TECHNICAL REPORT ON ASSESSING NUTRITION STATUS, MORBIDITY AND MORTALITY OF CHILDREN, MAIDUGURI METROPOLITIAN COUNCIL LGA, BORNO STATE, NIGERIA ...
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TECHNICAL REPORT ON
ASSESSING NUTRITION STATUS, MORBIDITY AND MORTALITY OF CHILDREN,
MAIDUGURI METROPOLITIAN COUNCIL LGA, BORNO STATE, NIGERIA
November 2019
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?
SMART survey will provide nutrition as well as mortality information for Maiduguri LGA in Borno state. In
terms of nutrition status, prevalence of Acute Malnutrition (GAM and SAM), Underweight (MUW, SUW) and
stunting (global and severe) shall be estimated along with indicators like IYCF, WASH, Food security,
morbidity and death rates (deaths/10000/day) will be provided to estimate U5MR and IMR in the survey
area.
Contents
List of Tables 5
List of Figures 5
List of Abbreviations 6
Acknowledgements 7
Executive summary 8
1. Introduction 10
1.1 Borno State 10
1.2 Maiduguri Metropolitan Council (MMC) 11
1.3 Humanitarian Assistance in Borno 12
1.4 Nutrition and Health Context 12
2. Objectives of the Survey 14
2.1 General Objectives 14
2.1.1 Specific Objectives 14
3. Methodology 15
3.1 Study Design 15
3.2 Target population 15
3.3 Sampling methodology 15
3.4 Sample Size Calculation 15
3.4.1 Sample size estimation of Acute Malnutrition 15
3.4.2 Sample size calculation for Mortality: 16
3.5 Final Sampling Strategy 17
3.6 Cluster Selection 17
3.7 Household Selection Techniques 17
3.8 Survey Teams 17
3.9 Survey equipment 18
3.10 Key Variables 18
3.11 Daily field procedure 18
3.12 Data collection and Supervision 19
3.13 Training of Enumerators 19
3.14 Data Collection Schedule 19
3.15 Data analysis and interpretation 19
3.16 Reserve Clusters: 20
3.17 Ethical clearance 20
3. 18 Operational Definitions: 20
4. Results of Child Nutrition 23
4.1 Survey Achievements 23
4.2 Anthropometric results (based on WHO standards 2006) 23
4.2.1 Prevalence of Acute Malnutrition by Weight for Height Z-score 24
4.2.2 Prevalence of Acute Malnutrition by Mid-Upper Arm Circumference 25
4.2.3 Comparison of Acute Malnutrition by WHZ and MUAC 26
4.3 Prevalence of Underweight 27
4.4 Prevalence of Chronic Malnutrition 28
4.5 Morbidity and Immunization: 294.5.1 Immunization: 29
4.5.2 Morbidity and treatment 30
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 Time spent in fetching water 34
8.3 Practice and methods for water purification 34
8.4 Handwashing practices 35
8.5 Defecation practices 35
9. Food Security and Livelihood 35
9.1 Food consumption score 35
9.2 Reduced coping strategy Index (rCSI) 36
10. Discussion and Conclusion 36
10.1 Limitations of the survey: 39
11. Recommendations 40
11.1 Short term recommendations (0 to 3 years): 40
11.1.1 Preventive strategies for awareness and BCC strategies 40
11.1.2 Treatment for Acute Malnutrition 40
11.1.3 Advocacy for management of acute malnutrition: 42
11.1.4 Research for simplified CMAM 42
11.2 Long term recommendations (3 to 6 years) 43
11.2.1 Awareness and capacity building 43
11.2.2 Management of Acute Malnutrition (Therapeutic and facility-based intervention) 44
11.2.3 Advocacy 44
12. Annexure 46
12.1 Annex 1: List of selected clusters 46
12.2 Annex 2: List of Indicators 47
10.3 Annex 3: Training Schedule 52
12.4 Annex 4: Questionnaire 53
12.4.1 Anthropometry Questionnaire 53
12.4.2 FCS and rCSI Questionnaire 54
12.4.3 WASH Questionnaire 55
10.4.4 IYCF Questionnaire 56
10.4.5 Demography and Mortality Questionnaire 59
12.5 Annex 4 Plausibility test 60
12.6 Annex 5: List of clusters with data collection schedule 61List of Tables Table 1 Sample size estimation of Acute Malnutrition ............................................................................... 15 Table 2 Sample size calculation for Mortality assessment in SMART survey on MMC LGA ....................... 16 Table 3 showing a list of reserve clusters for MMC SMART ........................................................................ 20 Table 4 showing survey targets and achievements in the SMART survey .................................................. 23 Table 5 Distribution of age and sex of children surveyed .......................................................................... 23 Table 6 Prevalence of acute malnutrition based on WFH z-scores and by gender ..................................... 24 Table 7 showing the Prevalence of acute malnutrition by age, based on WFH z-scores........................... 25 Table 8 Distribution of acute malnutrition and oedema based on WHZ .................................................... 25 Table 9 Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by gender ....... 25 Table 10 Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema ................... 26 Table 11 showing the prevalence of GAM and SAM. ................................................................................ 26 Table 12 Prevalence of underweight based on WFA z-scores by gender. .................................................. 27 Table 13 Prevalence of underweight by age, based on WFA z-scores ....................................................... 28 Table 14 Prevalence of stunting based on HFA z-scores and by gender .................................................... 28 Table 15 Prevalence of stunting by age based on HAZ .............................................................................. 29 Table 16 Mean z-scores, Design Effects and excluded subjects ................................................................. 29 Table 17 Death rates of Maiduguri MC LGA ............................................................................................. 31 Table 18 showing the prevalence of malnutrition among women with MUAC ......................................... 31 Table 19 showing prevalence of underweight as per BMI in MMC LGA .................................................... 32 Table 20 minimal meal frequency as per age groups in Maiduguri LGA................................................... 33 Table 21 showing grades of FCS and rCSI among host and IDPs in MMC LGA ........................................... 38 Table 22 comparison between FCS and rCSI among community of MMC LGA .......................................... 39 List of Figures Figure 1 showing map of Nigeria with Borno state .................................................................................... 10 Figure 2 showing Map of MMC including the selected clusters ................................................................. 11 Figure 3 showing the seasonal calendar of North Nigeria (Source: FEWS NET 2019) ................................ 14 Figure 4 showing data distribution as compare to WHO graph ................................................................. 24 Figure 5 showing overlap of MUAC and WHZ in MMC SMART survey ....................................................... 27 Figure 6 showing illness reported among children under 5 years in MMC LGA......................................... 30 Figure 7 showing sources of water used by the community of MMC LGA ................................................. 34 Figure 8 showing time needed for community to fetch a water in MMC LGA ........................................... 34 Figure 9 showing water purifcation methods used by MMC LGA community ........................................... 34 Figure 10 showing distribution of handwashing at different events in MMC LGA..................................... 35 Figure 11 showing food consumption score ratings in the community of MMC LGA ................................ 35 Figure 12 showing coping index of community of MMC LGA..................................................................... 36 Figure 13 showing prevalence of malnutrition among two types of communities. ................................... 37
List of Abbreviations 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 ENA Emergency Nutrition Assessment (Version: 7th July 2015) 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 - Emergency KM Kilometer LGA Local Government Area MAM Moderate Acute Malnutrition MUW Moderate Underweight MUAC Mid-Upper Arm Circumference NCA Nutrition Causal Analysis NSAG Non-Security 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 Practices SMART Standardized Monitoring and Assessment of Relief and Transitions TAG Technical Alliance Group TFP Therapeutic Feeding Practices U5MR Under-five (years) 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
Acknowledgements I would like to express my deepest gratitude to LGA and state authorities for providing their support to carry out SMART Survey successfully in Maiduguri LGA in Borno state of Nigeria. I extend my sincere gratitude to Dr. Fahad Zeeshan (Deputy Director, 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 OFDA, finace 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 Niegria) for their unwavering support during planning and implementation phase of the survey. I am also thankful of National Beurau of Statistics for loaning us a necessary equipments to conduct this survey and SPCHDA for their great cooperation throughout the porcess. I would thank my colleagues at Action Against Hunger, Nigeria team for their support. Mr. Jeremiah and Mr. Andrew helped us managing SMART logistics at base level. Also, we thank Mr. Solomon for providing technical support for overall management for this survey. I also thank two enthusiastic and energetic nutrition officers of AAH OFDA team, Mr. Peter Oyovwe and Mr. Brethren Igwe- Nworji for their support and efforts throughout this survey that made this survey successful. Also, I thank whole M&E team of AAH Nigeria for their support and cooperation. Finally and most importantly, I want to appreciate 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 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
Executive summary Background: The survey area was Maiduguri Metropolitan Council Local Government Area (LGA), Borno State of Nigeria. Maiduguri LGA is one of the largest LGA in Bonro with a population of 1,907,600. However, due to the ongoing conflict, population figures are highly fluid for the LGA as there is a continuous movement of people in and out of LGA. The result of the previous SMART survey conducted in MMC and Jere in 2016 indicated that the prevalence of Global Acute Malnutrition (GAM) was 19.2%, of which 3.1% were severely malnourished. Objectives: The main objective of the survey is to determine the nutritional status of children aged 6 to 59 months in MMC LGA. The survey also captured the nutritional status of the mothers having children age 0-59 months along with morality status of the community. Infant and Young Child Feeding (IYCF) practices (0-23 months), morbidity status (0-59 months), immunization coverage, food security situation and Water, Sanitation and Hygiene (WASH) conditions were also assessed in this survey. Methodology: The cross-sectional nutrition survey based on the SMART methodology was employed. The data collection was carried out from 11th – 17th November 2019. PPS (Probability Proportionate to Size) method was used to identify clusters from the sampling frame i.e. the list of wards. A total of 35 clusters and 617 households (HHs) were selected for this survey as a sample for the nutrition component of the survey. Data was collected using Smartphones (Samsung Tablets) and paper based questionnaires in the field. This data was collected using the ODK interface and uploaded on the Kobo Server. Data were mainly analyzed using Emergency Nutrition Assessment (ENA) software for SMART (July 2015 version) and epi info application (version 3.5.4). Results: A total of 605 children aged 6-59 months were participated for anthropometry survey. The overall prevalence of Global Acute Malnutrition (GAM) and Severe Acute Malnutrition (SAM) based on weight-for-height z-score was 17.1 %( 13.8%–20.9% 95% C.I.) and 3% (1.9%-4.7%: 95% C.I.) respectively. The prevalence of GAM and SAM with MUAC was 6.6% (5.0%-8.7% 95% C.I) and 1.0% (0.4%-2.5%: 95% C.I) respectively. Also, no case of bilateral pitting edema was identified in the survey. The prevalence of underweight for MMC LGA is 26.6% (22.9%-30.6% 95% CI) and severe underweight is 7.1% (4.9%-10.3% 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 MMC LGA affecting a large proportion of children. Prevalence of stunting was 31.7%( 27.4% -36.3%, 95% C.I.), and 12.0% (9.4% - 15.1%; 95% CI) children were affected by severe stunting. The prevalence of severe and moderate malnutrition among mothers was 2.5% and 8.1% respectively among mothers with MUAC. Whereas the prevalence of malnutrition with MUAC among non-mothers is 1.6% and 11.4% for severe and moderate malnutrition respectively. In terms of morbidity , about 17.6%, 32.6%, and 60.4% of children have suffered from diarrhea, cough, and fever respectively in the past two weeks of data collection. Among the children who suffered illness, 38.2% caretakers preferred Pharmacy store as a choice for the treatment for their children. Among the children from 9-59 months age group, 74.8% received the measles (with or without card) and 72.8% received vitamin A supplementation. Also, deworming tablet was received by as low as 28.5% of the sampled children. Among the young infants, 66% was put on the breast of the mother within the first hour of birth and 76.9% of infants received colostrum. The data also shows that 84.6% (54.6% - 98.1%) children under 6 months were exclusively breastfed. In terms of complementary feeding, 87.5% children received food at the 6-8 months age. At the age of 6-23 months, 10.3% children received meals as per minimum meal
frequency, whereas among 6-23 months 2.6% and 4.3% received the minimum adequate diets among non breastfed and breastfed women respectively. Borehole is most preferred source of water as 85.7% respondants uses the source. Also 57.2% respondents needed less than 30 minutes to fill the water required. On an average, 114 litres of water used by per family which is lesser than the WHO standards. Almost three fourth of respondents (78.6%) doesn`t do any treatment for drinking water. Also, 73% uses soap with water in this community where majority of the families washes hands after defecation and eating food i.e. 98.8% and 96.8%. Majority of the respondents used the latrine (68.4%) for defecation. About 62.2% population consumed acceptable food since past seven days survey whereas 14.3% had poor food consumption scores. The data also shows that about 58.8% has instrumented high coping strategy to survive the food scarcity whereas 23.3% used low coping strategy. Conclusion: This survey falls under the “good” range as per the Plausibility test of SMART survey classification as the data received 11% penalty points. The GAM prevalence of MMC LGA is 17.1%, indicating the survey population is under ‘critical’ condition based on the WHO classification of emergency. Also, the retrospective mortality rates for both overall and under 5 population are well below the alarming category as per the CDC standards. However, the early initiation of breastfeeding shows a promising picture, but still, only one in ten children receive minimal food frequency, whereas less than 5% children received minimal acceptable diets for their age. The data shows that majority of the population has consumed acceptable food however still about half of the survey population also used high coping strategy to survive the food scarcity. This paradox needs to be understand further in order to analyze the situation better. Also, in terms of source of water boreholes tops the chart however no use of purification techniques also raise the concern since the water may have been contaminated with e-coli during wet season. This phenomena may have also caused 17.6% children suffer from diarrhea in past 15 days prior to data collection. Though the prevalence of SAM is about 3% as per WHZ, but data also shows that host communities has prevalence as high as 4.02% as compare to IDPs that has 1.11%. This comparison, highlights a disparity of interventions between IDPs and host communities in this LGA. Therefore need to strategies program accordingly. Recommendations: Considering the prevalence of acute malnutrition (GAM and SAM) both are critical as per the WHO thresholds, there is a need of holistic approach to mitigate the challenge of malnutrition.The short term strategies could include extensive behavior change communication to enhance MYCN practices. Along with the awareness and management of SAM, an advocacy strategy to engage all stakeholders shall be implemented using a Technical workshop in the LGA and consortium to invite more funding resources to this state in order serve the vulnerables. Innovations validated through research to simplify the CMAM strategies along with conducting the studies that will dig deep into the dynamics of malnutrition using methodologies like Nutrition Causal Analysis and Anthropological studies etc. Inclusion of nationwide Health System Strengthening (HSS) strategies will help to move towards sustainable growth of the program and will help to improve the program as well as infrastructure to take care of future needs. Also, considering the disparity of prevalence of malnutrition (acute) among host and IDPs, there is an urgent to conduct proper comparison group study to understand the impact of malnutrition in both types of setting followed by appropriate division of interventions to mitigate this inequality.
1. Introduction
Adequate nutrition during the early childhood period i.e. first 1000 days of life is essential to ensure the
growth, health, and development of children to their full potential1. World Health Organization (WHO)
acknowledges that malnutrition is a serious problem that can be associated with a substantial increase in
the risk of mortality and morbidity2. The World Bank states that malnutrition slows economic growth and
perpetuates poverty3, where mortality and morbidity associated with malnutrition represent a direct loss
in human capital and productivity for the economy.
Due to its large population across the continent, Nigeria Republic is also known as a “Giant of Africa”. This
West African country shares the border with Chad, Cameroon, Niger, and Benin. Nigeria has 36 states and
one Federal Capital Territory (FCT) which is National capital ‘Abuja’. The population of the country is about
200 million as per the 2019 estimates4 becoming a ninth most populous country in the world.
Action Against Hunger (AAH) is actively working in Nigeria since 2010. From 2014 to 2015, it doubled the
volume of its operations in response to the crisis, meeting 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 situated in the North-eastern
part of the country. The capital of the state is
Maiduguri. This state has 27 local Government
Borno
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 State
Armed Groups (NSAG) has disrupted life in this
state and majority of the population greatly
affected. Government and Humanitarian
agencies including INGOs and UN have tried their
best to minimize the catastrophe of the crisis
however so far they have failed to resolve the Figure 1 showing map of Nigeria with Borno state
issues faced by community.
According to the humanitarian response plan of 2019, the total population of Borno state is 3.9 million of
which 1 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
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
5
Humanitarian Response Strategy of Nigeria from January 2019 to December 20212019, 7.1 million people (2.3 million girls, 1.9 million boys, 1.6 million women and 1.3 million men) are in
need of humanitarian assistance in north-east Nigeria as a result of a crisis that is now in its 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 was 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 food scarcity has diminished completely.
1.2 Maiduguri Metropolitan Council (MMC)
Maiduguri is the capital and largest city of the Borno state of Nigeria. This city is established closer to
Ngadda River which vanishes into Firki swamps in the areas
around lake chad6. This city founded during the British era in
1907 and has grown rapidly since then. The population of
Maiduguri MC estimated to be 1,907,600 as of 20077. The
residents of the city majorly follow Muslim religion and major
tribes includes Kanuri, Hausa, Shuwa, Bura, Marghi and
Fulani ethnic groups. The overall population is mixed with
settlements in IDPs and host communities. Also, there are
host community villages that also have a settlement of IDP
camp within the hamlets and on average more than two
households are residing in one house compound to
accommodate the relatives from the displaced areas. In
general, the raining season begins in April month of the year
and dry season begins from October month.
On 14 May 2013, President Goodluck Jonathan declared a
state of emergency in Northeast Nigeria, including Borno
State, due to the militant activity of NSAGs8. The entire city
was under overnight curfew, and trucks have been Figure 2 showing Map of MMC including the selected
prevented from entering the city. Twelve areas of the city clusters
that are known to be strongholds of NSAGs are under
permanent curfew9. In January 2015, there was a bomb attack on a famous Monday market resulted in
demise of 19 people. Also there was another major attack on 7th March 2015, five blasts by a suicide
bombing which left 54 dead and 143 wounded.
6
"Encyclopædia Britannica". Retrieved 6 April 2007
7
"The World Gazetteer". Archived from the original on 30 September 2007. Retrieved 6 April 2007 .
8
"Nigeria: State of Emergency Declared". New York Times. 14 May 2013. Retrieved 6 September 2019
9
"Nigeria army's offensive to continue 'as long as it takes'". BBC News. 18 May 2013. Retrieved 6 September 2019.1.3 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 operations in 2016 following the Borno state government’s declaration of
a nutrition emergency. Working closely with partners, Action Against Hunger provided 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 MMC LGA in order to minimize the gaps. The IDP camps are managed mainly by
NEMA/ SEMA, IOM and other UN agencies with imminent support from the partners to provide the best
possible care in different sectors like Nutrition, Health, WASH and Food, Security and Livelihoods (FSL).
These partner organizations are actively supporting in areas like Bakassi camp, Bayan Texaco, Fatima Kurti,
Shuwari Dannari, DCC Shuwari camp, Zajeri, Malumeri, Bustop, Budumeri, Gidan Gona, Karo lenge, CAN
center camp and Mustapha Mallambe, etc. for Nutrition, Health, and FSL, etc. UNICEF co-leads the
nutrition, WASH, child protection and education sections in line with the 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 a 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.
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%)12. 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%)13. 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.
10
Humanitarian Action Plan: UNICEF 2019-2021, UNICEF
11
https://www.msf.org/crisis-info-borno-and-yobe-states-august-2019
12
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
13
ibid 10AAH has conducted a SMART survey in 2016 in MMC and Jere LGAs of Borno state. The data suggests that the prevalence of GAM was 19.2% (14.7% -24.6%; 95% CI) and SAM was 3.1% (1.6% -6.0%) when assessed for weight for height. Whereas with MUAC, prevalence of GAM was 5.9% (3.9% - 8.8%; 95% CI) and SAM was 1.3% (0.5% - 2.9%; 95% CI). The prevalence of underweight was 28.1% (22.8% - 34.1%) and severe underweight was 8.7% (6.4% - 11.7%; 95% CI) and prevalence of stunting was 30.7% (25.8% -36.2%) and severe stunting was 11.4% (8.7% - 14.8%)14. The recent NFSS survey also suggests that prevalence of GAM was 12.5% (8.9% - 17.4%) and SAM was 1.4% (0.6% - 3.5%) as per WHZ and 0.8% (0.3% -2.0%) and 0.6% (0.2% - 1.8%) as per MUAC in MMC and Jere LGAs combined. In terms of Chronic malnutrition, prevalence of stunting was 17.8% (13.5% - 23.2%) and severe stunting was 4.5% (2.5% - 8.1%) whereas prevalence of underweight was 16.6% (12.9% - 21.2%) and severe underweight was 1.8% (1.0% - 3.2%) in above mentioned two LGAs15. In Borno state, 66.7% of children received any vaccines whereas 54.6% of children received measles vaccine and 44.5% received all three dosages of pentavalent vaccines. In this state, only 36.3% and 45.2% children received ORS and Zinc respectively who suffered from diarrhea in the past two weeks of the data collection16. The data from NNHS revealed that 20.7% under five children in Borno state suffered from fever in the past two weeks of data collection, among them 60.6% received any antimalarial treatment whereas 18.2% have been tested for malaria (RDT). In terms of prevalence of acute malnutrition among women, prevalence of GAM was 12.6% (9.8% - 15.5%; 95% CI) which almost double than the national average i.e. 6.9% (6.5% - 7.4%; 95%CI) whereas for SAM among women, the prevalence was 7.2% (5.4% - 9.1%; 95% CI)17. For MMC and Jere, 6.9% surveyed women were acute malnourished (MUAC
Figure 3 showing the seasonal calendar of North Nigeria (Source: FEWS NET 2019)
In conclusion, three 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 MMC and Jere 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. Therefore, to reduce the knowledge gap and to understand the data of MMC
LGA only, it was necessary to conduct the SMART survey in this LGA.
2. Objectives of the Survey
2.1 General Objectives
To assess the nutritional situation among 6-59 months old and mortality among the general population
and children 6-59 months in Maiduguri Metropolitan Council (MMC) LGA, Borno State, Nigeria.
2.1.1 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 determine the coverage of measles vaccinations, and vitamin A supplementation in the last
six months 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 estimate the retrospective crude mortality rates and under five mortality rates in a specific
time period (94 days)
● To assess the maternal malnutrition status among the mothers of under five children surveyed.
● To determine morbidity attributed to fever and cough, use of mosquito net and Infant and young
child feeding practices (IYCF) key indicators in the study area among 0-23 months children.
● To determine water, sanitation and hygiene practices among the study population in the study
area.
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● To assess the current food consumption score and coping strategy situation of the surveyed
population.
● To provide recommendations based on the findings of the survey for planning, advocacy, decision
making and monitoring.
3. Methodology
3.1 Study Design
The survey used a cross-sectional quantitative study design using a two-stage cluster sampling methods
based on Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology.
3.2 Target population
The target population for the anthropometric survey was 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 9-59 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 targeted.
3.3 Sampling methodology
Due to the big size of the population of interest, the Nutrition Survey used a two-stage random sampling
method. In the first stage, the sample of clusters (villages) was drawn from the official list of Vaccination
Tracking System (VTS) by UNICEF. 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 implemented using simple or systematic random selection of households
depending on the availability of information within the cluster. In general, the team leader was
responsible to create a complete and updated list of all households in the Cluster (here villages) and then
a random number table was used to randomly select the households to be included in the survey.
3.4 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.
3.4.1 Sample size estimation of Acute Malnutrition
Table 1 Sample size estimation of Acute Malnutrition
Parameters Value Assumptions
Estimated Prevalence of GAM (%) 25% As per the previous NFSS conducted in MMC and Jere, GAM
prevalence was 12.5% (8.9%-17.4%). But since the survey was
conducted in May 2019, the estimated prevalence was
considered as high as 25% as the survey is scheduled in the
lean period as opposed to the NFSS survey period.
± Desired precision 5% Since the GAM prevalence is higher and the data is not
available, a precision of ± 5% was chosen as per the guidelines
of SMART.
Design Effect 1.7 The standard DEFF is 1.5 is considered as per the thumb ruleof SMART Methodology.
Children to be included for 533
Based on the formula above done in ENA
Anthropometric measurements
The SMART Methodology recommends converting the number of children into a number of households
(fixed household method) for numerous reasons:
1. It is easier to create lists of households than lists of children in the field.
2. The sample size calculated in the 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 the number of children to sample into the number of households, the
following assumptions considered.
Parameters Value Assumptions
Average HH Size 5.5 As per the NNHS 2018 survey, Household size for Borno was
5.5
% Children under-5 18% Based on the SMART survey report, 17.6% children are under
5 among population. This rounded up to 18%
% Non-response Households 3% The percentage of non-response chosen was relatively low
because the target population living in the close community
and does not migrate for a longer period.
Households to be included for 617 households
Anthropometric measurements
(according to ENA)
3.4.2 Sample size calculation for Mortality:
Table 2 Sample size calculation for Mortality assessment in SMART survey on MMC LGA
Parameters Value Assumptions
Estimated Death Rate /10,000/day 1.03 As per the previous SMART survey conducted in 2016 in
MMC and Jere
± Desired precision ± 0.5 For Mortality indicator, precision will be chosen ± 0.5 as
this is a standard precision for the mortality of >1/ 10000/
day
Design Effect 1.7 Since the data of the previous study is not available and
study will be having cluster sampling methodology hence
the standard Design effect is considered i.e. 1.5
*Recall period 94 From the 12th August to 14th November 2019 (middle of
the data collection).
Average HH Size 5.5 As per the NNHS 2018 survey, the HH size is 5.5
Non-response rate 3% The percentage of non-response chosen was relatively low
because the target population living in the close
community and does not migrate for a longer period.
Sample to be included for Mortality 3116 Based on the calculations by ENA
HH to cover for mortality 5843.5 Final Sampling Strategy The sample size for anthropometry was 533 children and 617 households, whereas for mortality, it was 3116 individuals and 584 households. Since the anthropometry sample is higher, all indicators were captured from the 617 HHs from 35 clusters (Please see section 2.11 for cluster calculation). 3.6 Cluster Selection Using the ENA software, 35 Clusters were drawn from the sampling frame of the sites of MMC. Clusters were selected using the PPS (Probability Proportional to size) method. Random sampling methods was used to select clusters. Please see the annex 1 for the list of the selected clusters. 3.7 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. Majority of settlements/camps, which have geographically large area thus segmentation methods was introduced. For the selection of second-stage sampling method, team leaders were equipped with the necessary information that was explained during training sessions. For the selection of households, based on a number of households in each cluster following method was used. Segmentation method: If the cluster had more than 250 households then the cluster were segmented using geographic landmarks either man made (like schools, mosques, churches, special buildings etc. ) or natural (river, mountains, farms, etc.). The PPS was used for selection of clusters from the segments. Once the segment was selected, the enumeration of the households were conducted. In this survey, Simple Random Sampling method was majorly used for selection of Households (18 HH in each cluster). The sampling interval (k) was determined by dividing the total number of households in the zone by the 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 will be 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 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. Systematic Random Sampling: If the cluster had a household between 150 to 250 then a systematic random sampling method was used. Based on the enumerations, the total HH no. were captured and k was calculated. The formula for k is = Total no of HH in the cluster/ No of HH required from each cluster i.e. 18 HH 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 lottery method households were selected for data collection. 3.8 Survey Teams Six teams were engaged in data collection and each team had four members. Two team members (one female and one male) were responsible for measuring children and their mothers and for the data
recording. The other two team members were responsible for rapport building at the village level and
administration of questionnaires (IYCF, WASH, and HFS, etc.) in the local dialect i.e. Hausa and Kanuri.
The two supervisors and SMART survey managers were monitoring the data collection including
representatives from M&E team AAH, NBS, HMIS and SPCHDA on rotation basis. The survey manager and
a supervisor were responsible for daily data entry and double data entry) into ENA software, to ensure a
high level of data quality collected by the teams.
3.9 Survey equipment
Anthropometric measurements were taken on children 0-59 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 was measured on the left arm of children using a child
MUAC tape.
For maternal anthropometry, a wooden stadiometer and electronic weighing scales were used, while for
MUAC, adult MUAC tapes were used.
Weight-for-height z-score was then determined by using the WHO Weight-for-Height tables for both
genders. Age was reviewed through their immunization cards or birth proofs. 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.
3.10 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, other information
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 collected.
3.11 Daily field procedure
The number of households to be completed per day 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 8.00 am and back at 5.00 pm = 540 minutes
2. Two way travel time to reach a cluster: 60 min.
3. Duration for introduction and selection of households: 45 min.
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 30 mins.
7. Revisit time period: 15 min was kept for revisiting the household if the house members were
absent at the time actual data collection
This estimation of 6 hours and 00 minutes on the field and 20 minutes per household has led to the
conclusion of having 18 HH per day per team (360 minutes/20 minutes = 18 HH).The 617 households in the sample were 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. 617 HHs/ 18 households per day in each cluster = 34.28 ~ 35 Clusters Since this survey will be done with 6 survey teams, therefore to cover 35 clusters with six teams required 35/6 = 5.8 ~ 6 days. 3.12 Data collection and Supervision A mobile tablet 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 the central server using internet connection. Once, the data were received, the daily quality checks conducted and shared with the manager. Along with digital data collection, considering the previous experiences paper-based data collection was conducted to ensure the backup of the data. The supervisor was overall in charge of a group. A group consists of two teams that cover on average 12 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 helps the team in locating clusters. Additionally, he/she was also responsible for checking the quality of the interview by observing the interview and anthropometric measurements. 3.13 Training of Enumerators A four days dedicated for training and the additional day was for piloting the survey. The first day was dedicated to theoretical training, second day for explaining the questionnaires and in house practice on weight and height measurement, third day was for sampling and field strategies and the fourth day was for standardization and fifth day was reserved for pilot testing. The standardization test conducted to evaluate enumerators based on their measurement performance. The participants have measured about 10 children twice each and the outcome of the same helped in determining the team structure. One day pilot was conducted to understand the quality of the questionnaire and understanding of enumerators regarding the enumerations, sampling and data collection skills. 3.14 Data Collection Schedule The data collection was conducted from 11th to 16th of November 2019. Based on the location of clusters, the schedule of data collection was prepared. The data collection schedule was twisted a bit as the data collection of Bakasi camp was switched with cluster of Modugnari tudu. 3.15 Data analysis and interpretation The primary analysis was approximately 2 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. The nutrition results was 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. For estimation of the malnutrition prevalence, WHO 2006 growth references. 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).
3.16 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 are achieved during the survey
3. If less than 90% of children are measured than desired during the survey
In this survey, all reserved clusters were used since the survey cannot be able to reach 90% of the expected
sample size for the children. Therefore, two extra days were included in the survey schedule to collect
data for the SMART survey. However, since the target was achieved therefore no reserved clusters
required to use for data collection in this survey. The details of the reserved clusters are mentioned in the
table below.
Table 3 showing a list of reserve clusters for MMC SMART
Ward Settlement Population Reserve cluster
Bolori II Bolori Gana 99578 RC1
Gamboru Gamboru 25391 RC2
Maisandari Camp - BAKASI CAMP 20433 RC3
Maisandari Kasula 90909 RC4
3.17 Ethical clearance
It was 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 was remain
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. In case of identification of SAM children, the children will be referred to nearest health facilities and
also provide necessary guidance to the caretakers.
3. 18 Operational Definitions:
Abandoned HH: Those houses were considered abandoned which were not occupied for a long period of time. Such
households shall be eliminated from sampling frame at the time of enumerating the cluster.
Absent Household: The member of the household recently inhabited but currently empty due to some reason will
be considered as absent HH. The revisits were made to such HH before leaving the village.
Bilateral Pitting Oedema: Only bilateral pitting edema is considered as nutritional edema. Their presence was
detected by applying gentle pressure with the thumbs to top part of both feet for ten seconds. If the imprint of the
thumbs remained on both feet for ten seconds after releasing the thumbs, the child was considered to have
nutritional edema. Bilateral edema was diagnosed and not graded.
Complementary feeding: It is defined as the process starting in addition to breast milk, foods that are readily
consumed and digested by the young child and that provide additional nutrition to meet all the growing child's needswhen breast milk alone is no longer sufficient to meet the nutritional requirements of infants. Although exclusive
breastfeeding provides the best start, after six months and as long as breastfeeding continues, the child needs more
vitamins, minerals, proteins, and carbohydrates than are generally available from breast milk alone. Any non-breast
milk food or nutritive liquids that are given to young children during this period are defined as complementary foods,
and complementary feeding is the process of introducing these foods.
Diarrhea: The passage of three or more loose or liquid stools per day (or more frequent passage than is normal for
the individual).
Early initiation of Breastfeeding: A child is breastfed within the first hour of delivery.
Exclusive Breastfeeding: A child has no other food or liquid, not even water, except breast milk (including milk
expressed or by wet nurse) for the first six months of life. According to WHO, if the child receives ORS, multivitamin
syrups or any other medicines this will still get counted as Exclusive breastfeeding.
Global Acute Malnutrition (GAM): All the children with weight for height z-score are less than -2SD or MUAC less
than 125 mm with the presence or absence of bilateral pitting edema.
Height: Children above 24 months of age or taller than 87 cms were measured standing on the measuring board.
The children's height/length was measured with a precision of 0.1 cm by height boards. Children were measured
with no shoes or braids, hairpieces on their heads that could interfere with a correct height measurement. In case
the birth-date was unknown or it was not possible to estimate the age of the child using the event calendar, length
guided the measurement process.
Length: Children between 6-24 months of age or shorter than 87 cms were measured lying down.
Maternal malnutrition: A pregnant or non-pregnant woman who has less than 23 cm MUAC measurement and if it
is lesser than 19 cm then mother is severely malnourished.
Minimum dietary diversity in complementary feeding is equally important to capture as it is a proxy indicator for
adequate micronutrient-density of foods. It indicates the proportion of children 6–23 months of age who receive
foods from 4 or more food groups during the previous day. The 7 foods groups used for calculation of this indicator
are: — grains, roots and tubers, legumes and nuts, dairy products (milk, yogurt, cheese), flesh foods (meat, fish,
poultry and liver/organ meats), eggs, vitamin-A rich fruits and vegetables, and other fruits and vegetables21.
The age group included for estimation of initiation of complementary feeding was 6-8 months and for frequency and
diversity of complementary feeding was 6-23 months.
Minimum Meal Frequency for breastfed children is defined as 2 times for infants 6–8 months and 3 times for
children 9–23 months and for non-breastfed children, the minimum is defined as 4 times for children 6–23 months.22
Moderate Acute Malnutrition (MAM): A child is considered MAM if he/she meets any one of these criteria or both:
weight for height z-score is between -3SD and -2SD or
when MUAC is between 115mm - 124 mm
MUAC: It stands for Mid Upper Arm Circumference. The special tri-colored tape is used to measure the presence of
acute malnutrition a.k.a. wasting among children under 5 years of age. The left hand is used to take measurements.
The mid-point is calculated from the tip of the shoulder to the tip of the elbow (olecranon process and the acromion).
21 Indicators for assessing infant and young child feeding practices Part-3Country profiles
http://www.unicef.org/nutrition/files/IYCF_Indicators_part_III_country_profiles.pdf
22Indicators for assessing infant and young child feeding practices Part-3Country profiles
http://www.unicef.org/nutrition/files/IYCF_Indicators_part_III_country_profiles.pdfYou can also read
