the majority of
the population has been relocated to IDPs camps or near the host communities living under the army
trenches. Therefore, the scope of agriculture to mitigate the food scarcity has diminished completely.
1.2. Mobbar Local Government Area Mobbar LGA situated very close to the Niger country
border as the LGA is located in farthest towards the
northern part of the country. The administrative
headquarter of the LGA is Damasak. As per the 2016
projections, the population of Mobbar LGA is 163,9006
and the LGA spread across 2790 Km2 area. The majority
of the population is between 15-64 years i.e. 51.1%7.
However, the major part of the LGA is inaccessible due
to ongoing insurgencies and unrest since the past few
years.
1.2.1 Damasak Town Damasak is a head town of the Mobbar LGA. This town
is comprised of two wards i.e. Damasak Central and
Zanna Umarti wards. It is located near the confluence of
the Yobe river and Komadagu Gana river on the juncture
of border of Niger. This town is covered by the military
through the trenches since it is considered as one of the
Army super camps. There are two primary roads to reach Damasak, one heads south to Gubio and
Maiduguri and the other towards east reaching Kukawa and Baga8. Apart from this town, the whole LGA
is inaccessible due to the constant attacks of NSAGs. According to the Rapid assessment done in 2018 in
Damasak, Farm land in parameters of 2-3 Kms is not considered safe due to heavy presence of NSAGs9.
5 Humanitarian Response Plan for 2020, Humanitarian Program cycle 2020, UN-OCHA
6 https://en.wikipedia.org/wiki/Mobbar 7 National population commission of Nigeria, http://www.citypopulation.de/php/nigeria-admin.php?adm2id=NGA008023 8 Damasak information: https://en.wikipedia.org/wiki/Damasak 9 Damasak/ Mobbar LGA – Rapid Assessment, WFP Report, 2017-18, https://docs.wfp.org/api/documents/WFP-
0000040176/download/?_ga=2.153708728.1823584999.1582727612-1762494381.1582727612
Figure 2 showing map of Damasak town with locations
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Same report also revealed that more than 60% population living in the Damasak are displaced from other
locations.
1.3. Humanitarian Assistance in Borno AAH has played a leading role in strengthening nutrition security in northern Nigeria over the last five
years, and it further scaled up our operations in 2016 following the Borno state government’s declaration
of a nutrition emergency. Working closely with partners, Action Against Hunger provided nutrition
support and treatment, food to displaced people and host families, distributed much-needed sanitation
and hygiene items and organized blanket supplementary feeding programs for children under five and
pregnant and lactating women. To address the current and projected issues families face in northeast
Nigeria, it employed a multi-sectoral approach to meet the rapidly growing humanitarian needs while
maintaining our commitment to improve nutrition security in the long term.
Through the constant coordination among the partner organizations, each partner is working in coalition
with other partners depending upon their objectives and goals to provide all possible support in the IDP
camps situated in the Mobbar LGA in order to minimize the gaps. The IDP camps are managed mainly by
IOM and other UN agencies with imminent support from the partners to provide best possible care in
different sectors like Nutrition, Health, WASH and Food, Security and Livelihoods (FSL). For Nutrition,
Health and FSL etc. UNICEF co-leads the nutrition, WASH, child protection and education sections in line
with country level multi-layer humanitarian response plan in order to reach 2.9 million displaced
population of the region10. The Medecines Sans Frontieres (MSF) provide healthcare support to these
population through permanent medical facility in Maiduguri along with mobile medical care whereas ICRC
also has a functional hospital which is equipped with expert surgeons, pediatrics and other specialist to
provide emergency care to the displaced population11.
Based on the latest humanitarian requirement document, an estimated 900,000 people remain out of
reach for humanitarians, but some areas became accessible in 2017. AAH prioritized aid for the most
vulnerable, commencing operations in six areas within Yobe and Borno and expanding programs in
Maiduguri and Monguno to assist newly displaced people and respond to a cholera outbreak.
In terms of Humanitarian situation of Damasak, according to UNHCR report, more than 100,000 people
have fled to Niger from Mobbar and closer area due to the threat of insurgencies12. Insurgency not only
affect the people to leave or fled their home but also affected the overall economy of the Damasak town.
Prior to insurgency, production of millets was higher than the consumption whereas now the local
production can only contribute 15-20% of the actual needs13. That has exponentially increase the price of
the food vis-a-vis increasing the poverty, malnutrition and food insecurity situation of the town.
Organizations like INTERSOS, UNICEF, IOM, FHI 360, NRC, Acted and Plan along with AAH are currently
working to mitigate the needs of the community. However, constant displacement has also lead the
confusion among partners to understand the magnitude of the needs for example population is key factor
affecting this confusion as the population of town according to the NIS is 90,000 whereas as per INTERSOS
10 Humanitarian Action Plan: UNICEF 2019-2021, UNICEF 11 https://www.msf.org/crisis-info-borno-and-yobe-states-august-2019 12 https://www.unhcr.org/news/latest/2014/11/5478554a6/deadly-boko-haram-attack-forces-3000-flee-niger.html 13 Ibid 9
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it is 63,000. Apart from AAH, very few organizations are working on nutrition related activities which is
also affecting the overall growth of the community members.
1.4. Nutrition and Health Context The Global Acute Malnutrition (GAM) prevalence is defined as children with very low weight for their
height with standard deviation of less than or equal to -2 with or without presence of edema. Severe acute
malnutrition (SAM) is defined as less than -3 standard deviation of WHZ score. The prevalence of GAM for
Borno state was 10.6% (8.1% - 13.7%; 95% CI) and SAM was 0.9% (0.3% - 2.3%; 95% CI) whereas with
MUAC GAM prevalence was 4.6% (3.0% - 7.2%) and SAM was 0.7% (0.3% - 1.9%)14. In terms of
Underweight, prevalence of Underweight was 27.2% (23.1% - 31.8%) and Severe Underweight (SUW) was
6.6% (4.8% - 9.0%)15. The stunting in the state was 37.3% (32.1% - 42.7%) and severe stunting was 9.3%
(7.1% - 12.2%) based on NNHS report of 2018.
The recent NFSS round eight survey was conducted in November 2019. The data from survey suggest that
prevalence of GAM by WHZ was 9.4% (6.7% - 12.9%) whereas SAM prevalence of 1.5% (0.6% - 3.3%) for
Mobbar and Nganzai combined. Whereas prevalence of GAM with MUAC was 3.4% (2.0% - 6.0%) and SAM
was 1.6% (0.7% - 3.6%). In addition, the prevalence of underweight was 13.5% (9.8% - 18.3%) and Severe
underweight was 1.2% (0.6% - 2.4%). Moreover, prevalence of stunting was 21.9% (17.8% - 26.7%) and
severe stunting is 6.9% (4.7% - 10.2%)16.
In Borno state, 86.0% of children received measles vaccine between the age group of 12-59 months. In
this state, about 94% children received ORS and Zinc or both among those who suffered from diarrhea in
the past two weeks of the data collection17. In terms of Northern Borno LGA, 75.5% children received
measles either by card or by recall as per the same report. Moreover, 6.1% of children suffered from
diarrhea in the past two weeks of the survey however, the data is not clear about the treatment part.
In terms of prevalence of acute malnutrition among women, prevalence of GAM was 12.2% (9.4% - 15.8%;
95% CI) which higher than the state average i.e. 10.3% (9.1% - 11.3%; 95% CI) whereas for SAM among
women from Mobbar and Nganzai LGAs, the prevalence was 7.0% (4.9% - 9.9%; 95% CI)18. Whereas, 5.9%
(4.9% - 7.1%; 95% ci) surveyed women were severely malnourished in Borno state19.
As far as IYCF is concerned, in Borno State, 23% of children were exclusively breastfed as per the NNHS
2018 whereas as per recent NFSS (round eight), 45.6% of children breastfed exclusively and 46.4% for
Mobbar and Nganzai LGA. In this region, the prevalence of minimal diet diversity and minimal meal
frequency was about 7% and 8.6% respectively. The survey will be conducted during the post harvesting
season (dry season), that is also known as good season since the food stocks have been restocked post
harvesting. Therefore, it will be ideal time to assess the situation to understand the impact on the nutrition
situation of the LGA.
14 National Nutrition and Health Survey 2018: Report on the Nutrition and Health Situation of Nigeria, June 2018, National Bureau of Statistics,
Nigeria, pg.- 31-35 15 ibid 10 16 Nutrition and Food Security Surveillance: North east Nigeria – Emergency survey, Round 8, UNICEF, November 2019 (unpublished source) 17 Ibid 14 18 Ibid 14
19 Ibid 14
15
Figure 3 showing the seasonal calendar of North Nigeria (Source: FEWS NET 2019)
In conclusion, two different assessment at different time points cumulatively suggests that the prevalence
of GAM (WHZ) fell under the ‘Serious’ threshold of WHO20. Also, the previous survey suggests that
indicators such as morbidity, immunization coverage and IYCF have also projected an alarming picture.
Moreover, all the past surveys were conducted merging Mobbar, Gubio, Kukawa and Nganzai LGAs in one
survey and indicators such as FSL, Food Consumption Score, coping strategy index and hygiene practices
were not part of the previous surveys. Moreover, apart from Damasak town, the rest of the Mobbar LGA
has been inaccessible. Therefore, to reduce the knowledge gap and to understand the data of Damasak
town only, it is necessary to conduct the SMART survey in this part of the LGA.
1.5. Objectives of the Survey
1.5.1. General Objectives To assess the current prevalence of acute malnutrition among 6-59 months old and mortality among the
general population and children 6-59 months in Damasak town, Mobbar LGA, Borno State, Nigeria.
1.5.2. Specific Objectives ● To estimate the prevalence of acute malnutrition (wasting and Oedema) among children aged 6-
59 months,
● Determine the prevalence of chronic malnutrition and underweight among children 0 to 59
months of age
● To estimate the coverage of measles vaccinations, vitamin A supplementation and health seeking
behavior among caretakers of children aged 6-59 months,
● Assess the prevalence of diarrhoea and use of ORS and zinc among children under-five years two
weeks preceding the survey
● To retrospectively estimate the levels of crude mortality rates and under five mortality rates in a
specific time period (134 days),
● To assess the maternal malnutrition among the mothers of children surveyed.
● To determine morbidity (fever and cough), use of mosquito net and Infant and young child feeding
practices (IYCF) indicators in the survey area for the age group of 0-23 months children.
● To determine water, sanitation and hygiene practices of the survey population.
● To assess the current food consumption score and coping strategy situation of the surveyed
population.
20 Onis MD, Borghi E, Arimond M et al., Prevalence thresholds for wasting, underweight and stunting in children under 5 years, Public Health
Nutrition: doi:10.1017/S1368980018002434, pg. 1-5
16
2. Methodology
This nutrition survey was conducted using the SMART (Standardized Monitoring and Assessment of Relief
and Transition) methodology. The standardized procedures and recommendations were provided in order
to collect timely and reliable data from the field.
2.1. Study Design The survey conducted using cross sectional quantitative methodology in the whole Damasak town area
of Borno state. The Damasak town comprised of two wards i.e. Damasak central and Zanna Umarti. The
cross-sectional nature of the study allowed us to assess the point prevalence of malnutrition and other
indicators with the highest precision.
2.2. Target population The target population for the anthropometric survey were children and their mothers among the sampled
households. The target group for the Infant and Young Child Feeding (IYCF) survey were the children
between 0 and 23 months of age in the selected households. To assess the coverage of measles vaccine
and vitamin-A supplementation, children aged between 12-59 months were selected from the sampled
households. To capture the prevalence of diarrhea, fever and cough, all children from the selected HHs
were included. Also for mortality surveys, all members from the sampled household were captured.
2.3. Sampling methodology Due to the big size of the population of interest, the Nutrition Survey used a two-stage random sample.
In the first stage, sample of clusters (villages or camps) was drawn from the official list of the Immunization
office of Mobbar. Clusters were selected using the PPS (Probability Proportional to Size) method as the
population largely differs in these clusters. ENA 2011 (July 9, 2015 version) was used for selecting clusters.
The second stage of sampling used a simple random selection of households depending on the availability
of information within the cluster. If available, the team leader was responsible to use a complete and
updated list of all households in the Cluster (here villages) and then a random number table or random
number generator application was used to randomly select the households to be included in the survey.
If no list is provided and the households are arranged in rows, then systematic random sampling was used
for household selection. Sample size was calculated for Anthropometric indicators.
2.4. Sample Size Calculation The sample size for the nutrition survey was calculated using the ENA software. The following assumptions
based on the given context were used to obtain the number of children to survey.
17
2.4.1. Sample size estimation of Acute Malnutrition Table 1 Sample size estimation of Acute Malnutrition
Parameters Value Assumptions
Estimated Prevalence of GAM (%) 14% As per previous NFSS (round 8) conducted in Mobbar and Nganzai, GAM prevalence was 9.4% (6.7%-12.9%). The estimated prevalence was considered as 14% as it is highest level of the confidence interval.
± Desired precision 4% A precision of ± 4% was chosen as per the calculation of precision from last round of NFSS (round 7).
Design Effect 1.5 The standard DEFF is 1.5 as per the SMART survey thumb rule
Population 52,349 Since the survey was done in small area, the population of these two wards also considered for SS calculation
Children to be included for Anthropometric measurements
422 Based on the formula above done in ENA
The SMART Methodology recommends converting the number of children into number of households
(fixed household method) for the numerous reasons:
1. It is easier to create lists of households than lists of children in the field.
2. Sample size calculated in number of children can encourage teams to skip households without
any children (thus introducing a bias for household-level indicators).
3. Households can provide a common metric for comparing sample size of many indicators.
In order to do the conversion of number of children to sample into number of households, the following
assumptions considered.
Parameters Value Assumptions
Average HH Size 4.4 As per the NFSS round 7, HH size was 4.4
% Children under-5 18.8% Based on NFSS round 8, 18.8% children are under 5 among population.
% Non-response Households 3%
The percentage of non-response chosen was relatively moderate because the target population living in the close community but with moderate migration.
Households to be included for Anthropometric measurements (according to ENA)
588 households
2.4.2. Sample size calculation for Mortality: Table 2 Sample size calculation for Mortality assessment in the SMART survey on Damasak Town
Parameters Value Assumptions
Estimated Death Rate /10,000/day 0.3 As per the recent NFSS (round 8) survey conducted in November 2019 in Mobbar and Nganzai.
± Desired precision ± 0.3 For Mortality indicator, precision was chosen ± 0.3 as this is a standard precision for the mortality of >1/ 10000/ day
Design Effect 1.5 As per the NFSS round 8 results the DEFF was 1.75
*Recall period 134 From the 29th October 2019 (prophet`s birthday) to 11th March, 2020 (middle of the data collection).
Average HH Size 4.4 As per NFSS survey the HH size was 4.4 is HH size
18
Non-response rate 3% The percentage of non-response chosen is 3% because the target population living in the close community and does not migrate for longer period.
Sample to be included for Mortality 1515 Based on the calculations by ENA
HH to cover for mortality 355
2.5. Final Sampling Strategy The sample size for anthropometry is 422 children and 588 households whereas for mortality it is 1515
individuals and 355 households. Since the anthropometry sample was higher, all indicators were captured
from the 588 HHs from 45 clusters (please see calculation in section 2.11).
2.6. Cluster Selection Using the ENA software, the 45 Clusters were drawn from the sampling frame of the sites of Damasak
central and Zanna Umarti wards shared by the Immunization office of Borno. Clusters were selected using
the PPS (Probability Proportional to size) method. The random selection of the clusters was only done
once.
2.7. Household Selection Techniques If selected cluster captured village/camp area has less than 150 HHs then all households were enumerated
to make the household selection more feasible for survey teams without introducing selection bias. There
are some settlement/camps, which have geographically large area thus segmentation method was
introduced. For the selection of second stage sampling method, team leaders were equipped with the
necessary information which were explained during training sessions.
For the selection of households, based on number of households in each cluster following method were
used.
Segmentation method: If cluster has more than 250 households then cluster were segmented using
geographic landmarks either man made (like schools, temples, special buildings etc. ) or natural (river,
mountains, farms, etc.). The PPS method was introduced for selection of clusters from the segments. Once
the segments were selected, the enumeration of the households were conducted. Based on the condition
either Simple Random Sampling or Systematic Random Sampling method was used for the selection of
Households (13 HH in each clusters).
Systematic Random Sampling: If the cluster has household between 150 to 250 then a systematic random
sampling method was used. Based on the enumerations, the total HH no. was captured and k was
calculated.
The formula for k is = Total no of HH in the cluster/ No of HH required from each clusters i.e. 13 HH
The sampling interval (k) was determined by dividing the total number of households in the zone by
number of samples required. The first household was the household with the number chosen randomly
between 1 and the sampling interval (e.g. if the sampling interval is 11.7, a number between 1 and 11 was
chosen). Adding the sampling interval (11.7) to the number of the first household chosen randomly,
rounded to the nearest whole number, the number of the second household for the survey was found. At
19
the cumulative number obtained, again the sampling interval was added, the third household was chosen.
This method was used until the end of the cluster.
Simple Random Sampling: In cluster is having less than 150 households the enumeration of all households
was done by the field team. Then with the help of a random table or random number generator
application, households were selected for data collection.
2.8. Survey Teams Six teams will be engaged in data collection and each team had three members. Two team members (one
female and one male) were responsible for measuring children and their mothers and also take care of
the data recording. Other team member was responsible for rapport building at village level and
administration of questionnaires (IYCF, WASH and FSL etc.) in the local dialect.
The four supervisors and SMART survey manager along with Assistant SMART survey Manager, were
monitoring the data collection along with representatives from M&E team and ECHO sector manager from
AAH if any. Survey manager and a supervisor were responsible for daily data entry into ENA software, to
ensure a high level of data quality collected by the teams.
2.9. Survey equipment Anthropometric measurements were taken on children 6-59 months were height/ length (to the nearest
1 mm) using a standard wooden infant cum Stadiometer , weight (to the nearest 100 g) using an electronic
weighing scale and Mid Upper Arm Circumference were measured on the left arm of children using a child
MUAC tape.
For maternal anthropometry, carpenter`s tape and SECA electronic weighing scales were used, while for
MUAC, adult MUAC tapes were used.
Weight-for-height z-score was determined by using the WHO Weight-for-Height tables for both gender.
For age, immunization card or birth proofs were checked as a priority. In the case of the absence of official
documentation about birth date or if the mother doesn’t remember the exact birth-date of her child, age
was estimated using a local event calendar.
2.10. Key Variables In Anthropometry, variables such as age, gender, weight, height, MUAC and edema were measured among the children under five years old during the survey. Along with these indicators such as illness, vaccination (measles and vitamin A), treatment approaches, use of mosquito nets, WASH, food consumption scores, coping index, maternal MUAC, breastfeeding, complementary feeding, diet diversity and dietary frequency etc. were also be collected.
2.11. Daily field procedure The number of households to be completed per day can be determined according to the time the team
could spend on the field excluding transportation, other procedures and break times. The details below
were taken into consideration when performing this calculation based on the given context.
1. Departure from office at 9.00 am and back at 4.30 pm = 450 minutes
2. Two way travel time to reach a cluster: 30 min.
3. Duration for introduction and selection of households: 60 min.
20
4. Time spent to move from one household to the next: 5 min.
5. Average time in the household: 15 min.
6. Breaks: 2 breaks of 15 min each and one lunch break for 45 mins.
7. Revisit time period: 15 min will be kept for revisiting the household if the house members were
absent at the time actual data collection
This estimation of 4 hours and 30 minutes on the field and 20 minutes per households has led to the
conclusion of having 13 HH per day per team (270 minutes/20 minutes = 13.5 ~ 13 HH).
The 588 households in the sample was then divided by the number of households to be completed in one
day, to get the number of clusters to be included in the survey.
588 HHs/ 13 households per day in each cluster = 45 Clusters
Since, this survey will be done with 5 survey teams, therefore to cover 45 clusters with five teams needed
45/5 = 9 days.
2.12. Data collection and Supervision Mobile tablet equipped with either ODK or Kobo collect were used to collect data in the field. The
questionnaires was developed in ODK and hosted on the ONA (Ona.io). The data was automatically receive
at central server using internet connection. Once, the data were received, daily quality checks conducted
and shared with the team in Damasak. Along with digital data collection, considering the previous
experiences paper based data collection for anthropometry and mortality was conducted to ensure the
backup of the data.
The supervisor is in overall in charge of a group. A group consists of two teams that covered on average
18 clusters. He/she was responsible for the daily organization and supervision of the team’s work. He/she
assigned work to the team members, responsible for logistic arrangements and where possible also
helped the team in locating accommodation. Additionally, he/she was also responsible for checking the
quality of the interview by observing the interview and anthropometric measurements in the field.
2.13. Training of Enumerators A two days of theoretical training and the additional day was for piloting the survey. The first day was
dedicated for theoretical training, the second day for explaining the questionnaires and in house practice
on weight and height measurement, the third day was reserved for pilot testing. The standardization test
was not conducted since the main measurers were used to conduct data collection passed the test during
previous SMART surveys. One-day pilot was conducted to understand the quality of the questionnaire and
understanding of enumerators regarding the enumerations, sampling and data collection skills.
2.14. Data Collection Schedule The data collection took place between 5th and 16th March 2020. Based on the population data availability,
the detailed schedule is prepared. (Please see section 13.4)
2.15. Data analysis and interpretation The primary analysis needed approximately 4 days following the completion of data collection. A brief
summary report of the survey was prepared within 3 days following the completion of data collection.
21
The nutrition results were presented using the standard format. The standard SMART flags were
considered wherever necessary in the analysis of child anthropometric data to exclude extreme values
that result likely from incorrect measurements. To estimate the malnutrition prevalence, WHO 2006
growth references and other standards for additional indicators were used. The anthropometry and
mortality indicators were analyzed using ENA for SMART application (July 9, 2015 version) and additional
indicators were analyzed using Epi-info (version 3.5.4) and SPSS (version 20).
2.16. Reserve Clusters: In the case that several of the selected clusters could not be surveyed due to insecurity, accessibility, or refusal, the ENA software had automatically selected Reserve Clusters at the planning stage. 10% of the selected clusters + 1 had been pre-selected by the software. All of these Reserve Clusters can be used if fulfilled one of the following condition,
1. 10% or more of the selected clusters cannot be surveyed 2. If less than 80% HHs than required sampled were achieved during the survey 3. If less than 90% of children were measured than desired during the survey
In this survey, no reserved clusters were used since the survey reached more than 100% of the expected sample size for the children. Although, one cluster was not reached during the survey due to insecurity but since it does not affect the reach hence the no reserved cluster was used. Details of the reserved cluster in also mentioned in section 13.4.
2.17. Ethical clearance It is very important to maintain the dignity of the respondents during the survey. At the time of data
collection, the verbal administration of informed consent was taken. The personal identifiers were kept
anonymous by using certain codes. The consent was enclosed with the following points:
1. Introduction of surveyor and information about the organization.
2. Brief information on the survey.
3. Assurance of confidentiality.
4. Empower respondents so as to draw back their participation at any point of data collection.
5. The identified SAM children will be referred to the closest CMAM clinic by sharing referral slip with
caretakers and program team of AAH.
22
4. Results of Child Nutrition
4.1 Survey Achievements Table 3 showing survey targets and achievements in the SMART survey
Indicators Target Sample Achieved sample Percentage Coverage
Households 588 588 100%
Children 422 429 101.7%
Mortality population 1515 2678 176.8%
The data collection was done among 45 clusters that were drawn using the PPS method. One cluster from the survey were inaccessible during the data collection phase due to insecurity. Also, 100% of households were reached for data collection, some households cannot be reached in few clusters due to time restrictions and some were absent at the time of data collection. To mitigate this issue, one team was reserved on the last day to reach these households. The team managed to retrieve the data from about 8 households from 5 different clusters.
4.2 Anthropometric results (based on WHO standards 2006) In total 429 children between 6-59 months were measured for height/ length, weight, and MUAC and checked for Oedema. Since this is an LGA level survey, SMART flags were used for analysis instead of WHO flags. Out of 429, no children were excluded from Weight-for Height (WHZ). The nutritional analysis for Height-for-age (HAZ) was based on 428 children as one child was excluded as they were flagged as SMART outliers. Regarding weight-for-age (WAZ) analysis, no children were flagged as a SMART outlier therefore 429 children were analyzed to estimate the prevalence of underweight in this LGA. For MUAC, data of 429 children were analyzed as SMART flags do not apply on MUAC and data of one child was missing. Out of 429 children, 224 boys and 205 girls comprised the sample for the anthropometric survey. Among those 119 and 113 were boys and girls respectively each age group (6-29 months), and 105 boys and 92 girls were age group of 30-59 months. Statistical tests (using Chi-square test) indicate that boys and girls were almost equally represented within the survey population (the ratio was 1.1:1) and a random distribution was detected (p-value= 0.359).
Table 4 Distribution of age and sex of children surveyed
Boys Girls Total Ratio
AGE (mo) no. % no. % no. % Boy: girl
6-17 76 51.7 71 48.3 147 34.3 1.1
18-29 43 50.6 42 49.4 85 19.8 1.0
30-41 67 51.1 64 48.9 131 30.5 1.0
42-53 32 59.3 22 40.7 54 12.6 1.5
54-59 6 50.0 6 50.0 12 2.8 1.0
Total 224 52.2 205 47.8 429 100.0 1.1
23
Prevalence of Acute Malnutrition by Weight for Height Z-score Wasting or acute malnutrition is the
condition represented by the measure of
thinness or bilateral edema. It represents
child’s failure to receive adequate food or a
recent episode of illness triggering loss of
weight and the onset of malnutrition.
Figure 4 shows that the survey distribution
of Weight-for-Height (in red) curve against
Gaussian distribution (in green). The mean
of Weight-for-Height in Z-score was -0.61
with a Standard Deviation (SD) of ±0.95.
Standard Deviation of ±0.95 suggests that
data is of good quality since it is ranging
between 0.8 and 1.2 as per the plausibility
test guidelines.
Furthermore, the curve of the survey population (in red) shifted to the left of the curve of the
reference population (in green), indicating that the surveyed population has more malnourished
children than the reference population. The survey curve is slightly shorter than the reference one;
however, the kurtosis level in plausibility is within the range therefore it does not raise any concern
related to data quality.
Table 5 Prevalence of acute malnutrition based on WFH z-scores and by gender
All
n = 429
Boys
n = 224
Girls
n = 205
Prevalence of global malnutrition
(<-2 z-score and/or edema)
(28) 6.5 %
(4.5 – 9.5 95%
C.I.)
(21) 9.4 %
(5.8 – 14.8 95%
C.I.)
(7) 3.4 %
(1.7 – 6.8 95% C.I.)
Prevalence of moderate malnutrition
(<-2 z-score and >=-3 z-score, no edema)
(24) 5.6 %
(3.7 – 8.3 95%
C.I.)
(19) 8.5 %
(5.3 – 13.3 95%
C.I.)
(5) 2.4 %
(1.0 – 5.7 95% C.I.)
Prevalence of severe malnutrition
(<-3 z-score and/or edema)
(4) 0.9 %
(0.4 – 2.4 95%
C.I.)
(2) 0.9 %
(0.2 – 3.5 95% C.I.)
(2) 1.0 %
(0.2 – 3.8 95% C.I.)
The prevalence of GAM based on WHZ criteria is more than two fold higher among boys (9.4%) as
compared to girls (3.4%), also this difference is statistically significant (p-value= 0.018).
Table 7 below shows the prevalence of acute malnutrition based on WHZ for different age groups. The
prevalence of GAM is high among younger children (7.76%) of 6-29 months when compared with the
older children (5.08%) of 30-59 months. Also, the prevalence of SAM is more than two folds lower in the
elder age group (0.51%) of 30-59 months as compared to the younger age group (1.29%) of 6-29 months.
Figure 4 showing data distribution as compare to WHO graph
24
Table 6 showing the Prevalence of acute malnutrition by age, based on WFH z-scores.
Severe wasting
(<-3 z-score)
Moderate wasting
(>= -3 and <-2 z-score )
Normal
(> = -2 z score)
Edema
Age (mo) Total no. No. % No. % No. % No. %
6-17 147 3 2.0 10 6.8 134 91.2 0 0.0
18-29 85 0 0.0 5 5.9 80 94.1 0 0.0
30-41 131 0 0.0 5 3.8 126 96.2 0 0.0
42-53 54 1 1.9 4 7.4 49 90.7 0 0.0
54-59 12 0 0.0 0 0.0 12 100.0 0 0.0
Total 429 4 0.9 24 5.6 401 93.5 0 0.0
Prevalence of Acute Malnutrition by Mid-Upper Arm Circumference The overall prevalence of Global Acute Malnutrition (GAM) and Severe Acute Malnutrition (SAM) based
on MUAC was 1.6% (0.8% – 3.3% 95% C.I.) and 0.5% (0.1% - 1.9% 95% C.I.) respectively. When data
segregated by gender demonstrates that, the prevalence of GAM was higher among boys (2.2%) as
compared to girls (1.0%). The relationship was statistically insignificant (p value=0.307). SAM prevalence
was similar among girls (0.5%) than boys (0.4%).
Table 7 Prevalence of acute malnutrition based on MUAC cut offs (and/or edema) and by gender
All
n = 429
Boys
n = 224
Girls
n = 205
Prevalence of global malnutrition
(< 125 mm and/or edema)
(7) 1.6 %
(0.8 – 3.3 95%
C.I.)
(5) 2.2 %
(1.0 – 5.2 95%
C.I.)
(2) 1.0 %
(0.2 – 3.8 95%
C.I.)
Prevalence of moderate malnutrition
(< 125 mm and >= 115 mm, no edema)
(5) 1.2 %
(0.5 – 2.7 95%
C.I.)
(4) 1.8 %
(0.1 – 4.6 95%
C.I.)
(1) 0.5 %
(1.5 – 7.4 95%
C.I.)
Prevalence of severe malnutrition
(< 115 mm and/or edema)
(2) 0.5 %
(0.1 – 1.9 95%
C.I.)
(1) 0.4 %
(0.1 – 3.3 95%
C.I.)
(1) 0.5 %
(0.1 – 3.4 95%
C.I.)
The analysis of the prevalence of acute malnutrition age-wise shows GAM is very high among younger
children of 6-29 months (2.59%) as compared to older children of 30-59 months (0.51%).
The combined analysis of table 9 and 10 may lead the following conclusions; 1. MUAC is more sensitive towards detecting cases among females than males 2. Children belong to the younger age group (6 to 29 months) are majorly identified by MUAC as
compared to older age group (30 to 59 months) 3. Such a vast disparity in the prevalence of GAM and SAM using two different criteria like WHZ and
MUAC demonstrates that using MUAC as only criteria for identification of SAM and GAM in the community would mislead the actual caseload of the acute malnutrition cases in the community.
25
Table 8 Prevalence of acute malnutrition by age, based on MUAC cut offs and/or edema
Severe wasting
(< 115 mm)
Moderate wasting
(>= 115 mm and <
125 mm)
Normal
(> = 125 mm )
Edema
Age
(mo.)
Total
no.
No. % No. % No. % No. %
6-17 147 2 1.4 2 1.4 143 97.3 0 0.0
18-29 85 0 0.0 2 2.4 83 97.6 0 0.0
30-41 131 0 0.0 1 0.8 130 99.2 0 0.0
42-53 54 0 0.0 0 0.0 54 100.0 0 0.0
54-59 12 0 0.0 0 0.0 12 100.0 0 0.0
Total 429 2 0.5 5 1.2 422 98.4 0 0.0
4.2.3 Comparison of Acute Malnutrition by WHZ and MUAC Combining the cases based on only WHZ (<-2SD) only, only MUAC (<125 mm) and cases that fall in both
the categories is best way to understand actual caseload in any community as many cases could only be
identified by one criterion and vice-versa. The combined prevalence of GAM based on WHZ and/or MUAC
was 7.3% and combined prevalence of SAM with WHZ and/or MUAC was 1.4%.
Table 9 showing the prevalence of GAM and SAM.
Criteria GAM Prevalence
(95% CI)
Number
of cases
SAM Prevalence
(95% CI)
Number
of cases
WHZ as only criteria21 5.6% (3.7% - 8.3%) 25 0.9% (0.3% - 2.5%) 4
MUAC as only criteria22 0.9% (0.3% - 2.5%) 4 0.5% (0.1% - 1.9%) 2
Both criteria (children found
GAM with both MUAC and
weight for height)
0.7% (0.2% - 2.2%) 3 0% 0
Combined prevalence
(Children found GAM with
weight for height only,
MUAC only and combined)
7.5% (5.2% - 10.5%) 32 1.4% (0.6% - 3.2%) 6
21 This indicator excludes all children who were identified as SAM/GAM by both indicators 22 This indicator excludes all children who were identified as SAM/GAM by both indicators
26
Table 11 (above) suggests that the prevalence
of GAM as per only WHZ criteria was 5.6%
(excluding the cases that were also identified
as GAM by MUAC) while with only MUAC it
was 0.9%.
The prevalence of GAM with both criteria was
found to be 0.7%. If we sum all the cases
together then we get a total GAM of 7.5%.GAM
and SAM prevalence calculated by MUAC was
much lower than the prevalence obtained from
Weight-for-Height z-scores. A meta-analysis of
multi-country data conducted by Prof. Michael
Golden also shows that In Nigeria, only one-third of the cases could be identified by both criteria23.
The current survey it was observed that using WHZ only criteria would get 87.5% of the cases
whereas MUAC programs would only reach to 21.9% of the cases.(Please see figure 5 above).
4.3 Prevalence of Underweight Weight-for-age (WAZ) is a composite index of height-for-age and weight-for-height. It takes into account
both acute and chronic malnutrition. While underweight or weight-for-age is used for monitoring the
Millennium Development Goals, it is no longer in use for monitoring individual children as it cannot detect
children who are stunted but of normal weight; furthermore, it does not detect acute malnutrition that
threatens children’s lives.
About 14.7% (11.2% - 19.1%; 95% C.I.) of children in the surveyed population were underweight. Among
429 sampled children, 2.1% (1.0% - 4.2%, 95% CI) were severely underweight. When the prevalence of
underweight was compared by gender, table 11 demonstrates that the prevalence of underweight is
higher among boys than girls and the situation is same among severely underweight children.
Table 10 Prevalence of underweight based on WFA z-scores by gender.
All
n = 429
Boys
n = 224
Girls
n = 205
Prevalence of underweight
(<-2 z-score)
(63) 14.7 %
(11.2 – 19.1 95%
C.I.)
(37) 16.5 %
(11.9 – 22.4 95%
C.I.)
(26) 12.7 %
(8.5 – 18.5 95% C.I.)
Prevalence of moderate underweight
(<-2 z-score and >=-3 z-score)
(54) 12.6 %
(9.4 - 16.6 95% C.I.)
(29) 12.9 %
(9.1 - 18.0 95% C.I.)
(25) 12.2 %
(8.0 – 18.1 95% C.I.)
Prevalence of severe underweight
(<-3 z-score)
(9) 2.1 %
(1.0 - 4.2 95% C.I.)
(8) 3.6 %
(1.7 – 7.4 95% C.I.)
(1) 0.5 %
(0.1 – 3.6 95% C.I.)
23 Grellety E, and Golden M.; Weigh for height and mid-upper arm circumference should be used independently to diagnose acute malnutrition: Policy implications;
Bio Med Central Journal of Nutrition, 2016 vol. 2 (10) pg. 2 - 17
Figure 5 showing overlap of MUAC and WHZ in Damasak town
WHZ <-2 only
25 (78.1% )
MUAC
<125mm
only
4
(12.5%)
WHZ <-2
& MUAC
<125mm
3
(9.4%)
27
When data of underweight is further segregated by age shows that the younger children (14.65%) have
a slightly lower prevalence than older children (14.72%). The same analysis for severe underweight
depicts that prevalence of severe underweight among younger children (2.59%) was almost double than
older children (1.52%).
Table 11 Prevalence of underweight by age, based on WFA z-scores
Severe
underweight
(<-3 z-score)
Moderate underweight
(>=-3 and <-2 z-score )
Normal
(> = -2 z score)
Edema
Age
(mo.)
Total
no.
No. % No. % No. % No. %
6-17 147 4 2.7 16 10.9 127 86.4 0 0.0
18-29 85 2 2.4 12 14.1 71 83.5 0 0.0
30-41 131 1 0.8 19 14.5 111 84.7 0 0.0
42-53 54 1 1.9 5 9.3 48 88.9 0 0.0
54-59 12 1 8.3 2 16.7 9 75.0 0 0.0
Total 429 9 2.1 54 12.6 366 85.3 0 0.0
4.4 Prevalence of Chronic Malnutrition Stunting indicates chronic undernutrition during the most critical periods of growth and development. It
usually reflects cumulative and persistent effects of poor nutrition during pregnancy and the first two
years of life (the first 1000 days), diseases and other deficits that often span across several generations.
Stunting prevents children from reaching their full physical and mental potential as it impairs brain
development, which makes children less capable in school and reduces productivity as they grow into
adulthood.
The presented in table 13 shows that prevalence of stunting 26.9 % (21.3 – 33.3, 95% C.I.) and 3.7% (2.1%
- 6.5%; 95% CI) children were severely stunted. Also, the prevalence was observed higher among the
boys (29.9%) as compared to the girls (23.5%) however this difference was statistically insignificant (p
value= 0.242).
Table 12 Prevalence of stunting based on HFA z-scores and by gender
All
n = 428
Boys
n = 224
Girls
n = 204
Prevalence of stunting
(<-2 z-score)
(115) 26.9 %
(21.3 – 33.3 95% C.I.)
(67) 29.9 %
(22.5 – 38.6 95% C.I.)
(48) 23.5 %
(16.9 – 31.7 95%
C.I.)
Prevalence of moderate stunting
(<-2 z-score and >=-3 z-score)
(99) 23.1 %
(18.2 – 29.0 95% C.I.)
(58) 25.9 %
(19.3 – 33.8 95% C.I.)
(41) 20.1 %
(14.3 – 27.6 95%
C.I.)
Prevalence of severe stunting
(<-3 z-score)
(16) 3.7 %
(2.1 – 6.5 95% C.I.)
(9) 4.0 %
(2.1 – 7.6 95% C.I.)
(7) 3.4 %
(1.7 – 7.0 95% C.I.)
When data of stunting is further segregated by age shows that the younger children (28.1%) have a higher
prevalence than older children (25.4%). The same analysis for severe underweight depicts that
prevalence of severe underweight among younger children (4.76%) was almost doubled than older
children (2.53%).
28
Table 13 Prevalence of stunting by age based on HAZ
Severe stunting
(<-3 z-score)
Moderate stunting
(>= -3 and <-2 z-score )
Normal
(> = -2 z score)
Age (mo.) Total no. No. % No. % No. %
6-17 146 4 2.7 23 15.8 119 81.5
18-29 85 7 8.2 31 36.5 47 55.3
30-41 131 4 3.1 31 23.7 96 73.3
42-53 54 0 0.0 10 18.5 44 81.5
54-59 12 1 8.3 4 33.3 7 58.3
Total 428 16 3.7 99 23.1 313 73.1
The data of 428 children from 6 to 59 months of age was captured during the survey. As mentioned in
table 15 below, the mean of WAZ is more than -1.13 since more than one sixth (14.7%) of the sampled
children are identified as underweight. Since the Standard deviation of WHZ was within a range of 0.8 to
1.2, therefore, measurements can be appreciated for a good quality hence probability of over or
underestimation of prevalence could be minimal. However, SD of HAZ were slightly over one, this could
be due to lack of availability of date of birth (77% children has no DOB). This may have defarred the stadard
deviation above 1.0 but still under acceptable range (<1.2), the quality of anthropometry can still be
considered good.
The design effect calculated in this table is to estimate the ‘index of dispersion’ i.e. disparity among the
clusters w.r.t. cases identified. The design effect calculated in this table demonstrate randomness of cases
spread across all clusters and also predicts the probability of availability of malnutrition pockets in the
survey area. The design effect for WHZ was 1.05, HAZ with 1.93 and WAZ with 1.32, predicts that
possibility of having ‘malnutrition pockets’ present in the sampled clusters is very low.
Table 14 Mean z-scores, Design Effects and excluded subjects
Indicator n Mean z-scores
± SD
Design Effect (z-
score < -2)
z-scores not
available*
z-scores out of
range
Weight-for-Height 429 -0.61±0.95 1.05 0 0
Weight-for-Age 429 -1.13±0.88 1.32 0 0
Height-for-Age 428 -1.31±1.04 1.93 0 1
* contains for WHZ and WAZ the children with oedema.
4.5 Morbidity and Immunization:
Immunization: Immunization is a critical public health intervention that protects children from mortality and morbidity.
Nigerian government launched a fully-fledged program on Immunization in 1978. In this SMART survey,
data regarding coverage of measles and vitamin A was captured. The data was captured among children
between 9 to 59 months of age group.
In this survey, 85.8% children received measles vaccination among which the majority of the participants
has the EPI card along with them i.e. 59.7% (54.7% - 64.5%) with EPI card and 26.1% (21.9% - 30.8%) by
recall.
29
Vitamin A Supplementation Provision of vitamin A supplementation every 6 months can help protect a child from morbidity and
mortality associated with vitamin A deficiency. Improving the vitamin A status of deficient children
through supplementation enhances their resistance to disease and can significantly reduce mortality.
Vitamin a supplementation starts from 9 months after birth usually and its dosage every 6 months
continuously until the child reaches 5 years of age.
For this survey, children between 9-59 months were covered to estimate the coverage of vitamin-A
supplementation in the last 6 months from the survey date. A total of 402 children were covered. The
results showed that the majority of children i.e. 86.1% (82.3% - 89.3%) were supplemented with Vitamin-
A in the last six months based on recall of mothers.
The data also suggests that among all 402 children, 80.6% of children received both Measles and Vitamin
A supplementation whereas 8.5% of children didn`t receive any vaccine from the sample. Moreover,
5.2% of children each received only Vitamin A and only measles vaccine.
Use of mosquito net: Being malaria-endemic LGA, state government and various partners had a provision to supply treated
mosquito nets (LLITN) in all households. This survey aimed to capture the percentage of households with
actual utilization of the nets for sleeping among children.
For this particular indicator, children below 59 months (n=429) were selected. To assess the utilization
of mosquito nets we asked the primary care-givers, if their children slept under the mosquito nets, the
previous night. The finding indicates that majority of the children i.e. 99.5% (98.1% - 99.9%) below the
age 5 years slept under the mosquito net.
Coverage of deworming tablets: Deworming is part of Nigeria`s one of the largest child survival interventions. Under this program, all
children above 1 year of age receive one tablet/ syrup of anthelminthic drug at least once every six
months. As the presence of helminths can also deteriorate nutrition status, therefore it was also included
in the survey. As per the survey data, 67.0% (61.9% - 71.8%) of children between one to five years (n=
364) of age received at least one deworming tablet in the past six months to the survey.
Morbidity and treatment In terms of morbidity, slightly less than one-quarters of the children i.e. 23.4% (19.7% - 27.9%) were
suffered one or more illnesses in the past 14 days prior to data collection. Among all children who
reported illness (101), 38.6% of children suffered fever, 32.7% had diarrhea and 38.6% had a cough in the
past two weeks.
30
Figure 6 showing illness reported among children under 5 years in Damasak town
Among these 101 children, the majority of the caretakers (40.6%) preferred visiting mobile clinic for
medical ailments. About 34.7% children received treatment through PHCs whereas 5.9% caretakers of ill
children, did not visited any facility. Among other illness, eye infection is quite common among the
children as 10.9% children recorded with the same.
5. Mortality rate: In total, 582 households were interviewed, representing 2,649 people with 343 households with children under five years. As per the analysis, 18.4% of children in the survey area are under five years of age. Among these 582 surveyed households, 58.93% (343) HHs had at least one child less than five years of age. The average household size is 4.6 people per house. The birth rate of the sampled population was 1.24 per 10000 people per day i.e. six children are born every five days per 10000 population of the survey area. 0.06/10000/day is an in-migration rate whereas 0.23/10000/day is an out-migration rate for the survey population. Based on the population data of the town, around 9.42 people join the community in every month whereas 36.12 people leave the area in every month from the whole Damasak town24. One third (32.6%) of the population is in the reproductive age group (391 males and 473 females) of 18 to 49 years. Table 15 Death rates of Damasak town
Total No. of Deaths Death rate Design effect
Crude mortality rate 2649 10 0.28 (0.12-0.67) (95% CI) 1.85
Under 5 mortality
rate
461 2 0.32 (0.08-1.32) (95% CI) 1.00
The crude death rate and U5MR were 0.28 and 0.32 per 10000 per day respectively. However, as per IPC classification cut-offs, these figures are lower than the emergency threshold. About 50.0% of death caused due to any illness. Unknown causes contributed to 40%. The majority of deaths (60%) occurred while the deceased at the current location (home) and 10% in place of last residence.
24 As per the population of Damasak town is 52,349 which is being used here to calculate the in and out migration per 10000 population per day by using basic
calculation i.e. division and multiplication.
38.60% 38.60%32.70%
16.80%
0%
10%
20%
30%
40%
50%
Fever Cough Diarrhea Other
History of Illness in past two weeks
Illness
31
6. Maternal Malnutrition The direct relation between maternal nutritional status and child nutritional status has been widely documented. Under-nutrition in women of reproductive age is a significant risk factor for mortality and morbidity in both children and mothers. Underweight status in women has been associated with poor reproductive performance, poor pregnancy outcome and more specifically with intrauterine growth restriction (IUGR)25, and low-birth-weight (LBW)26, and increased risk of maternal deaths. In this survey, not only mothers or pregnant women were assessed but also women between the age group of 18 to 49 years irrespective of marital status were measured for weight, height and MUAC. The BMI and MUAC was further calculated to assess the malnutrition status of the women in this LGA. For BMI, pregnant women were excluded from measurement. MUAC was measured using adult MUAC tapes, the SECA scale was used to measure weight and height was measured using carpenter`s tape due to unavailability of proper adult height boards. Therefore information related to BMI needs to be studied with caution. In this survey, total 471 women were measured for MUAC and BMI (Pregnant women excluded from BMI
measurement). Among this women, 85.4% (n= 402) were mothers (including pregnant women as well)
and 14.6% (n=69) were women from age group of 18-49 years. Both groups were assessed separately in
the table below. BMI measurement was conducted on 357 women since 24.2% women from the survey
was pregnant during data collection period. All women were measured for MUAC since it is independent
from the status of pregnancy.
6.1 Maternal Malnutrition with MUAC Table 16 showing the prevalence of malnutrition among women with MUAC
Indicators Percentage% (n) 95% CI
Mothers of the
children surveyed
(n= 357)
Severely malnourished women
(<19 cm)
0.0% (0) (0.0% - 1.2%)
Moderately malnourished women
(19-23 cm)
11.7%(47) (8.8% - 15.3%)
Normal women (>23 cm) 88.3% (355) (84.8% - 91.3%)
Women 18-49
years group
(Mothers
excluded) (n = 69)
Severely malnourished women
(<19 cm)
2.9% (2) (0.4% - 10.1%)
Moderately malnourished women
(19-23 cm)
15.9% (11) (8.2% - 26.7%)
Normal women (>23 cm) 81.2% (56) (69.9% - 89.6%)
Acute malnutrition was also assessed using adult MUAC tapes. To estimate malnutrition in women MUAC cut-off of < 230 mm was used. The overall global acute malnutrition among mothers was 11.7%. However no severe malnutrition cases among mothers (MUAC < 19cm) were identified. Among women between age group of 18-49 years (excluding mothers), moderate malnutrition is 10.3% whereas severe malnutrition is 2.9%. The data suggests that the mothers have a better nutrition profile that the rest of the women from that age group.
25Intrauterine growth retardation (IUGR) refers to a rate of growth of a fetus that is less than normal for the growth potential of a fetus, for that particular gestational
age 26Low birth weight (LBW) has been defined as weight at birth of less than 2,500 grams
32
6.2 Maternal Malnutrition (BMI) Body Mass Index is calculated using the formula of weight (kg)/ height (M)2. The mothers and women between age group of 18 to 49 years were included in the survey. The women who were pregnant at the time of data collection were excluded from these measurements. Table 17 showing prevalence of underweight as per BMI in Damasak town
Indicators Percentage% (n) 95% CI
Mothers of the
children surveyed
(n= 288)
Severely underweight women
(BMI <16 )
1.4% (4) (0.4% - 3.5%)
Moderately underweight women
(BMI 16- 18.5)
18.4%(53) (14.1% - 23.4%)
Normal women (BMI 18.5 - 25) 58.0% (167) (52.1% - 63.8%)
Overweight Women (BMI >25) 22.2% (64) (17.6% - 27.5%)
Women 18-49
years group
(Mother
excluded) (n = 69)
Severely underweight women
(BMI <16 )
4.3% (3) (0.9% - 12.2%)
Moderately underweight women
(BMI 16- 18.5)
13.0% (9) (6.1% - 23.3%)
Normal women (BMI 18.5 - 25) 66.7% (46) (54.3% - 77.6%)
Overweight Women (BMI >25) 15.9% (11) (8.2% - 26.7%)
Among the mothers of surveyed children, the prevalence of severe underweight is 1.4% whereas about 22.2% mothers were overweight. About 18.4% women has underweight whereas more than half (58%) are normal. Among women between 18 to 49 years of age, the prevalence of severe underweight is about 4.3% whereas overall 13.0% women were underweight in this survey. About 15.9% women were overweight.
7. Infant and Young feeding practices Among all proven preventive health and nutrition interventions, IYCF has one of the great potential to
impact on child survival especially at young age. Optimal IYCF which includes initiation of breastfeeding
within an hour after birth, exclusive breastfeeding for first six months, introduction and continued
complementary feeding from six months onwards along with continued breastfeeding at least up to 2
years is essential for child growth.
7.1 Early initiation of breastfeeding for children 0-23 months A total of 234 children aged 0-23 months were included in the IYCF survey to capture the status of early
initiation and exclusive breastfeeding. Mothers with infants below 6 months (n=58) were asked that after
delivery when (duration) was the child first put to the breast. About 22.4% (12.5% - 35.3%) of the
participants responded that they put the child to the breast within an hour of the birth. About 53.4%
(39.9% - 66.7%) of mothers responded that they put their babies to the breast within one day and 24.1%
(13.9% - 37.2%) after one day each.
The colostrum is the earliest breastmilk produced by mammals. This milk is thick, sticky, concentrated and
yellowish in general. This milk is made up of immune factors, protein, sugar and fats. This milk is being
continuously produced for the first few days (approximately 2-5 days) post-partum27. About 77.6% (64.7%
- 87.5%) mothers recalled that they have given the colostrum to their newborn from the whole sample.
27 Colostrum General https://www.llli.org/breastfeeding-info/colostrum-general/
33
Since this milk is secreted for about 2 days, it is possible that despite low early initiation of breastfeeding
wouldn`t have any impact on colostrum feeding.
7.2 Exclusive Breast-feeding To capture exclusively breastfeeding among infants below 6 months in the selected households, we asked their mothers, if they were breastfeeding the child presently. Then the second part of the question followed to check if the baby was breastfed exclusively or was given anything else to drink or to eat any semi-solid food during the previous day and night. The data shows that 38.2% (25.4% - 52.3%) children (n=21) under six months old were being breastfed and did not receive any food in the past 24 hours. The data also shows that animal milk and cereal diet were also preferred by mothers for their children under 6 months i.e. 54.9% and 37.3% respectively. Data also shows that 67.9% of children who received milk also received the cereals based diet in this age group.
7.3 Initiation of Complementary Feeding and Continued Breastfeeding After a child turns 6 months of age, breast-milk alone is not sufficient to meet the nutritional requirements
of the infant and hence it is important to introduce and initiate age-specific complementary feeding. It
should be timely, nutritious in the right quantity and quality, safe with correct consistency and frequency.
To capture the initiation of complementary feeding, we asked mothers with infants 6-8 months old if they
fed the infants with soft/ semi-solid food during the previous day. From selected households, information
from caretakers of 25 infants captured from this sub-age-group. 60.0% (38.7% - 78.9%) caregivers
confirmed that their child received soft/ semi-solid food during the previous day, implying that they have
initiated complementary feeding.
7.4 Frequency and Diversity in Complementary Feeding Furthermore, to collect information on complementary feeding practices, we assessed the minimum meal
frequency for 6-23 months old. 176 children (6-23 months) from the selected households were included
for the analysis.
Minimum meal frequency indicates a minimum number of times (or more) solid, semi-solid, or soft foods
that must be received by breastfed and non-breastfed children 6–23 months of age. Minimum is defined
as four times for non-breastfed children 6–23 months. Whereas minimal dietary diversity, is defined as a
more than 4 food groups.The below table shows the minimum meal frequency of breastfed and non-
breastfed children aged 6-23 months.
22.4%
77.6%
38.2%
60.0%
0%
20%
40%
60%
80%
100%
Early initiation ofbreastfeeding
Colostrum feeding Exclusive breastfeeding Early initiation ofcomplementary feeding
Feeding practices of children less than 9 months
Feeding practices of children less than 9 months
Figure 7 showing the feeding practices for children >9 months in Damasak town
34
Table 18 complementary feeding status as per age groups in Damasak town
Indicators Prevalence Confidence Intervals
Minimal meal frequency 58.6% 49.8% - 67.1%
Minimum Dietary Diversity 56.9% 49.2% - 64.4%
Min. Acceptable diet 33.1% 26.3% - 40.5%
Primarily, the complementary feed consumed by this age group was Animal milk, cereal-based (Rice
gruel), pulses and flesh foods sometimes a child is fed vegetables i.e. 91.4% (86.2% - 95.1%), 79.3% (72.5%
- 85.1%), 56.9% (45.2% - 64.4%), 62.1% (54.4% - 69.3%) and 43.7% (36.2% - 51.4%) respectively.
8. Water, Sanitation and Hygiene: Due to their interdependent nature, these three core issues are grouped to represent a growing sector.
While each has a separate field of work, each is dependent on the presence of another. Access to safe
drinking water, sanitation, and hygiene (WASH) affects everyone. It affects survival, health, nutrition,
education, safety, dignity, income, and quality of life. For children under five, water- and sanitation-
related diseases are one of the leading causes of death. The following section presents the status of WASH
practices in Damasak town of Borno state. A total of 581 respondents were interviewed from the town.
8.1 Source of drinking water The data from the SMART survey reveals that
the majority of the population i.e. 98.5%
(97.0% - 99.2%) are using the borehole or Hand
pump as a main source of water. HH
connection was the second major source as
about 0.9% (0.3% - 2.1%) respondents were
fetching water from these sources.
8.2 Water quantity consumed for
drinking and cooking: As per the data, on an average 23.4 liters of water is being consumed per capita per day in the Damasak
town. This is quite above the SPHERE standards i.e. 15 liters per capita per day28. The data also states, that
smaller families have higher water intake whereas larger families have lower intake. As the families with
less than or equal to 5 members has intake of 23.6 liters per capita per day whereas families larger than
5 members has 13.9 liters per capita per day. Though the town has no IDP camps but there are many
people who are living among the host community. The displaced population has 19.4 liters pcpd29 of water
whereas residents has 23.7 liters pcpd. Though both are above the standards, but the disparity is still
significantly visible (p=0.0007, as per Bartlett’s chi square test for inequality of population variances).
28 SPHERE, Humanitarian charter and minimum standards in Disaster response, The SPHERE Project, Edition 2004, ISBN: 92-9139-0976, page 63
29 Pcpd: Per capita per day
98.50%
0.30%
0.90%
0.20%0.20%
Source of water
Borehole Protected shallow well HH connection River Others
Figure 8 showing source of water for Damasak town community
35
Table 19 showing average water intake per capita per day by community members of Damasak town
Average water intake per capita
per day
HH sizes Water intake Type of community Water intake
2 or more 42.2 Residents 23.7
Between 2-5 23.6 Displaced 19.4
Above 5 13.9
23.4 Overall
8.3 Time spent in fetching water It was noted that 26.2% (22.7% - 30.0%) of the total respondents took “Less than 30 minutes” to fetch
water which included going to the main source of drinking water, collect water and coming back to home;
48.0% (43.9% - 52.2%) took “30 - 60 minutes” while 24.4% (21.0% - 28.2%) had water to spend 1 to 2
hours to fetch the water.
8.4 Practice and methods for water purification It was observed that almost three fourth i.e. 72.7% (68.8%
- 76.2%) of total respondents do not practice any water
purifying technique while about 27.3% of the total
respondents practiced some method of purifying drinking
water to make it presumably safer for drinking. However,
only 8.9% technically purify the water. Filtering with a cloth
as a purification was the most preferred method 17.9%
(14.9% - 21.3%) of respondents selected as the most
preferred method. 7.2% (5.3% - 9.7%) use boiling of water
whereas 1.7% (0.9% - 3.2%) treating water using chemicals.
Moreover, 0.5% let the water settle for purification. In
terms of water storage, 91.6% (88.9% - 93.7%) has a closed
lid jerry cans in their house for storing the water. 9.8%
(7.5% - 12.7%) has jerry cans without lids to store their
water whereas 40.3% (36.1% - 44.5%) has other pots with
lid and 4.6% (3.0% - 6.7%) has pots without a lid to store
the drinking water.
8.5 Handwashing practices In this survey, 73.7% (69.8% - 77.2%) of respondents use only water for handwashing whereas 22.2%
(18.9% - 25.8%) uses water with soap and 4.4% (2.6% - 6.0%) uses the water and ash or other such items
for the same. In terms of timing of handwashing practices, the majority of the population washes their
hands after defecation i.e. 96.7% (94.8% - 98.0%) and 92.3% (89.7% - 94.2%) washes before eating. About
37.2% (33.3% - 41.3%) washes their hands before cooking but only 1.7% (0.9% - 3.2%) and 1.9% (1.0% -
3.5%) said that washing their hands prior to feeding the baby and after cleaning the baby respectively.
(See fig. 10)
Boiling7.2%
Filtering with cloth
17.9%
Letting it settle0.5%
chemical 1.7%
No treatmen
t72.7%
Water treatment methods
Figure 9 showing the water purification methods implemented
by community in Damasak town
36
The data is further analyzed to assess the proportion of population washes hands at all five critical time
points, unfortunately, no one from sampled households confirmed that they wash hands at all time points.
The data were further analyzed to assess the proportion of handwashing done on the three points i.e. 1.
After defecating, 2. Before cooking and 3. Before eating, it was found that less than one-third of the
population i.e. 31.2% (27.4% - 35.1%) washed hands on the above mentioned three points.
8.6 Defecation practices The data suggests that 48.0% (43.9% - 52.2%) responded used pit latrine for defecation on daily basis.
Also, 39.6% (35.6% - 43.7%) responded uses latrine to relieve themselves. Moreover, 3.6% (2.3% - 5.6%)
and 11.5% (9.1% - 14.5%) community members use undesignated and designated open area respectively
for defecation. It was also observed that 19.7% (16.5% - 23.2%) households have been contaminated with
human feces in their surrounding.
9. Food Security and Livelihood
9.1 Main source of income This town has majority of population
belonging to host community i.e. 92.8%
(90.3% - 94.7%) whereas about 3.4% (2.2%
- 5.4%) are the displaced population. All
respondents were interviewed to
understand the source of income of the
household. The source of income also one
of the main indicators to determine the
status of the family.
One of the major sources of income for the
population of Damasak town was casual
labors i.e. 25.5% (22.0% - 29.4%) whereas
Sale of crop20.2%
Sale of livestock
4.4%
Sale of animal product
1.2%
Sale of alchohol
0.2%
Sale of fish6.0%Sale of natural
resources1.2%
Grocery shop1.4%
Casual labour25.6%
Skilled labour7.2%
Salaried work6.3%
Petty trading13.2%
Family support
9.9%Remitance
2.6%
Other0.5%
Source of Income
Figure 11 showing the sources of income of population in Damasak town
96.70%
37.20%
92.30%
1.70% 1.90% 0
32.10%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
A F T E R D E F E C A T I O N
B E F O R E C O O K I N G
A F T E R E A T I N G
B E F O R E F E E D I N G T H E
B A B Y
A F T E R C L E A N I N G
B A B Y
A T A L L T I M E P O I N T S
A T T H R E E P O I N T S
HANDWASHING EVENTS
Handwashing events
Figure 10 showing proportion of handwashing events in the survey population
37
sale of crops and petty trading being second and third most source with 20.2% (17.1% - 23.8%) and 13.2%
(10.6% - 16.3%) (Fig 11).
9.2 Agriculture, livestock and humanitarian support The people from Damasak have been suffering from the catastrophe of human-made emergencies for
past one decade and it has forced them leave their homes and have returned in 2016 to their homes.
Therefore it is also understood the proportion of population from these communities has enough
agriculture resources and livestock. Also at the same time we need to understand the role of
humanitarian assistance provided in the field to minimize the gap.
In terms of humanitarian support, no one among the sample has denied receipt of any humanitarian
assistance in the past three months. As per the data, 74.4% (70.6% - 77.8%) received GFD support, 15.3%
(12.5% - 18.6%) received some seeds and tools whereas 7.9% (5.9% - 10.5%) received Therapeutic
feeding program or Supplementary feeding program.
Among the sample of 581 households, almost three fourth of the population i.e. 74.5% (70.7% - 78.0%)
had cultivated in a previous season whereas 43.5% (39.5% - 47.7%) families own some livestock. Among
the total sample, 17.7% (103) had neither livestock nor cultivated in the past season. About 82.3% (478)
have either cultivated or had livestock or both. Among these 478 households, 9.4% has only owned
livestock and 47.1% population had cultivated in past season but do not have any livestock whereas 43.5%
have both done the cultivation in the past season as well as own the livestock.
9.40%
47.10%
32.10%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
Only livestocks Only cultivation Both
Only livestocks Only cultivation Both
Figure 13 showing proportion of people have cultivated in past
season and livestock
74%
7.90%0.30%
15.30%
1.20%
23.30%
0%
20%
40%
60%
80%
GFD TFP/ SFP School meals Seeds and tools Fishing kit Others
Humanitarian support
Humanitarian support
Figure 12 showing the humanitarian assistant provided in past 3 months to the Damasak town community
38
9.3 Main source of Food
As mentioned earlier, the ongoing unrest
has caused people to disrupted the
agricultural practices and that has
resulted into decade long acute shortage
of food security. Though the
humanitarian agencies and government
are working hard to minimize the gap,
one cannot assume that the issue of food
unavailability at the household level has
been resolved.
Among the respondents (n=581), 7.4%
(5.5% - 10.0%) have been completely
supported by food aid whereas 33.7%
(29.9% - 37.8%) have to work for their
food. 27.2% (23.6% - 31.0%) of people
own the food production for their families and 26.3% (22.8% - 30.1%) have bought it from the shop or
market. 1.9% ( 1.0% - 3.5%) had the food as a gift from neighbors and 1.0% (0.3% - 2.1%) have borrowed
the food in past seven days in fulfilling their family’s needs. In addition, 0.9% (0.3% - 2.1%) and 1.4%
(0.6% - 2.8%) are mainly hunting and fishing to supply the food for their family.
9.4 Main shocked faced by the community: This survey also included information regarding the main shocking situation perceived to be facing in the
current scenario by the community in the survey area.
The data from figure 15 shows that the major shock for them was Insecurity or violence, as 65.5% (61.5%
- 69.4%) have perceived the same. This is not surprising at all, since this town is bordering with Niger and
has faced many violent events on a regular basis since one decade. Moreover, 18.1% (15.1% - 25.1%)
population mentioned that the flood was one of the major shocks they have faced. It is also obvious since
last such event was occurred in past few months i.e. September 2019. Also, 16.0% households perceived
Figure 14 distribution of sources of food for families in Damasak in past
7 days
Own production
27%
Work for food34%
Gifts2%
Markets26%
Borrows1%
Food aid7%
Hunting1%
Fishing1.4%
Gathering0%
Source of food
65.50%
16.00%
0.20%
18.10%
0.20%0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Violence Expensive food livestockdiseases
Flood Human sickness
Main shock faced
Main shock faced
Figure 15 showing the main shocked faced by the community of Damasak
39
that expensive or constantly increasing food prices are a major shock for them. However, one may be
assumed that skyrocketing prices of food could be the outcome of violence or insurgency that has been
disrupted the population since past decades and flood may have triggered the issue. Only less than half
percent believed that human and animal sickness as the main shock for them i.e. 0.2% each respectively.
9.5 Food consumption score The FCS considered a proxy indicator of current
food security. FCS is a composite score based
on dietary frequency, food frequency and
relative nutrition importance of different food
groups. 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 = 21.1 to 35); and acceptable consumption
(FCS = >35.0). The FCS is a weighted sum of food
groups. The score for each food group
calculated by multiplying the number of days
the commodity consumed and its relative
weight. As per the analysis presented in figure,
about 86.4% (83.3% - 89.0%) population has an acceptable food consumption score whereas 10.2% (7.9%
- 13.0%) had a borderline consumption score and 3.4% (2.2% - 5.4%) had a poor consumption score.
9.6 Reduced coping strategy Index (rCSI) When livelihoods are negatively affected by a shock
/crisis, households may adopt various mechanisms
(strategies) that are not adopted in normal day-to-day
life, to cope with reduced or declining access to food.
Reduced Coping Strategy Index (rCSI) is often used as a
proxy indicator of household food insecurity.
The data shows that 33.7% (29.9% - 37.8%) of
households were using high coping strategies whereas
26.9% (23.3% - 30.7%) and 39.4% (35.4% - 43.5%) were
using the medium and low coping strategies.
Though 86.4% population has acceptable diet however
still almost one third of the sample is still undergoing
high coping strategies to mitigate the food scarcity. This
paradox will be further assessed in the discussion chapter of the report.
3.40%
10.20%
86.40%
Food Consumption Score
Poor Borderline Acceptable
Figure 16 showing the distribution of food consumption score for
Damasak town
39.40%
26.90%
33.70%
Reduced Coping Strategy Index
Low coping Moderate coping High coping
Figure 17 showing the coping strategy index implemented by
Damasak community to mitigate food needs
40
10. Discussion The SMART survey methodology has one unique feature called the ‘Plausibility test’. This test is automatically executed by ENA software 2020 (January 2020 version) in the data entry anthropometry tab. This test is conducted to understand the quality of the data. The plausibility test gives penalty points to the data by categorizing them into excellent, good, acceptable and problematic categories. The plausibility test assesses the data based on 10 different indicators. This survey received 6% penalty score which is “Excellent” as per the category. The data received 4% penalty points for Age Ratio mainly because of excess of young children. This is also justified by high birth rate as data shows that on an average 6.5 children born every day (1.24/10,000/day) as per the population of the survey area. Also, 2% penalty score for digit preference in height measurement. The mean of WHZ was -0.61 with SD ±0.95 suggests that the survey population has more malnourished children than the reference population. Condition of malnutrition in the survey area: The prevalence of global acute malnutrition for WHZ is 6.5% whereas SAM is 0.9% however the survey conducted in the previous quarter by UNICEF suggests that the prevalence of GAM was 9.4% and SAM was 1.5%30. Though there is a decrease in the prevalence for GAM and SAM but the relationship is statistically insignificant (p=0.138 and p= 0.433 respectively). Moreover, as per the NFSS survey conducted in March 201731, shows the prevalence of GAM was 8.2% and SAM was 0.9% which shows that prevalence of GAM is higher but SAM is similar although the relationship is statistically insignificant (p=0.423). In terms of MUAC, prevalence of GAM in this survey was 1.6% and SAM is 0.5%. The NFSS round 2 that was conducted in March 2017, shows that the prevalence of GAM and SAM by MUAC is 7.7% and 4.0% respectively. The current survey shows the four fold decline in the prevalence in past three years as the relationship (t-test analysis) is statistically significant for both GAM and SAM i.e. p=0.007 and p=0.020 respectively. Malnutrition and morbidity: The data from this survey suggests that about a quarter (23.4%) of the children were also suffered from one or more illnesses in the past two weeks prior to data collection. The data also suggests that 38.6% of children suffered from cough and fever each and also 32.7% of children had diarrhea. The data also suggests that 40.6% of the cases of GAM (by WHZ or MAUC), also had some illness in the past two weeks. This relationship is statistically significant (p= 0.028). The data also suggest that 12.5%, 21.9% and 93.8% of the GAM cases also had diarrhea, fever and cough respectively. The data was further analysed to assess the impact of illness on the outcome of acute malnutrition. The data suggest that the relationship between fever and GAM (WHZ or MAUC) has found to be significant (p=0.018). Food security and coping: In terms of food consumption score (FCS) based on 7 days recall, more than three quarter i.e. 86.4% population managed to consume acceptable food groups and quantities. Whereas 3.4% has poor FCS. The survey conducted by WFP at the state level showed the relatively similar picture as the 66.7% had acceptable levels of FCS and 11.1% belonged to poor FCS groups32. This could suggest that situation might be similar due to post harvesting period.
30 Ibid 16
31 Nutrition and Food Security Surveillance (NFSS) round 2, Preliminary results, March 2017, UNICEF. 32 Rapid food security assessment in Mobbar LGA, Borno state, Nigeria: report prepared by WFP and Nigeria VAM team, April 2019.
41
Though the current survey shows great proportion of FCS, still being closer to Niger border has increased the uncertainty due to ongoing crisis has their own demerits. Poor access to basic needs such as food, shelter, and clothes gives rise to the coping mechanism in the community. The data suggests that despite having more than 85% population received acceptable food consumption; about 33.7% households imply high coping strategies to combat food insecurity in the past week. This may be due to the communities of Damasak are receiving less amount (in terms of both quality and quantity) of food that their requirement or could be due to less availability of food choices subjected to individual preferences. The data also suggests that more than 37.9% of the families have to restrict consumption of adults in order to provide adequate food for their children for at least a day or more in past week. Whereas 64.4% and 62.8% has to reduce the number of meals and limit portion of food for at least one day or more respectively during the recall period. These two indicators i.e. FCS and rCSI, provide very convoluted picture of food security situation since it is expected that with acceptable food consumption, coping should be reduced. Although still data shows that 30.7% of the population had to use high coping strategy despite having an acceptable score as per FCS scale whereas this figure goes up to 59.3% among borderline food consumption grade. Also among the respondents who had to use high coping strategies, 3.6%, 17.9% and 78.6% had poor, borderline and acceptable food consumption score respectively. The Pearson’s correlation also suggests that there is a negative relationship between FCS and rCSI i.e. higher FCS score is also related with higher coping strategies (Pearson`s R: -0.18, p = 0.000) and Chi-square test also showing significant difference among these two indicators (p=0.000). This could mean that the organizations focusing on the FSL needs to revisit their current strategy as it may be providing the basic needs to the community but does not address expectations or requirements of the community in terms of both quantity and quality. The study needs to be understand the gap in need and demand in terms of FSL programming. Table 20 comparison between FCS and rCSI among community of Damasak town
% Food Consumption
Score (FCS)
% Reduced Coping Strategy Index (rCSI)
Low (n) Medium (n) High (n) Total (n)
Poor (n) 0.9% (5) 1.4% (8) 1.2% (7) 3.4% (20)
Borderline (n) 0.7% (4) 3.4% (20) 6.0% (35) 10.2% (59)
Acceptable (n) 37.9% (220) 22.0% (128) 26.5% (154) 86.4% (502)
Total (n) 39.4% (229) 26.9% (156) 33.7% (196) 100% (581)
General food distribution, food consumption and coping strategy: The data shows that majority of the respondents received the General food distribution (GFD) from various humanitarian organizations. This may have also resulted into high proportion of population having acceptable food consumption score i.e. 86.4%. The data shows that those who have received the GFD in last three months, 91.4% has acceptable food consumption score whereas 32.9% have used the high coping strategy and 40.3% has instrumented low coping strategy. The Pearson’s correlation analysis shows that the relationship between GFD and FCS is moderately positive and significant (value: 0.255 and p=0.000) whereas relationship between GFD and rCSI was not significant statistically.
42
Table 20 shows relationship between GFD with FCS and rCSI
GFD Low coping
Medium coping
High coping
Pearson`s correlation
Poor FCS Borderline FCS
Acceptable FCS
Pearson`s correlation
No 55(36.9%) 40(26.8%) 54(36.2%) Value: - 0.034, p= 0.407
13(8.7%) 29(19.5%) 107(71.8%) Value: 0.255, p= 0.000
Yes 174(40.3%) 116(26.9%) 142(32.9%) 7(1.6%) 30(6.9%) 395(91.4%)
Total 229(39.4%) 156(26.9%) 196(33.7%) 20(3.4%) 59(10.2%) 502(86.4%)
Income or food sources against food consumption or coping: The information above suggests that despite the fact that GFD is improving the food consumption, but still many families using high coping strategies to meet the ends need. The data suggests that 64.1% of those who work for food have to use low coping strategies whereas 50% of the population who has to rely on gift or borrowing has been using high coping strategies. Table 21 showing the FCS and rCSI categories of the respondents with different income sources.
Low coping Medium coping
High coping
Income sources
←-------------------→
Poor FCS Borderline FCS
Acceptable FCS
11 (9.4%) 40 (34.2%) 66 (56.4%) Sale of crops 1 (0.9%) 5 (4.3%) 111 (94.9%)
8 (32.0%) 5 (20.0%) 12 (48.0%) Sale of live stocks 1 (4.0%) 5 (20.0%) 19 (76.0%)
3 (42.9%) 4 (57.1%) 0 (0.0%) Sale of animal products 0 (0.0%) 2 (28.6%) 5 (71.4%)
15 (44.1%) 7 (20.6%) 12 (35.3%) Sale of fish 2 (5.9%) 2 (5.9%) 30 (88.2%)
85 (57.0%) 29 (19.5%) 35 (23.5%) Casual labour 5 (3.4%) 19 (12.8%) 125 (83.9%)
24 (54.5%) 8 (18.2%) 12 (27.3%) Skilled labour 2 (4.5%) 5 (11.4%) 37 (84.1%)
13 (33.3%) 14 (35.9%) 12 (30.8%) Salaried work 1 (2.6%) 4 (10.3%) 34 (87.1%)
14 (18.7%) 37 (49.3%) 24 (32.0%) Petty trading 1 (1.3%) 6 (8.0%) 68 (90.7%)
29 (51.8%) 9 (16.1%) 18 (32.1%) Family support 4 (7.1%) 8 (14.3%) 44 (78.6%)
14 (93.3%) 0 (0.0%) 1 (6.7%) Remittance 3 (20.0%) 2 (13.3%) 10 (66.7%)
The source of income was again segregated into two subgroups i.e. owned (own production, work for food, market, hunting, fishing and gathering) and not owned (Gifts, borrow, food aid and others). The data shows that acceptable levels of food consumption is almost similar in these two groups i.e. 86.1% and 88.9% in owned and not owned groups respectively. Whereas the respondents who has not owned the food sources, 39.7% had to use high coping. In terms of income sources, it is quite interesting to know that respondents who sales the crops had 94.9% consuming acceptable food whereas 56.4% had to use high coping strategies. On the contrary, 71.4% each of the respondents selling animal products has an acceptable food consumption and low coping strategy respectively. Also the main income sources had a statistically significant relationship between rCSI and FCS i.e. interval by interval pearson`s correlation is -0.257(p=0.000) and -0.105 (p=0.012) respectively. Therefore it is very important to conduct the study focusing on the food security and livelihood to understand this information to confirm the analysis and implement better programming. Water, sanitation and Hygiene: The major source of water was the borehole as 98.5% of respondents use this source. However it seems to be the water points are quite less in the communities since almost half of the respondents (48.0%), require about one hour to fetch the water. At the same time, the water consumption per capita per day is 23.4 liters which is higher than SPHERE standards. In terms of water safety, majority (91.1%) of the survey population do not use any water purification techniques, this could be dangerous since the major source of water is from borehole could increase the
43
chances of contamination during water handling, especially during the wet season33. This may have resulted in high diarrhoea prevalence as only 7.2% practices boiling of water prior to consumption as well as 9.8% and 4.6% households do not cover their lids for jerry cans and other pots respectively. Moreover, 22.2% respondents uses water with soap to wash their hands whereas 73.7% use only water for handwashing. The handwashing is highest after the defecation and post-meal i.e. 96.7% and 92.3% respectively. Also, Moreover, 39.6% uses latrines to relieve themselves. Despite these good numbers, the prevalence of diarrhoea is still very high i.e. 32.7% which may have contributed to high case load of acute malnutrition in this survey area.
11. Conclusion The plausibility test shows the data is on “excellent” quality since the data only received the 6% of the penalty points. Therefore overall data especially anthropometry indicators are good quality and representable for the Damasak town. Prevalence of acute malnutrition at 6.5% (4.5% - 9.5% 95% CI) is above 5% which is classified as ‘moderate’ nutritional situation based on WHO’s prevalence cut-off values for public health significance. This indicates ongoing health and nutrition interventions should continue to address the medium level of acute malnutrition in communities of Damasak town. The importance of access to nutrition treatment and prevention is therefore maintained across all target populations. However since the data collection was done in March which is relatively secured in terms of food compare to the other wet months, this could have affected the low malnutrition rates.
The prevalence of stunting at 30.6% (25.2%-36.5% 95% CI) is classified as ‘very high prevalence’ based on WHO’s public health significance. This indicates the need to implement IYCF and other long term development interventions to address the serious level of chronic malnutrition which affects future labour earning, school performance and other lifetime outcomes.
In terms of mortality, the crude death rate is about 0.37/10000/day whereas under 5 death rate is about 0.17/10000/day. Both these rates are below the emergency threshold. Maternal malnutrition was accessed with both parameters i.e. MUAC and BMI. As per MUAC, no mothers and 2.9% women are severely malnourished. Also, BMI shows that about 1.4% and 4.3% sampled women are severely underweight as per the WHO classification among mothers and women of 18-49 years age respectively. Among children 0 to 23 months, about 22.4% of mothers informed that initiation of breastfeeding started within one hour of delivery whereas 77.6% of women recalled that they have given colostrum to their children. Among the children below 6 months, 38.2% of children received exclusive breastfeeding. Among children between 6 to 8 months, 60.0% initiated complementary feeding. Among children, 73.3% received minimal food frequency, 33.1% received a minimally acceptable diet. The population of Damasak town has a major proportion of host communities i.e. 92.8%. The major source of income for this population was casual or unskilled labor i.e. 25.5% whereas 20.2% population sells crops to earn their wages. The data suggests that 74.4% of people received GFD support whereas 15.3% received seeds and tools from the humanitarian support. 74.5% and 43.5% population have cultivated in the previous season and have livestock respectively. Also, 33.7% of households confirmed that they have
33 Osiemo MM, Ogendi GM, M`Erimba C, Microbial quality of drinking water and prevalence of water borne diseases in Marigat Urban Center, Kenya, Environmental
Health Insights, vol 13: 1-7, Feb. 2019; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419249/pdf/10.1177_1178630219836988.pdf
44
worked for their food and 44.4% brought their food from the market with support from humanitarian programs. Moreover, ongoing violence is perceived by 65.5% survey population as a major shock for the Damasak town. The overall SMART survey shows that the prevalence of GAM is about 6.5%, which requires proper preventive strategies along with curative intervention to counter the upcoming threat. Though the prevalence of acute malnutrition could be classified as a ‘medium’ risk, it cannot be taken lightly since the survey area is volatile and unpredictable. The humanitarian has to be well prepared since the next catastrophe could reverse the whole situation related to malnutrition. As the majority of the population is relying on humanitarian support and with limited resources for agriculture and income generation. There is an utter need for the humanitarian sector to concoct a strategy that would be more sustainable in order to control the situation related to malnutrition.
11.1 Limitations of the survey: 1. The assessment was conducted during the beginning of the food secure season therefore which
could possibly factor into the lower prevalence along with ongoing emergency response through
various partners.
2. Since the majority of the population that has been part of this survey was affected with
insurgencies, therefore the information might be skewed. This may have had an impact on the
standard deviation of the survey.
3. The indicators such as IYCF and maternal malnutrition are based on availability of the subject in
the sampled household for anthropometry thus it will serve as a supportive information and
cannot be considered for baseline of programs focused on these indicators.
4. The height of adult women was measured through ‘carpenter`s tape’ therefore the maternal
underweight analysis of BMI needs to be interpreted carefully.
5. Due to some technical error in the online data platform, the data of 7 households for indicators
except anthropometry, morbidity and mortality were lost. Therefore rest of the indicators were
assessed using available data. Though losing 7 households data may not have any significant
impact on overall quality of the report.
45
12. Recommendations
12.1 Preventive strategies for awareness and BCC strategies
12.1.1 Short term awareness strategy: In Damasak town, due to ongoing displacements and constant movements of the community, lack of
knowledge regarding malnutrition and it`s components may be inevitable. The population of these town
needs to be provided with the appropriate information about the nutrition requirements of the child and
the consequences of malnutrition on the children. The short-term campaigns followed by long term
behavioral strategy needs to be implemented in the survey area.
The activities such as Awareness campaigns, MCH (Maternal and Child Health) meetings, focus group
discussion, audiovisual sessions and BCC meetings can be conducted at the town level. These activities
can involve stakeholders like organization, government staff, local leaders aka Bulamas, religious leaders,
caregivers, PLWs and local volunteers etc. The strategy and dynamics of the participants can be changed
as per the activities. The activities can be conducted with the help of the Ministry of Health staff, AAH
front line staff or volunteers and partner organizations.
12.1.2 Long term awareness and capacity building strategies Awareness and capacity building activities require long term actions to increase the impact and coverage
of the program. The following activities can be undertaken to increase and sustain awareness and capacity
building for nutrition and related issues.
1. Workshops for malnutrition: Annual one-day workshops can be arranged for all stakeholders
including state government staff, NGOs, INGOs, self-help groups, local leaders and local donors to
raise awareness and funding for the program.
2. Audio-visual awareness campaigns: The key messages can be developed and shared with the
local community via print and digital media, to increase coverage of communication in the survey
area.
3. Creation of local contact centers: The government located in high burden areas of the town can
be converted into resource or excellence centers where direct information and counseling can be
given to caretakers of SAM/ GAM cases.
12.2 Treatment for Acute Malnutrition
12.2.1 Short term strategy for the treatment of acute malnutrition The malnutrition in the Damasak town was considered as ‘Moderate’ based on the thresholds laid out by
WHO i.e. 6.5% GAM, therefore, there is a need to undertake pragmatic action to combat the same. In this
state, the majority of the INGOs and government stakeholders practices CMAM programs using the global
guidelines that are very helpful since it will help to minimize the burden. However, at the same time, we
need to start thinking about the different approaches that are needed to achieve desirable success
through these interventions. The following activities can be conducted to strengthen the therapeutic
management of acute malnutrition;
a. CMAM plus: Globally it is well known that factors such as WASH, food security, livelihoods, health,
46
and social circumstances, etc. also affect malnutrition34. In this survey, it was found that 23.4% children in this town were suffered from either diarrhoea, cough or fever in past two weeks also the coverage of measles and vitamin A was also not at the optimum level. Moreover, more than half of the population using high coping strategies and water purification practices are also almost diminished. In this scenario, only focusing on nutrition programming will only be mere rhetoric effort. The various experts and program managers from organizations including AAH has already understood the value of inclusion of other components like WASH, FSL, NiE and IYCF using the nutrition lenses. However, it is evident from the data that there is still a gap in programming that needs to strong amalgamation of these sectors into achieving one goal. The gap can be reduced by creating interlinking indicators such as % of cure children who also received GFD or % of families of SAM children received WASH kits or vouchers to meet the ends need etc. will be helpful to predict intervention specific impact on Malnutrition.
b. Implementing proper strategies to existing innovation: Currently AAH have implemented various innovation strategies like porridge mom, volunteer based programming and family MUAC etc. The only problem is that these innovations aren`t properly assessed thus they does not provide any programmatic guidance. However organizations needs to focus on creating pilot modules of these interventions as they may turn out to be cost effective solutions as well as might turn out to be sustainable strategies. Therefore these interventions needs to be compared with standard interventions to understand the efficacy of the innovations instead of just rolling out for the sake of implementation. For example family MUAC and standard mass MUAC can be compared using operational study design as a pilot for six months or year to assess the rejection rates of both interventions. Moreover these screening can be seen as a stepping stone for active surveillance strategies.
c. Multi-sectoral assessment: It is being observed that the AAH generally uses a different set of methodologies or tools to assess the sector performance individually. In addition, even if they combine the multi-sectoral approach in surveys like SMART it does not provide reliable information since the experts of the sector are not involved in the process. Moreover, the strategy of survey implementation is robust and does not provide any clear picture of these additional indicators. This is mainly due to various issues such as sample size or methodologies. The surveys can be conducted using the multi-sectoral approach including the team of all sector managers that are available within the organization to provide the best possible technical knowledge to the surveyors in order to collect good quality data.
d. Management of MAM children: Due to high caseloads, children with relatively high MUAC are often ignored. The researchers have suggested that the children diagnosed as MAM with MUAC are associated with a hazard ratio (risk of death) of 3.1935. Though this is less than half as compare to SAM children with MUAC (HR: 6.44) but this number could also contribute heavily in a country like Nigeria where U5MR is already as high as 104 per 1000 live births36. The MAM with co-morbidity should also be treated along with the SAM children with or without RUTF. In case of
34 UNICEF`s approach to scaling up Nutrition: For mothers and their children, Nutrition Section Program division, UNICEF, June 2015, pg. 9
35 Schwinger C, Golden MH, Grellety E, Roberfroid D, Guesdon B (2019) Severe acute malnutrition and mortality in children in the community: Comparison of indicators in a multicountry
pooled analysis. PLoS ONE 14(8): e0219745. https://doi.org/10.1371/journal. pone.0219745. 36 Levels and trends in child mortality: Report 2017, Estimates developed by the UN inter-agency group for child mortality estimation, UNICEF, WHO & WB, pg 27.
47
using RUTF, the protocol from ComPAS study may be referred37. This study has implemented management of MAM using two sachets of RUTF per day. In case of no RUTF, the strong programming using innovations like Tom Brown or porridge mom food along with additional support from the WASH, FSL and health sector can be provided to treat the co-morbidities to avoid further deterioration.
e. Food security and Livelihood Programming: The data suggests that almost all participants received some support from the humanitarian agencies regarding FSL that shows almost 86.4% with acceptable food consumption score. However, the population is still heavily coping which skewed the scenario towards the negative side. Thus, FSL programming needs to be strategies to address the coping instead of just satisfying the basic needs. Inclusion of discussion and feedback mechanism with the beneficiaries and MCH meetings may help to address the needs. Moreover, inclusion of more indigenous food items such as nuts or bean cakes even though low nutrition values, can be added to improve the acceptability of the food aid provided by the organizations.
f. Fish and malnutrition: The survey area has abundance of fish and majority of the community enjoys consuming the same. However, the study needs to be conducted to understand the possible impact of fish consumption among children on malnutrition. Currently drying the fish is only way to improve the durability of the food for long term consumption. However, other methods of fish conservation such as creating fish pickle (commonly done in Bangladesh) can be explored not only to preserve the fish long terms but also to make tastier to encourage children to consume upon conducting an acceptability trail in the beginning of the intervention. Fish not only has good source of protein but also has vitamins and minerals that are necessary for growth.
12.2.2 Long term strategy for management of Acute Malnutrition Long term strategy to further decrease the burden of malnutrition in the study area is quintessential for
the success of the CMAM program. After execution of short term strategy for treatment of acute
malnutrition successfully, more sustainable and effective plan needs to be rolled out for prevention and
better recovery of the survey area from huge caseload of acute malnutrition. The second phase should
strengthen data monitoring, inclusion of underlying as well as indirect causes of malnutrition and
execution of various studies to understand different dynamics of malnutrition. The following activities
should be undertaken during long term phase of the program.
a. Use of Family MUAC: This method is also known as Mother`s MUAC at a global scale. The mothers or caregivers from the community to train on measurement of MUAC. The reporting of the cases from mothers/ caregivers could either be based on colours or with measurement in mm/ cm. Since mothers or caregivers involved in this task would allow program staff to detect SAM cases at the earliest. This will lead to less hospitalization and allow the field team to have more time for management of the child`s health and improve the success a.k.a. cure rate.
b. Inclusion of volunteers or mothers in CMAM programming: Though the survey area was secured due to the trenches and presence of armed forces, but still major portion of the LGA is still inaccessible as well as very unpredictable. However, the children living in such situation may have
37 Jeanette Bailey, Rachel Chase, Marko Kerac, André Briend, Mark Manary, Charles Opondo, Maureen Gallagher and Anna Kim (2016). Combined protocol for SAM/MAM treatment: The
ComPAS study. Field Exchange 53, November 2016. p44. www.ennonline.net/fex/53/thecompasstudy
48
been more affected with the issue of malnutrition due to severely restricted resources due to ongoing insurgencies. Moreover, many research shows that 85% of SAM cases are uncomplicated and can be treated with just RUTF. Therefore local volunteers or champion mothers (very active mothers from the community) can be identified and trained on management of uncomplicated SAM cases at remote level. This can be strengthen by using proper active surveillance method.
c. Research and innovations: The separate team can be created from the stakeholders including technical specialists from program and monitoring teams of organization as well as epidemiologist or researchers to design and conduct various studies to understand the different dynamics of malnutrition in the community. The information collected in the research and studies should be used to upgrade the existing CMAM program. In addition, RCTs for indigenous RUTF, two-stage screening method and use of lower dosage of RUTF can be undertaken through this team based on the learnings of pilots of short-term strategies. Moreover, the studies using methodologies such as NCA, anthropological and ecological studies can be conducted to understand the local practices to combat malnutrition.
12.3 Research for simplified CMAM
12.3.1 Short term strategies on research for simplified CMAM Though the prevalence of SAM was 0.5% and 0.9% respectively for MUAC and WHZ, this could turn into a
very high caseload since the proportion of under-five-year-old is as high as 18.4% as per this survey. In
terms of whole Nigeria, the caseload for SAM was about 441,612 children in 201538 also each child would
need a cost of $ 150 for the treatment39 and it would easily double if other baskets from sectors like FSL,
WASH, and emergency included. This would incur a cost of 132.5 million USD for the country. Both the
caseload and the cost of treatment may have increased for past four years. Therefore there is need to
create a simplified CMAM which would make the intervention more cost-effective.
1. CMAM with a lower dosage of RUTF: The pilots can be conducted with a reduction of dosage of
RUTF40 to assess the efficacy for the same. This will help to treat more children with lesser RUTF.
At the global scale studies like ComPAS and MANGO are being conducted to assess the efficacy of
the same. The learning can be used in near future in these pilots to improve the availability of the
costly RUTF.
2. Use of two-stage screening method: Though this may found contrary to the objective of the
research mentioned above. However, the data shows that MUAC only programs may miss the WHZ
only cases that could contribute to 78.1% of the actual burden (Table 10). This may affect the
impact of the CMAM intervention negatively since the major strata of cases is invisible for MUAC
only programming. The study conducted in Cambodia, suggests a two-stage screening method. In
first stage MUAC <133 mm can be identified and in second stage SAM children with both MUAC
and WHZ can be detected for treatment41.
38 Bulti A, Briend A, Dale N et al, Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute
malnutrition: experience from Nigeria, Archives of Public Health (2017) 75:66 39 Collins S, Treating acute malnutrition seriously, Arch Dis Child 2007;92:453–461. doi: 10.1136/adc.2006.098327 40 Ibid 27
41 Laillou A, Prak S, de Groot R, Whitney S, Conkle J, et al. (2014) Optimal Screening of Children with Acute Malnutrition Requires a Change in Current WHO
Guidelines as MUAC and WHZ Identify Different Patient Groups. PLoS ONE 9(7): e101159. doi:10.1371/journal.pone.0101159
49
3. SMART surveys: Another SMART survey should be conducted in the monsoon period i.e. August
to October in order to understand the actual burden of malnutrition in the survey area. The SMART
survey can be undertaken including other indicators like IYCF, WASH, and morbidities, etc.
4. Food Costing study: Along with FSL programming, the FSL costing study needs to be conducted. This study will assess not only the overall cost of diet for GFD but also will help to estimate the cost and its effectiveness of using the acceptable and adaptable diets for the community to reduce coping. This study not only will help to minimize gap of food but also will help to create a sustainable programming for FSL.
12.4 Surveillance strategies
12.4.1 Short term strategies for surveillance: The Damasak town comprised of only two wards i.e. Damasak Central and Zanna Umorti. The whole area
is relatively smaller not only in terms of geography but also while considering population. The whole town
is secured through trenches and more than 90% population is permanent residents. As per the data, this
town has about 12,000 children. Therefore this area is perfect place to launch active surveillance strategy
as a pilot. The only limitations would be low cellular network and unpredictable circumstances. The short
term strategy should lay a foundation for active surveillance of acute malnutrition whereas after 3 years
program should shift to sentinel surveillance of malnutrition in order to have different layers of
intervention (soft programs in normal sites whereas more interventions in high-risk areas for better
management of resources).
a. Understanding existing strategies: AAH is working in this town since past few years, managing different sectors through different grants such as SDC, GAC and ECHO. Though all these grants have different approaches and teams, inter program coordination is a key to establish the surveillance strategy in the survey area. Currently SDC is conducting house to house enumeration of all members of the community in the selected areas whereas ECHO has been using monthly MUAC screening through mother groups as well as measurers (community volunteers). However, both activities are done separately and both teams are oblivious to information they possess. Proper merger of these two activities across the whole program area would laid out the foundation of active surveillance simply introducing the adequate forms and system. All the grants managers need to discuss to explore the possibility of merging these two activities in order to create a complete database capturing family information of whole program area.
b. Creation of child-level database: At present, child monitoring is done at the level of sites and data is being captured in the hard form. Therefore, it is difficult at the M&E level to validate the performance especially when there is a very small M&E team is working in the survey area. Thus relying more on-field visits that have limited advantage of quality management. Therefore, the data at the level of child should be created using either a software (open SRP) or at least at the excel level which will allow program implementers to verify the data properly. This database will cater information from program not any surveys.
c. Inclusion of proper monitoring and surveillance strategies: The Damasak town, is quite a volatile area due to ongoing issues of violence and closeness to Niger border. Therefore many occasions, it is very difficult to predict the impact of programs as it may be confounded by the recent disturbances. Therefore it is quintessential to improve the monitoring and surveillance strategies to regularly assess the programs to prepare for the adjustments laid upon due to any issues.
d. Creation of surveillance team at the town level: For best results, surveillance should be created
50
as an autonomous body with less pressures of targets or achievements especially in the early stages since establishing surveillance strategy is very difficult task mainly due to complicated nature of the activity. The separate team of three to four members can be created along with field volunteers and data entry officers to create a team. This team has to launch the program by introducing surveillance forms and methodologies into the existing programs. The surveillance strategy need to be established as a feedback mechanism to the program to provide timely and legitimate guidance to programs. This team can include partners like UNICEF, INTERSOS or other existing organization as well as government stakeholders who are based in the Damasak town.
12.4.2 Long term strategies for Surveillance:
a. Nutrition Surveillance: Currently organizations have some surveillance strategy (NIS) which basically involves a series of surveys (SMARTs) at the state or LGA scale to assess the impact of the program. However, this can only provide point prevalence but surveillance needs to strengthen the active case finding. Therefore need to launch the strategy including principals of active surveillance systems. This would need to streamline strategy of management of cases with implementing a strict timeline. The short term strategies such as family MUAC and child database will help to develop the same further. Once the active surveillance being implemented for brief period, the programming can start identifying hotspots to descend into sentinel surveillance programs.
b. Timely appraisals of the NIS: Only establishing the surveillance system will not be the only solution as this will be unserviceable without regular appraisal of the systems. Constant surveillance visits not only to assess the quality of mass MUAC screening but also follow up of all identified cases is required as well. The survey methodologies like SQUEAC and SMART can be used to assess the efficacy of the surveillance needs to be tested for parameters including coverage and prevalence. These appraisals will help not only to understand the impact of the activity but also be used as a mechanism to execute the sentinel surveillance strategy.
c. Sentinel Surveillance: As the well-established surveillance system to understand the hotspots of malnutrition across whole town, the program can launch the sentinel surveillance. The ongoing mass MAUC screenings and follow ups will not only help us to identify malnutrition pockets but also highlights impact of seasons, migration patterns and food scarcity in these pockets to create a goal oriented strategies with proper resource allocation to ensure well planned program. The sentinel surveillance will allow program managers to create intense nutrition sensitive activities in the hotspots with moderate to mild intensity of activities in other part of the town. This system will also work as an alarming system to aware program managers to better prepare for upcoming spike in prevalence of acute malnutrition in the program area. Also use of sentinel surveillance will help to achieve higher coverage with relatively less operation cost.
13. Annexure
13.1 Plausibility check for: NG_SMART_Damasak_final data.as Standard/Reference used for z-score calculation: WHO standards 2006
(If it is not mentioned, flagged data is included in the evaluation. Some parts of this plausibility report are more for
advanced users and can be skipped for a standard evaluation)
Overall data quality
Criteria Flags* Unit Excel. Good Accept Problematic Score
Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-7.5 >7.5
(% of out of range subjects) 0 5 10 20 0 (0.0 %)
Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001
(Significant chi square) 0 2 4 10 0 (p=0.359)
Age ratio(6-29 vs 30-59) Incl p >0.1 >0.05 >0.001 <=0.001
(Significant chi square) 0 2 4 10 4 (p=0.001)
Dig pref score - weight Incl # 0-7 8-12 13-20 > 20
0 2 4 10 0 (7)
Dig pref score - height Incl # 0-7 8-12 13-20 > 20
0 2 4 10 2 (8)
Dig pref score - MUAC Incl # 0-7 8-12 13-20 > 20
0 2 4 10 0 (6)
Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >=1.20
. and and and or
. Excl SD >0.9 >0.85 >0.80 <=0.80
0 5 10 20 0 (0.95)
Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6
0 1 3 5 0 (0.05)
Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6
0 1 3 5 0 (-0.04)
Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001
0 1 3 5 0 (p=0.392)
OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 6 %
The overall score of this survey is 6 %, this is excellent.
There were no duplicate entries detected.
Percentage of children with no exact birthday: 77 %
13.2 List of Indicators
S.N Indicators Numerator Denominator Remarks Value
1. Child Nutrition 1.1 Wasting (Z-Score)
1.1.1 Wasting
prevalence
Number of children age 0-59 months who fall
below minus two standard deviations from the
median weight for height of the WHO standard
and/ or presence of bilateral pitting edema
Total number of children age 0-59
months measured Percentage
1.1.2 Moderate
Wasting
prevalence
Number of children age 0-59 months who fall
between below minus two to greater than or
equal to minus three standard deviations from
the median weight for height of the WHO.
Total number of children age 0-59
months measured
Percentage
1.1.3 Severe Wasting
prevalence
Number of children age 0-59 months who fall
below minus three standard deviations from the
median weight for height of the WHO standard
and/or edema
Total number of children age 0-59
months measured
This indicator will only be used to calculate
prevalence of Marasmus in the report
Percentage
1.2 Underweight
1.2.1 Underweight
prevalence
Number of children under age 5 who fall below
minus two standard deviations from the median
weight for age of the WHO standard
Total number of children age 0-59
months measured
Percentage
1.2.2 Moderate
underweight
prevalence
Number of children under age 5 who fall
between below minus two to greater than or
equal to minus three standard deviations from
the median weight for age of the WHO standard
Total number of children age 0-59
months measured
Percentage
1.2.3 Severe
underweight
prevalence
Number of children under age 5 who fall below
minus three standard deviations from the median
weight for age of the WHO standard and/ or
edema
Total number of children age 0-59
months measured
Percentage
1.3 Stunting
1.3.1 Stunting
prevalence
Number of children under age 5 who fall below
minus two standard deviations from the median
height for age of the WHO standard
Total number of children age 0-59
months measured
Percentage
1.3.2 Moderate
Stunting
prevalence
Number of children under age 5 who fall
between below minus two to greater than or
equal to minus three standard deviations from
the median height for age of the WHO standard
Total number of children age 0-59
months measured
Percentage
53
1.3.3 Severe Stunting
prevalence
Number of children under age 5 who fall below
minus three standard deviations from the median
height for age of the WHO standard
Total number of children age 0-59
months measured
Percentage
S.N Indicators Numerator Denominator Remarks Value
1.4 Acute malnutrition (MUAC &/or bilateral edema)
1.4.1 Wasting
prevalence
Number of children age 6-59 months who fall
below MUAC 125 mm
Total number of children age 6-59
months
Percentage
1.4.2 Moderate
Wasting
prevalence
Number of children age 6-59 months fall
between below MUAC 125 mm and greater or
equal to 115 mm
Total number of children age 6-59
months
Percentage
1.4.3 Severe Wasting
prevalence
Number of children age 6-59 months who fall
below MUAC 115 mm
Total number of children age 6-59
months
Percentage
1.5 Acute Malnutrition (WHZ &/ or bilateral edema)
1.5.1 Acute
malnutrition
prevalence
Number of children age 6-59 months who fall
below minus two standard deviations from the
median weight for height of the WHO standard
Total number of children age 6-59
months
Percentage
1.5.2 Moderate acute
malnutrition
prevalence
Number of children age 6-59 months who fall
between below minus two to greater than or
equal to minus three standard deviations from
the median weight for height of the WHO
standard
Total number of children age 6-59
months
Percentage
1.5.3 Severe acute
malnutrition
prevalence
Number of children age 6-59 months who fall
below minus three standard deviations from the
median weight for height of the WHO standard
Total number of children age 6-59
months
Percentage
1.6 Overweight
1.6.1 Overweight
prevalence
Number of children under age 5 who are above
two standard deviations of the median weight for
height of the WHO standard
Total number of children age 0-59
months
Percentage
S.N Indicators Numerator Denominator Remarks Value
2.Immunization, Child health and Morbidity
2.1 Vitamin A
supplementation
among children
under 5 years of
age
Number of children age 9-59 months who
received at least one high-dose vitamin A
supplement in the 6 months preceding the survey
Total number of children age 9-59
months
Percentage
2.2 Deworming
among children
under age 5
Number of children age 12-59 months who given
an anthelmintic drug in the 6 months preceding
the survey
Total number of children age 12-59
months
Percentage
54
2.3 Measles
immunization
coverage
Number of children age 9 to 59 months who
received measles vaccine before the survey
Total number of children 9 to 59
months
Percentage
2.4 Prevalence of
diarrhea among
children under
age 5 years
Number of children under age 5 years who had
diarrhea in the last two weeks
Total number of children under age 5
years
Percentage
2.5 Diarrhoea
treatment with
oral rehydration
salts (ORS) and
zinc
Number of children under age 5 years with
diarrhea in the previous 2 weeks who received
ORS and Zinc
Total number of children under age 5
years with diarrhea in the previous 2
weeks
Percentage
2.6 Prevalence of
fever among
children under
age 5 years
Number of children under age 5 years who had
fever in the last two weeks
Total number of children under age 5
years
Percentage
2.7 Treatment of
fever with ACT
Number of children under age 5 years who had
fever in the last two weeks who were treated
with ACT
Total number of children under age 5
years with fever in the previous 2
weeks
Percentage
S.N Indicators Numerator Denominator Remarks Value
3. Malaria
3.1
Household
availability of
mosquito nets
Number of households with
Total number of households
surveyed
Percentage
(a) at least one mosquito nets
(b) at least one mosquito nets for every two
people
3.2 Children under age 5 who slept
under a
mosquito net
Number of children under age 5 years who slept under a mosquito net the previous night
Total number of children under age 5 who spent the previous night in the
interviewed households
Percentage
4. Women Nutrition
4.1 Acute
Malnutrition
prevalence
Number of women age 15 - 49 years who fall
below MUAC 230 mm
Total number of women age 15 to 49 Percentage
4.2 Moderate Acute
Malnutrition
prevalence
Number of women age 15 - 49 years who fall
between below MUAC 230 mm and greater than
or equal to 180 mm
Total number of women age 15 to 49 Percentage
55
4.3 Severe Acute
Malnutrition
prevalence
Number of women age 15 - 49 years who fall
below MUAC 180 mm
Total number of women age 15 to 49 Percentage
5. Water and Sanitation
5.1 Water source Different sources of water used by the
population
Total number of households Percentage
5.2 Time require
for collection of
water
(drudgery)
Different time require for HH to collect water
(frequency)
Total number of households Percentage
5.3 Total water use Total water (in litres) use for domestic purpose Total number of households This will be calculated as a mean of quantity/ capita mean
5.4 Water
purification
Different purification methods used by
population
Total number of households Percentage
5.5 Hand washing
events
Number of household with handwashing on
different events
Total number of households Percentage
5.6 Use of Soap Number of households using either water or
soap or ash or other to wash their hand
Total number of households Percentage
5.7 Defecation
location
Number of households defecate in specific area Total number of households Percentage
5.8 Water storage Number of households with closed lid of water containers
Total number of households Percentage
5.9 Sanitation Number of households with human faeces
present in premises
Total number of households Percentage
6. Food consumption and coping
6.1 Food
consumption
score
Number of HH in each FCS category Total number of households This will be analysed based on FCS by INDDEX
Project
Percentage
6.2 Reduced Coping
Strategy index
Number of HHs with different coping index Total number of households As per guidance note from WFP Percentage
S.N Indicators Numerator Denominator Remarks Value
7. Infant & Young Child Feeding
7.1 Children ever
breastfed
Number of children 0-23 (born in the last 24)
months who were ever breastfed
Total number of children aged 0-23
months
Percentage
7.2 Early initiation
of breastfeeding
Number of children 0-5 months who were put
to the breast within the first hour of birth
Total number of children aged 0-5
months
Percentage
7.3 Colostrum
feeding
Number of children 0–5 months of age who
were fed with a colostrum after birth
Total number of children aged 0-5
months
Percentage
56
7.4 Exclusive
breastfeeding
Number of infants 0-5 months who received
breast milk the previous day (in the past 24
hours) and did not receive any other foods or
liquids during the previous day
Total number of infants aged 0-5
months
Percentage
7.5 Introduction of solid, semi-solid
or soft foods
Number of infants 6–8 months of age who breastfed and also received solid, semi-solid or
soft foods during the previous day
Total number of children aged 6-8 months
Percentage
7.6 Minimum
Dietary
Diversity
Number of children 6–23 months of age who
received foods from ≥4 food groups during the
previous day
Total number of children aged 6-23
months
Dietary diversity is computed based on 7 food
groups as recommended by WHO (2008b)
Consumption of any amount of food from each
food group is sufficient to count except if a food
item was only used as a condiment.
Percentage
7.7 Minimum Meal
Frequency
Number of breastfed and non-breastfed children
6–23 months of age who received solid, semi-
solid or soft foods the minimum number of times
or more during the previous day
Total number of breastfed children
aged 6-23 months
Minimum dietary diversity is defined as: 2 times for
breastfed infants 6–8 months old; 3 times for
breastfed children 9–23 months old and 4 times
for non-breastfed children 6–23 months old
(WHO, 2008a). “Meals” include both meals and
snacks (other than trivial amounts) as reported by
the respondents.
Percentage
7.8 Minimum
Acceptable Diet
Number of breastfed and non-breastfed children
6–23 months of age who had at least the
minimum dietary diversity and the minimum meal
frequency during the previous day
Total number of breastfed children
aged 6-23 months
Complex analysis of two indicators mentioned
above
Percentage
57
13.3 Questionnaire
13.3.1 Anthropometry Questionnaire
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Child No.
HHNO
Child Name
Sex m = Male f = Female
Date of Birth (DD/MMYY)
Age in months
Weght in Kg (eg 12.4)
Height in cm (eg 78.1)
Oedema n = No y =Yes
MUAC in cm (eg 11.3)
Vit. A in last 6 months --------- 0 = No 1 =Yes
Measles Vaccine ---- 0 = No 1 =Yes with EPI card 2 =Yes recall 3 = Child <9m
Illness in past 14 days? 0 = No 1 =Yes If no, go to 16
Type of Illness 1 = Fever 2 = Cough 3 = Diarrhoea 99 = Other (specify)
Treatment Sought: 0 = None 1 =Hospital 2 =PHC 3 = Mobile /outreach clinic 4= Teaching hospitals 5=Private clinic 6=Traditional practitioner 7= Spiritual healer 8=Pharmacy/chemist 99=Other (Specify)
Did the child sleep under a mosquito net(LLITN) last night? --------- 0= No 1= Yes
Dewormed in last 6 months (12-59 months) ------- 0 = No 1 =Yes 88 =DK
1
2
3
4
5
6
7
8
9
10
11
58
13.3.2 FCS and rCSI Questionnaire 29 Food Consumption Score 30. Reduced Coping Strategy Index
Over the last 7 days, how many days did your household consume the following foods? In the past 7 days, if there have been times when you did not
have enough food or money to buy food, how often (days)
has your household had to:
HH
NO
Cereals &
tubers
(maize,
rice,
sorghum,
cassava,
potatoes,
etc)?
Pulses
(beans,
peas,
groundnuts,
green
grams,
etc)?
Vegetables? Fruits?
Animal
source
foods
(meat,
fish,
eggs)?
Milk &
dairy
products?
Sugar and
honey
(e.g.sweetened
foods, drinks,
chocolates,
sweets,
candies, etc)?
Oils/fats
(e.g. fat or
oil, butter,
ghee,
margarine
added to
food or
used for
cooking)?
a. Rely on
less
preferred
and less
expensive
foods?
b.
Borrow
food, or
rely on
help
from a
friend or
relative?
c. Limit
portion
sizes
at meal
times?
d. Restrict
consumption
by adults so
that small
children can
eat?
e.
Reduce
the
number
of
meals
eaten in
a day?
13.3.3 FSL Questionnaire 18 19 20 21 22 23 24 25 26 27
59
HH
NO*
HH
size
(No. of
people
living
in HH)
Resident
status of
HH: -----
-----
1 =
Resident
2 =
Returnee
(in the
last 1yr)
3 = IDP
4 =
Refugees
Is there
any IDP
or
returnee
currently
living in
your
household
?
---------
0 = No
1 = Yes
Is the HH
head
male or
female?
---------
1 = Male
2=Female
What was your HHs main
source of income in the last 30
days? -----------
1 = Sale of crops
2 = Sale of livestock
3 = Sale of animal product
4= Sale of alcoholic beverages
5 = Sale of fish
6 = Sale of natural resources
(firewood; charcoal; grass)
7 = Grocery shop
8 = Casual Labour
9 = Skilled labour
10 = Salaried work
11 = Petty trading
12 = Family support
13= Remitance
99=others (Specify)
Did you
cultivate in
the recent
last
season? ---
------
0 = No
1 = Yes
Does the
HH own
any
livestock,
heards or
farm
animals ?
------
0 = No
1 = Yes
Has your HH
received any
Humanitarian
Assistance in
the past 3
months?
---------
0 = None
1 = GFD 2=
School meals
/feeding
3=TFP/SFP
4 = Seeds &
tools
5=Fishing kits
What was the
main source of
food in the past 7
days
---------
1 =Own
production
2 =Work for food
3 =Gifts from
neighbours
4 =Market/shop
purchase
5=Borrowing/debts
6 =Food aid
7= Hunting
8 = Fishing
9 = Gathering
99= Other, specify
What is the main
shock currently faced
by the HH
----------
1 = Insecurity /violence
2 = Food too expensive
/increased price
3 = Livestock diseases
4 = Flood
5 Human sickness
6 =Returnee/IDP living
with HH
7 =Late food
distribution 8 = Social
event
9 = Delay of rains
10 = Weeds/ Pest
99= Other( specify)
60
13.3.4 WASH Questionnaire 49 50 51 52 53 54 55 56 57
HHNO What is the household's
main source of drinking
water ? ----------
1=Borehole/hand pump
2=Protected Shallow
well 3= Open shallow
well 4= Protected
spring 5= Stream
6= HH connection /
Stand pipe /Tanker
7= Dam / Pond
99= Other
(specify______)
How long does
it take the HH
to collect water
(including travel
to and from and
waiting)?
--------------
1 = <30 min
2 = >30min to
<1hr
3 = >1hr to <
2hr
4 = >2hr to <
4hr
5 = >4hr
How many
jerricans of
water did the
HH use
yesterday in
total
(excluding
water for
washing
clothes and
for animal)?
(Define how
many litres
in a jerrycan
if the
population
all use the
same)
What do you usually do
to water to make it safer
before household
members drink it? -------
--
0 =Nothing
1 =Boiling
2 =Filtering with a cloth
3 =Letting it settle
4 =Water treatment
chemicals 99
=Others(Specify)
When do you usually
wash your hands
(more than one if
appropriate - do not
prompt) ---------
0 = Never
1 = After defecating
2 = Before cooking
3 = Before eating
4 = Before feeding the
baby
5=Affter cleaning the
baby
99=other (specify)
What do you
use to wash
hands? .......
0 = Nothing
1 = Water only
2 = Water +
Soap
3 = Water +
Ash
99 = other
(specify)
Where does the
household usually
defecate or relieve
themselves (include
more than one if
necessary)?
---------
1 = Undesignated open
area
2 = Designated open
area
3 = Hole
4 = Latrine
99 = Other (specify)
May I see all the
containers you
have for storing
the water?
(Include more
than one if
necessary)
1. Jerry can with
closed lid
2. Jerry can with
no lid
3. Other pot with
lid
4. not covered
1.
(Observe) Is there
any visible traces
of human faeces in
the vicinity (30
meters) of the
household?
1. Yes
0. No
1.
13.3.5 IYCF Questionnaire
IYCF QUESTIONNAIRE per child 0-23 months (one questionnaire per households)
This questionnaire is to be administered to the mother or the main caregiver who is responsible for
feeding the child and the child should be between 0 and 23 months of age)
No QUESTION ANSWER CODES Child 1
Answer
Child 2
Answer
Child 3
Answer
Child 4
Answer
Child 5
Answer
31 Child Number (ID)
32 Sex Male 1 Female 2
33 Birth-date
Take from the PREVIOUS
QUESTIONNAIRE- do not ask
mother again
Day/Month/Year
(dd/mm/yyyy)
34 Child’s age in months (Take from the PREVIOUS
QUESTIONNAIRE- do not ask mother again)
35 After the delivery, when was
the child put on breast for
the first time?
children 0-5 months 29 days
Less than 1 hour………….1
In first day………….…….2
More than one day………..3
Don’t know………………99
36 Did you feed your child with
colostrum (local language =
thiith)
children 0-5 months 29 days
Yes……………………….1
No………………………..0
37
Is the child (name)
receiving breast milk?
If NO, skip to question
IF7
children 0-5 months 29 days
Yes……………………….1
No………………………..0
(Note: If the child has received
ORS, multivitamin syrups, any
medicines we will still count it
as Exclusive breastfeeding)
37A If yes, was the child
(name) given anything
else to drink/or to eat
62
in addition to breast
milk during the day
and night yesterday
(between sunrise and
sunset yesterday, and
sunset and sunrise this
morning)
0 =None other than breast
milk
1 = Powder/animal milk/yogurt
2 = Cereals based diet
3 = Plain water
4 = Fruit Juice
5 = Sugar water
6 = Vegetables
38 Did (Name) receive
solid, semi-solid or soft
foods during the
previous day?
Question to be asked only for
child between
6–8 months of age
39 Number of times the
child was given solid,
semi-solid or soft foods
during the previous
day?
6-23 months of age
Mention the number
40 Ask mother if the child 6–23
months of age received
foods from these food
groups during the previous
day.
6-23 months of age
(Put a tick ( √ )
Grains, roots and tubers
(Rice, wheat, ragi, maize, millets,
potato, Yam)
Legumes, Pulses (Dal) and nuts
Dairy products (milk, yogurt,
cheese)
Flesh foods (meat, fish, poultry
and liver/organ meats)
Eggs
Vitamin-A rich fruits and
vegetables (Green Leafy
vegetables, Papaya, Mango,
Pumpkin, Carrot, Tomatoes)
Other fruits and vegetables
41 Did (Name) receive
breastmilk during the
previous day?
6-23 months of age
Yes…………1
No………….0
(To check continued breastfeeding for
upto2years)
63
42 How do you feed the
child when having
diarrhoea, do you feed
him
0. Nothing at all
1. Less than usual or fluids only
2. Same as usual
3. More than usual
4. Never had diarrhoea
Maternal Malnutrition (to measure mothers of child measured for anthropometry in one household)
No QUESTION ANSWER CODES Mother 1 Mother 2 Mother 3
43 Mother name
Child ID If multiple children please add eldest
one
44 Are you pregnant or
now?
Yes……………………….1
No………………………..0
45 Are you lactating
now?
Yes……………………….1
No………………………..0
46 MUAC of mother ---.- cm (till 0.1 cm)
47 Weight of mother ---.—Kg ( till 0.1 Kg) exclude
pregnant women
48 Height of mother ---.—cm (till 0.1 cm) exclude
pregnant women
49 BMI BMI – Kg (weight in Kg)/ M2 (Height
in meters)
64
13.3.6 Demography and Mortality Questionnaire
DATE OF INTERVIEW: [ D ][ D ]/[ M ][ M ]/ [ Y ][ Y ]
COUNTRY: STATE: VILLAGE:
NAME OF RESPONDER:
CLUSTER NO. [ ][ ] TEAM NO. [ ][ ] HOUSEHOLD42 NO. [ ][ ] 01 02 03 04 05 06 07 08 09 10
No.
Name
Sex
(M/F)
Age
(years)
Joined on or after:
Left on or after:
Born on or after:
Died on or after:
Cause of death
(optional)
Location of death
(optional)
_____Prophet`s Birthday_29/10/2019_____________ (Start date of the recall period - ex. Jan. 1, 1900)
WRITE ‘Y’ for YES. Leave BLANK if NO.
a) List all the household members that are currently living in this household.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
b) List all the household members that have left this household (out migrants) since the start of the recall period.
1 Y
2 Y
3 Y
4 Y
5 Y
c) List all the household members who died since the start of the recall period.
1 Y
2 Y
3 Y
13.4 List of Clusters
State LGA Ward Settlement Cluster SetPop LATITUDE LONGITUDE
Borno Mobbar DAMASAK KAIRI 1 915 13.105210 12.512260
Borno Mobbar DAMASAK MUNNA STREET 2 685 13.104550 12.511790
Borno Mobbar DAMASAK ZANNA GETOS 3 810 13.105690 12.510870
Borno Mobbar DAMASAK AJARI 4 1071 13.106520 12.508980
Borno Mobbar DAMASAK MUSUN WATER 5 630 13.107890 12.510330
Borno Mobbar DAMASAK BULAMA ALI RC1 675 13.104680 12.509970
Borno Mobbar DAMASAK ALISHAYIMARI ROAD 6 530 13.107440 12.508620
Borno Mobbar DAMASAK BULAMARI 7 315
Borno Mobbar DAMASAK BULAMA SAJE 8 880 13.110940 12.508860
Borno Mobbar DAMASAK KUMBURI 9 965 13.108650 12.508440
Borno Mobbar DAMASAK KUMBURI GANA RC2 535
Borno Mobbar DAMASAK FULATARI LAWANTI 10 830 13.113180 12.509180
Borno Mobbar DAMASAK MALLAM MAGAJI 11 610 13.111360 12.511170
Borno Mobbar DAMASAK BULAMA GASHI 12 770 13.112370 12.504810
Borno Mobbar DAMASAK BABA KUNNO 13 665 13.108350 12.507660
Borno Mobbar DAMASAK ALHAJI ABBA KURA RC3 610 13.108910 12.505880
Borno Mobbar DAMASAK FULATARI 14 1325 13.104270 12.506340
Borno Mobbar DAMASAK MALLAM HUDU 15,16 1210 13.104820 12.506130
Borno Mobbar DAMASAK GONI HUDU 17 1240 13.103070 12.506990
Borno Mobbar DAMASAK MALLAM TAWU 18 1185 13.104800 12.507940
Borno Mobbar DAMASAK JOKA JURUYE 19 1115 13.142010 12.514870
Borno Mobbar DAMASAK MODU NGULORI 20 350 13.113130 12.519810
Borno Mobbar Z. UMORTI MOHAMMED WANZAM 21,22 1455 13.100170 12.504410
Borno Mobbar Z. UMORTI CHURCH AREA 2 23 700 13.099840 12.513670
Borno Mobbar Z. UMORTI GRAVE YARD AREA 24,25 1340 13.103930 12.514900
Borno Mobbar Z. UMORTI BABAN LAYI 26 915 13.099860 12.511040
66
Borno Mobbar Z. UMORTI ALHAJI ABBA TAR 27 990 13.101760 12.511120
Borno Mobbar Z. UMORTI BAGONI AMODU 28 1270 13.100780 12.512970
Borno Mobbar Z. UMORTI BA MASTAFA 29 830
Borno Mobbar Z. UMORTI MALARI 30 1160 13.070940 12.522750
Borno Mobbar Z. UMORTI MASA KALE 31 1015 13.061430 12.502870
Borno Mobbar Z. UMORTI BULAMA KURDI 32 665 13.058930 12.480500
Borno Mobbar Z. UMORTI ARWODI 33 435 13.007340 12.521430
Borno Mobbar Z. UMORTI GAFONI B MAINTA 34 490 13.044920 12.488530
Borno Mobbar Z. UMORTI KALUSARI GANA RC4 725 13.034430 12.458670
Borno Mobbar Z. UMORTI NGUDURI ALHAJI MELE 35 615
Borno Mobbar Z. UMORTI WALURI 36 448 12.995420 12.457750
Borno Mobbar Z. UMORTI MALARI KURA 37 340 13.070940 12.522750
Borno Mobbar Z. UMORTI MALUMTI GANA 38 215 13.008420 12.436100
Borno Mobbar Z. UMORTI MALLAMTI KURA RC5 310 13.039910 12.452110
Borno Mobbar Z. UMORTI NGUDORI 39 1000 12.985800 12.441170
Borno Mobbar Z. UMORTI BULABULIN SANUSI 40 510 13.098280 12.505490
Borno Mobbar Z. UMORTI G.H DAMASAK 41 200 13.096740 12.507590
Borno Mobbar Z. UMORTI ANGUWAN DOKI 42 1000 13.098960 12.508270
Borno Mobbar Z. UMORTI BULABULIN FULATARI 43 1000 13.098520 12.506090
Borno Mobbar Z. UMORTI ALHAJI MUNKAILA 44 1325 13.101400 12.506350
Borno Mobbar Z. UMORTI BULAMA MODU 45 1055 13.100780 12.512970
67
13.5 Training Schedule
Days Monday Tuesday Wednesday
Dates 2nd March 2020 3rd March 2020 4th March 2020
Timings
8.30 am - 9:00 am Session Registration & Introduction Recap of the previous Day Explain field procedures
Facilitator Narendra Narendra Narendra
9:00 am - 9:30 am Session Program overview Overview of sampling
Field Test
Facilitator Narendra Narendra
9:30 am - 10:00
am
Session SMART Overview Segmentation
Facilitator Narendra Narendra
10.00 am - 10.45
am
Session Survey teams Simple Random Sampling
Facilitator Narendra Narendra
10:45 to 11:00 am Tea Break Tea Break
11:00 to 12.00 n Session Anthropometry Systematic Sampling
Facilitator Narendra Narendra
12.00 to 1:00 pm Session Anthropometry Mortality
Facilitator Narendra Narendra
1:00 to 1:45 pm Lunch Break Lunch Break Lunch Break
1:45 to 2:30 pm Session Event Calendar IYCF and Maternal nut
Field Test
Facilitator Narendra Narendra
2:30 to 3:00 pm Session Team and Logistics ODK Questionnaire
Facilitator Narendra M&E Team
3.00 pm to 3.30
pm
Session Malnutrition ODK Questionnaire
Facilitator Narendra M&E Team
3:30 to 3:45 pm Tea Break Tea Break Tea Break
3:45 to 4:45 pm Session WASH and FSL questionnaire Questionnaire – Practice Discussion - Field Test
Facilitator Narendra Narendra Narendra
4:45 to 5:00 pm Discussion and Wrap up Discussion and Wrap up Finalization of teams
If we can conquer SPACE,
We can conquer CHILDHOOD HUNGER!!!!
-Buzz Aldrin
